The last few years have certainly highlighted the need for businesses of all kinds to build up their operational resilience. With a rising tide of pandemic waves, high-level cybersecurity incidents, frequent technology failures, and a host of natural disasters — there’s been plenty to protect against.

As businesses become more digital and dependent upon end-to-end ecosystems of connected services, the responsibility for protecting critical business processes has clearly shifted. It’s no longer just a task for IT and security managers but has become top-of-mind for line-of-business owners, too.

Stay with us now as BriefingsDirect explores new ways that those responsible for business processes specifically in the financial sector are successfully leading the path to avoiding and mitigating the impact and damage from these myriad threats.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

To learn more about the latest in rapidly beefing-up operational resilience by bellwether finance companies, BriefingsDirect welcomes Steve Yon, Executive Director of the EY ServiceNow Practice, and Sean Culbert, Financial Services Principal at EY. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Sean, how have the risks modern digital businesses face changed over the past decade? Why are financial firms at the vanguard of identifying and heading off these pervasive risks?

Culbert: The category of financial firms forms a broad scope of types. The risks for a consumer bank, for example, are going to be different than the risks for an investment bank or from a broker-dealer. But they all have some common threads. Those include the expectation to be always-on, at the edge, and able to get to your data in a reliable and secure way.

There’s also the need for integration across the ecosystem. Unlike product sets before, such as in retail brokerage or insurance, customers expect to be brought together in one cohesive services view. That includes more integration points and more application types.

This all needs to be on the edge and always-on, even as it includes, increasingly, reliance on third-party providers. They need to walk in step with the financial institutions in a way that they can ensure reliability. In certain cases, there’s a learning curve involved, and we’re still coming up that curve.

It remains a shifting set of expectations to the edge. It’s different by category, but the themes of integrated product lines — and being able to move across those product lines and integrate with third-parties – has certainly created complexity.

Gardner: Steve, when you’re a bank or a financial institution that finds itself in the headlines for bad things, that is immediately damaging for your reputation and your brands. How are banks and other financial organizations trying to be rapid in their response in order to keep out of the headlines?

Interconnected, system-wide security

Yon: It’s not just about having the wrong headline on the front cover of American Banker. As Sean said, the taxonomy of all these services is becoming interrelated. The suppliers tend to leverage the same services.

Products and services tend to cross different firms. The complexity of the financial institution space right now is high. If something starts to falter — because everything is interconnected — it could have a systemic effect, which is what we saw several years ago that brought about Dodd-Frank regulations.

So having a good understanding of how to measure and get telemetry on that complex makeup is important, especially in financial institutions. It’s about trust. You need to have confidence in where your money is and how things are going. There’s a certain expectation that must happen. You must deal with that despite mounting complexity. The notion of resiliency is critical to a brand promise — or customers are going to leave.

One, you should contain your own issues. But the Fed is going to worry about it if it becomes broad because of the nature of how these firms are tied together. It’s increasingly important — not only from a brand perspective of maintaining trust and confidence with your clients — but also from a systemic nature; of what it could do to the economy if you don’t have good reads on what’s going on with support of your critical business services.

Gardner: Sean, the words operational resilience come with a regulatory overtone. But how do you define it?

The operational resilience pyramid

Culbert: We begin with the notion of a service. Resilience is measured, monitored, and managed around the availability, scalability, reliability, and security of that service. Understanding what the service is from an end-to-end perspective, how it enters and exits the institution, is the center to our universe.

Around that we have inbound threats to operational resilience. From the threat side, you want the capability to withstand a robust set of inbound threats. And for us, one of the important things that has changed in the last 10 years is the sophistication and complexity of the threats. And the prevalence of them, quite frankly.

We have COVID, we have proliferation of very sophisticated cyber attacks that weren’t around 10 years ago. Geopolitically, we’re all aware of tensions, and weather events have become more prevalent. It’s a wide scope of inbound threats.

If you look at the four major threat categories we work with — weather, cyber, geopolitical, and pandemics — pick any one of those and there has been a significant change in those categories. We have COVID, we have proliferation of very sophisticated cyber attacks that weren’t around 10 years ago, often due to leaks from government institutions. Geopolitically, we’re all aware of tensions, and weather events have become more prevalent. It’s a wide scope of inbound threats.

And on the outbound side, businesses need the capability to not only report on those things, but to make decisions about how to prevent them. There’s a hierarchy in operational resilience. Can you remediate it? Can you fix it? Then, once it’s been detected, how can minimize the damage. At the top of the pyramid, can you prevent it before it hits?

So, there’s been a broad scope of threats against a broader scope of service assets that need to be managed with remediation. That was the heritage, but now it’s more about detection and prevention.

Gardner: And to be proactive and preventative, operational resilience must be inclusive across the organization. It’s not just one group of people in a back office somewhere. The responsibility has shifted to more people — and with a different level of ownership.

What’s changed over the past decade in terms of who’s responsible and how you foster a culture of operational resiliency?

Bearing responsibility for services

Culbert: The anchor point is the service. And services are processes: It’s technology, facilities, third parties, and people. The hard-working people in each one of those silos all have their own view of the world — but the services are owned by the business. What we’ve seen in recognition of that is that the responsibility for sustaining those services falls with the first line of business [the line of business interacting with consumers and vendors at the transaction level].

Yon: There are a couple of ways to look at it. One, as Sean was talking about, the lines of defense and the evolution of risk has been divvied up. The responsibilities have had line-of-sight ownership over certain sets of accountabilities. But you also have triangulation from others needing to inspect and audit those things as well.

The time is right for the new type of solution that we’re talking about now. One, because the nature of the world has gotten more complex. Two, the technology has caught up with those requirements.

The move within the tech stack has been to become more utility-based, service-oriented, and objectified. The capability to get signals on how everything is operating, and its status within that universe of tech, has become a lot easier. And with the technology now being able to integrate across platforms and operate at the service level — versus at the component level – it provides a view that would have been very hard to synthesize just a few years ago.

What we’re seeing is a big shot in the arm to the power of what a typical risk resilience compliance team can be exposed to. They can manage their responsibilities at a much greater level.

Before they would have had to develop business continuity strategies and plans to know what to do in the event of a fault or a disruption. And when those things come out, the three-ring binders, the war room gets assembled and people start to figure out what to do. They start running the playbook.

What we’re seeing is a big shot in the arm to the power of what a typical risk resilience compliance team can be exposed to. They can manage their responsibilities at a mch greater level.

The problem with that is that while they’re running the playbook, the fault has occurred, the destruction has happened, and the clock is ticking for all those impacts. The second-order consequences of the problem are starting to amass with respect to value destruction, brand reputational destruction, as well as whatever customer impacts there might be.

But now, because of technology and moving toward Internet of things (IoT) thinking across assets, people, facilities, and third-party services, technology can self-declare their state. That data can be synthesized to say, “Okay, I can start to pick up a signal that’s telling me that a fault is inbound.” Or something looks like it’s falling out of the control thresholds that they have.

That tech now gives me the capability to get out in front of something. That would be almost unheard-of years ago. The nexus of tech, need, and complexity are all hitting right now. That means we’re moving and pivoting to a new type of solution rising out of the field.

Gardner: You know, many times we’ve seen such trends happen first in finance and then percolate out to the rest of the economy. What’s happened recently with banking supervision, regulations, and principles of operational resilience? 

Financial sector leads the way

Yon: There are similar forms of pressure coming from all regulatory-intense industries. Finance is a key one, but there’s also power, utilities, oil, and gas. The trend is happening primarily first in regulatory-intensive industries.

Culbert: A couple years ago, the Bank of England and the Prudential Regulation Authority (PRA) put out a consultation paper that was probably most prescriptive out of the UK. We have the equivalent over here in the US around expectations for operational resiliency. And that just made its way into policy or law. For the most part, on a principles basis, we all share a common philosophy in terms of what’s prudent.

A lot of the major institutions, the ones we deal with, have looked at those major tenets in these policies and have said they will be practiced. And there are four fundamental areas that the institutions must focus on.

One is, can it declare and describe its critical business services? Does it have threshold parameters logic assigned to those services so that it knows how far it can go before it sustains damage across several different categories? Are the assets that support those services known and mapped? Are they in a place where we can point to them and point to the health of them? If there’s an incident, can they collaborate around the sustaining of those assets?

As I said earlier, those assets generally fall into small categories: people, facilities, third parties, and technology. And, finally, do you have the tools in place to keep those services within those tolerance parameters and have other alerting systems to let you know which of the assets may well be failing you, if the services are at risk.

That’s a lay-person, high-level description of the Bank of England policy on operational risks for today’s Financial Management Information Systems (FMIS). Thematically most of the institutions are focusing on those four areas, along with having credible and actionable testing schemes to simulate disruptions on the inbound side. 

In the US, Dodd-Frank mandated that institutions declare which of those services could disrupt critical operations and, if those operations were disrupted, could they in turn disrupt the general economy. The operational resilience rules and regulations fall back on that. So, now that you know what they are, can you risk-rate them based on the priorities of the bank and its counterparties? Can you manage them correctly? That’s the letter-of-the-law-type regulation here. In Japan, it’s more credential-based regulation like the Bank of England. It all falls into those common categories.

Gardner: Now that we understand the stakes and imperatives, we also know that the speed of business has only increased. So has the speed of expectations for end consumers. The need to cut time to discovery of the problems and to find root causes also must be as fast as possible.

How should banks and other financial institutions get out in front of this? How do we help organizations move faster to their adoption, transform digitally, and be more resilient to head off problems fast? 

Preventative focus increases

Yon: Once there’s clarity around the shift in the goals, knowing it’s not good enough to just be able to know what to do in the event of a fault or a potential disruption, the expectation becomes the proof to regulatory bodies and to your clients that they should trust you. You must prove that you can withstand and absorb that potential disruption without impact to anybody else downstream. Once people get their head around the nature of the expectation-shifting to being a lot more
preventative versus reactive, the speeds and feeds by which they’re managing those things become a lot easier to deal with.

You’d get the phone call at 3 a.m. that a critical business service was down. You’d have the tech phone call that people are trying to figure out what happened. That lack of speed killed because you had to figure a lot of things out while the clock was ticking. But now, you’re allowing yourself time to figure things out.

Back when I was running the technology at a super-regional bank, you’d get the phone call at 3 a.m. that a critical business service was down. You’d have the tech phone call that people are trying to figure out what happened because they started to notice at the help desk that a number of clients and customers were complaining. The clock had been ticking before 3 a.m. when I got the call. And so, by now, by that time, those clients are upset.

Yet we were spending our time trying to figure out what happened and where. What’s the overall impact? Are there other second-order impacts because of the nature of the issue? Are other services disrupted as well? Again, it gets back to the complexity factor. There are interrelationships between the various components that make up any service. Those services are shared because that’s how it is. People lean on those things — and that’s the risk you take.

Before, the lack of speed literally killed because you had to figure a lot of those things out while the clock was ticking and the impact was going on. But now, you’re allowing yourself time to figure things out. That’s what we call a decision-support system. You want to alert ahead of time to ensure that you understand the true blast area of what the potential destruction is going to be.

Secondly, can I spin up the right level of communications so that everybody who could be affected knows about it? And thirdly, can I now get the right people on the call — versus hunting and pecking to determine who has a problem on the fly at 3 a.m.?

The nature of having speed is when you deal with an issue by buying time for firms to deal with the thing intelligently versus in a shotgun approach and without truly understanding the nature of the impact until the next day.

Gardner: Sean, it sounds like operation resiliency is something that never stops. It’s an ongoing process. That’s what buys you the time because you’re always trying to anticipate. Is that the right way to look at it?

Culbert: It absolutely is the way to look at it. A time objective may be specific to the type of service, and obviously it’s going to be different from a consumer bank to a broker-dealer. You will have a time objective attached to a service, but is that a critical service that, if disrupted, could further disrupt critical operations that could then disrupt the real economy? That’s come into focus in the last 10 years. It has forced people to think through: If you were if a broker-dealer and you couldn’t meet your hedge fund positions, or if you were a consumer bank and you couldn’t get folks their paychecks, does that put people in financial peril?

These involve very different processes and have very different outcomes. But each has a tolerance of filling in the blank time. So now it’s just more of a matter of being accountable for those times. There are two things: There’s the customer expectation that you won’t reach those tolerances and be able to meet the time objective to meet the customers’ needs.

And the second is that technology has made it more manageable as the domino or contagion effect of one service tipping over another one. So now it’s not just, “Is your service ready to go within its objective of half an hour?” It’s about the knock-on effect to other services as well. 

So, it’s become a lot more correlated, and it’s become regional. Something that might be a critical service in one business, might not be in another — or in one region, might not be in another. So, it’s become more of a multidimensional management problem in terms of categorically specific time objectives against specific geographies, and against the specific regulations that overhang the whole thing.

Gardner: Steve, you mentioned earlier about taking the call at 3 a.m. It seems to me that we have a different way of looking at this now — not just taking the call but making the call. What’s the difference between taking the call and making the call? How does that help us prepare for better operation resiliency?

Make the call, don’t take the call

Yon: It’s a fun way of looking a day in the life of your chief resiliency officer or chief risk officer (CRO) and how it could go when something bad happens. So, you could take the call from the CEO or someone from the board as they wonder why something is failing. What are you going to do about it?

You’re caught on your heels trying to figure out what was going on, versus making the call to the CEO or the board member to let them know, “Hey, these were the potential disruptions that the firm was facing today. And this is how we weathered through it without incident and without damaging service operations or suffering service operations that would have been unacceptable.”

We like to think of it as not only trying to prevent the impact to the clients but also from the possibility of a systemic problem. It could potentially increase the lifespan of a CRO by showing they can be responsible for the firm’s up-time, versus just answer questions post-disruption. It provides a little bit of levity but it’s also a truth that there are more than just the consequences to the clients, but also to those people responsible for that function within the firm. 

Gardner: Many leading-edge organizations have been doing digital transformation for some time. We’re certainly in the thick of digital transformation now after the COVID requirements of doing everything digitally rather than in person. 

But when it comes to finance and the services that we’re describing — the interconnections in the interdependencies — there are cyber resiliency requirements that cut across organizational boundaries. Having a moat around your organization, for example, is no longer enough.

What is it about the way that ServiceNow and EY are coming together that helps make operational resiliency an ongoing process possible? 

Digital transformation opens access

Yon: There are two components. You need to ask yourself, “What needs to be true for the outcome that we’re talking about to be valid?” From a supply-side, what needs to be true is, “Do I have good signal and telemetry across all the components and assets of resources that would pose a threat or a cause for a threat to happen from a down service?”

With the move to digital transformation, more assets and resources that compose any organization are now able to be accessed. That means the state of any particular asset, in terms of its preferential operating model, are going to be known.

With the move to digital transformation, more assets and resources that compose any organization are now able to be accessed. That means the state of any particular asset, in terms of its preferential operating model, are going to be known. I need to have that data and that’s what digital transformation provides.

Secondly, I need a platform that has wide integration capabilities and that has workflow at its core. Can I perform business logic and conditional synthesis to interpret the signals that are coming from all these different systems?

That’s what’s great about ServiceNow — there hasn’t been anything that it hasn’t been able to integrate with. Then it comes down to, “Okay, do I understand the nature of what it is I’m truly looking for as a business service and how it’s constructed?” Once I do that, I’m able to capture that control, if you will, determine its threshold, see that there’s a trigger, and then drive the workflows to get something done.

For a hypothetical example, we’ve had an event so that we’re losing the trading floor in city A, therefore I know that I need to bring city B and its employees online and to make them active so I can get that up and running. ServiceNowcan drive that all automatically, within the Now Platform itself, or drive a human to provide the approvals or notifications to drive the workflows as part of your business continuity plan (BCP) going forward. You will know what to do by being able to detect and interpret the signals, and then based on that, act on it.

That’s what ServiceNow brings to make the solution complete. I need to know what that service construction is and what it means within the firm itself. And that’s where EY comes to the table, and I’ll ask Sean to talk about that.

Culbert: ServiceNow brings to the table what we need to scale and integrate in a logical and straightforward way. Without having workflows that are cross-silo and cross-product at scale — and with solid integration of capabilities – this just won’t happen.

When we start talking about the signals from everywhere against all the services — it’s a sprawl. From an implementation perspective, it feels like it’s not implementable.

The regulatory burden requires focus on what’s most important, and why it’s most important to the market, the balance sheet, and the customers. And that’s not for the 300 services, but for the one or two dozen services that are important. Knowing that gives us a big step forward by being able to scope out the ServiceNow implementation.

And from there, we can determine what dimensions associated with that service we should be capturing on a real-time basis. To progress from remediation to detection on to prevention, we must be judicious of what signals we’re tracking. We must be correct.

We have the requirement and obligation to declare and describe what is critical using a scalable and integrable technology, which is ServiceNow. That’s the big step forward.

Yon: The Now platform also helps us to be fast. If you look under the hood of most firms, you’ll find ServiceNow is already there. You’ll see that there’s already been work done in the risk management area. They already know the concepts and what it means to deal with policies and controls, as well as the triggers and simulations. They have IT and other assets under management, and they know what a configuration management database (CMDB) is.

These are all accelerants that not only provide scale to get something done but provide speed because so many of these assets and service components are already identified. Then it’s just a matter of associating them correctly and calibrating it to what’s really important so you don’t end up with a science fair integration project.

Gardner: What I’m still struggling to thread together is how the EY ServiceNow alliance operational resiliency solution becomes proactive as an early warning system. Explain to me how you’re able to implement this solution in such a way that you’re going to get those signals before the crisis reaches a crescendo.

Tracking and recognizing faults

Yon: Let’s first talk about EY and how it comes with an understanding from the industry of what good looks like with respect to what a critical business service needs to be. We’re able to hone down to talking about payments or trading. This maps the deconstruction of that service, which we also bring as an accelerant.

We know what it looks like — all the different resources, assets, and procedures that make that critical service active. Then, within ServiceNow, it manages and exposes those assets. We can associate those things in the tool relatively quickly. We can identify the signal that we’re looking to calibrate on.

Then, based on what ServiceNow knows how to do, I can put a control parameter on this service or component within the threshold. It then gives me an indication whether something might be approaching a fault condition. We basically look at all the different governance, risk management, and compliance (GRC) leading indicators and put telemetry around those things when, for example, it looks like my trading volume is starting to drop off.

Based on what ServiceNow knows how to do, I can put a control parameter on this service or component within the threshold. It then gives me an indication whether something might be approaching a fault condition.

Long before it drops to zero, is there something going on elsewhere? It delivers up all the signals about the possible dimensions that can indicate something is not operating per its normal expected behavior. That data is then captured, synthesized, and displayed either within ServiceNow or it is automated to start running its own tests to determine what’s valid.

But at the very least, the people responsible are alerted that something looks amiss. It’s not operating within the control thresholds already set up within ServiceNow against those assets. This gives people time to then say, “Okay, am I looking at a potential problem here? Or am I just looking at a blip and it’s nothing to worry about?”

Gardner: It sounds like there’s an ongoing learning process and a data-gathering process. Are we building a constant mode of learning and automation of workflows? Do we do get a whole greater than the sum of the parts after a while?

Culbert: The answer is yes and yes. There’s learning and there’s automation. We bring to the table some highly effective regulatory risk models. There’s a five-pillar model that we’ve used where market and regulatory intelligence feeds risk management, surveillance, analysis, and ultimately policy enforcement.

And how the five pillars work together within ServiceNow — it works together within the business processes within the organization. That’s where we get that intelligence feeding, risk feeding, surveillance analysis, and enforcement. That workflow is the differentiator, to allow rapid understanding of whether it’s an immediate risk or concentrating risk.

And obviously, no one is going to be 100 percent perfect, but having context and perspective on the origin of the risk helps determine whether it’s a new risk — something that’s going to create a lot of volatility – or whether it’s something the institution has faced before.

We rationalize that risk — and, more importantly, rationalize the lack of a risk – to know at the onset if it’s a false positive. It’s an essential market and regulatory intelligence mechanism. Are they feeding us only the stuff that’s really important?

Our risk models tell us that. That risk model usually takes on a couple of different flavors. One flavor is similar to a FICO score. So, have you seen the risk? Have you seen it before? It is characterizable by the words coming from it and its management in the past.

And then some models are more akin to a bar calculator. What kind of volatility is this risk going to bring to the bank? Is it somebody that’s recreationally trying to get into the bank, or is it a state actor?

Once the false-positive gets escalated and disposed of — if it’s, in fact, a false positive – are we able to plug it into something robust enough to surveil for where that risk is headed? That’s the only way to get out in front of it.

The next phase of the analysis says, “Okay, who should we talk to about this? How do we communicate that this is bigger than a red box, much bigger than a red box, a real crisis-type risk? What form does that communication take? Is it a full-blown crisis management communication? Is it a standing management communication or protocol?”

We take that affected function and very quickly understand the health or the resiliency of other impacted functions. We use our own proprietary model. It helps to shift from primary states to alternative states.

And then ultimately, this goes to ServiceNow, so we take that affected function and very quickly understand the health or the resiliency of other impacted functions. We use our own propriety model. It’s a military model used for nuclear power plants, and it helps to shift from primary states to alternative states, as well as to contingency and emergency states.

At the end, the person who oversees policy enforcement must gain the tools to understand where they should be fixing the primary state issue or moving on from it. They must know to step aside or shift into an emergency state.

From our perspective, it is constant learning. But there are fundamental pillars that these events flow through that deliver the problem to the right person and give that person options for minimizing the risk.

Gardner: Steve, do we have any examples or use cases that illustrate how alerting the right people with the right skills at the right time is an essential part of resuming critical business services or heading off the damage?

Rule out retirement risks

Yon: Without naming names, we have a client within Europe, the Middle East and Africa (EMEA) we can look at. One of the things the pandemic brought to light is the need to know our posture to continuing to operate the way we want. Getting back to integration and integrability, where are we going to get a lot of that information for personnel from? Workday, their human resources (HR) system of record, of course.

Now, they had a critical business service owner who was going to be retiring. That sounds great. That’s wonderful to hear. But one of the valid things for this critical business service to be considered operating in its normal state is to check for an owner. Who will cut through the issues and process and lead going forward?

If there isn’t an owner identified for the service, I would be considered at risk for this service. It may not be capable of maintaining its continuity. So, here’s a simple use case where someone could be looking at a trigger from Workday that asks if this leadership person is still in the role and active.

Is there a control around identifying if they are going to become inactive within x number of months’ time? If so, get on that because the regulators will look at these processes potentially being out of control.

There’s a simple use case that has nothing to do with technology but shows the integrability of ServiceNow into another system of record. It turns ServiceNow into a decision-support platform that drives the right actions and orchestrates timely actions — not only to detect a disruption but anything else considered valid as a future risk. Such alerts give the time to get it taken care of before a fault happens.

Gardner: The EY ServiceNow alliance operational resilience solution is under the covers but it’s powering leaders’ ability to be out in front of problems. How does the solution enable various levels of leadership personas, even though they might not even know it’s this solution they’re reacting to?

Leadership roles evolve

Culbert: That’s a great question. For the last six to seven years, we’ve all heard about the shift from the second to the first line of primary ownership in the private sector. I’ve heard many occasions for our first line business manager saying, “You know, if it is my job, first I need to know what the scope of my responsibilities are and the tools to do my job.” And that persona of the frontline manager having good data, that’s not a false positive. It’s not eating at his or her ability to make money. It’s providing them with options of where to go to minimize the issue.

The personas are clearly evolving. It was difficult for risk managers to move solidly into the first line without these types of tools. And there were interim management levels, too. Someone who sat between the first and the second line — level 1.5. or line 1.5. And it’s clearly pushing into the first line. How do they know their own scope as relates to the risk to the services?

Now there’s a tool that these personas can use to be not only be responsible for risk but responsive as well. And that’s a big thing in terms of the solution design. With ServiceNow over the last several years, if the base data is correctly managed, then being able to reconfigure the data and recalibrate the threshold logic to accommodate a certain persona is not a coding exercise. It’s a no-code step forward to say, “Okay, this is now the new role and scope, and that role and scope will be enabled in this way.” And this power is going to direct the latest signals and options.

But it’s all about the definition of a service. Do we all agree end-to-end what it is, and the definition of the persona? Do we all understand who’s accountable and who’s responsible? Those two things are coming together with a new set of tools that are right and correct.

Yon: Just to go back to the call at 3 a.m., that was a tech call. But typically, what happens is there’s also going to be the business call. So, one of the issues we’re also solving with ServiceNow is in one system we manage the nature of information irrespective of what your persona is. You have a view of risk that can be tailored to what it is that you care about. And all the data is congruent back and forth.

It becomes a lot more efficient and accurate for firms to manage the nature of understanding on what things are when it’s not just the tech community talking. The business community wants to know what’s happening – and what’s next? And then someone can translate in between. This is a real-time way for all those personas to become a line around the nature of the issue with respect to their perspective.

Gardner: I really look forward to the next in our series of discussions around operational resilience because we’re going to learn more about the May announcement of this solution. 

But as we close out today’s discussion, let’s look to the future. We mentioned earlier that almost any highly regulated industry will be facing similar requirements. Where does this go next?

It seems to me that the more things like machine learning (ML) and artificial intelligence (AI) analyze the many sources of data, they will make it even more powerful. What should we look for in terms of even more powerful implementations?

AI to add power to the equation

Culbert: When you set up the framework correctly, you can apply AI to the thinning out of false positives and for tagging certain events as credible risk events or not credible risk events. AI can also to be used to direct these signals to the right decision makers. But instead of taking the human analyst out of the equation, AI is going to help us. You can’t do it without that framework.

Yon: When you enable these different sets of data coming in for AI, you start to say, “Okay, what do I want the picture to look like in my ability to simulate these things?” It all goes up, especially using ServiceNow.

But back to the comment on complexity and the fact that suppliers don’t just supply one client, they connect to many. As this starts to take hold in the regulated industries — and it becomes more of an expectation for a supplier to be able to operate this way and provide these signals, integration points, telemetry, and transparency that people expect — anybody else trying to lever into this is going to get the lift and the benefit from suppliers who realize that the nature of playing in this game just went up. Those benefits become available to a much broader landscape of industries and for those suppliers.

Gardner: When we put two and two together, we come up with a greater sum. We’re going to be able to deal rapidly with the known knowns, as well as be better prepared for the unknown unknowns. So that’s an important characteristic for a much brighter future — even if we hit another unfortunate series of risk-filled years such as we’ve just suffered.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: ServiceNow and EY.

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Thinking of IT security as a fortress or a moat around your compute assets has given way to a more realistic and pervasive posture.

Such a cybersecurity perimeter, it turns out, was only an illusion. A far more effective extended-enterprise strategy protects business assets and processes wherever they are — and wherever they might reach.

As businesses align to new approaches such as zero trust and behavior-modeling to secure their data, applications, infrastructure, and networks, there’s a new, rapidly expanding digital domain that needs such pervasive and innovative protection.

The next BriefingsDirect security trends discussion explores how application programming interfaces (APIs), microservices, and cloud-native computing form a new frontier for cybersecurity vulnerabilities — as well as opportunities for innovative defenses and resilience.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

To learn more about why your expanding use of APIs may be the new weak link in your digital business ecosystem, please welcome Jyoti Bansal, Chief Executive Officer and Co-Founder at Traceable.ai. The interview is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Jyoti, has the global explosion of cloud-native apps and services set us up for a new variety of security vulnerability? How serious is this new threat?

Bansal: Well, it’s definitely new and it’s quite serious. If you look at every time we go through a change in IT architectures, we get a new set of security challenges. The adoption of cloud-native architectures means challenges in a few things. 

One, you have a lot of APIs and these APIs are doors and entryways into your systems and your apps. If those are not secured properly, you have more opportunities for attackers to steal data. You want to open the APIs so that you can expose data, but attackers will try to exploit that. We are seeing more examples of that happening.

The second major challenge with cloud-native apps is around the software development model. Development now is more high-velocity, more Agile. People are using DevOps and continuous integration and continuous delivery (CI/CD). That creates the velocity. You’re changing things once every hour, sometimes even more often.

That creates new kinds of opportunities for developers to make mistakes in their apps and in their APIs, and in how they design a microservice; or in how different microservices or APIs interact with each other. That often creates a lot more opportunity for attackers to exploit.

Gardner: Companies, of course, are under a lot of pressure to do things quickly and to react to very dynamic business environments. At the same time, you have to always cover your backside with better security. How do companies face the tension between speed and safety?

Speed and safety for cloud-native apps

Bansal: That’s the biggest tension, in many ways. You are forced to move fast. The speed is important. The pandemic has been even more of a challenge for a lot of companies. They had to move to more of a digital experience much faster than they imagined. So speed has become way more prominent.

But that speed creates a challenge around safety, right? Speed creates two main things. One is that you have more opportunity to make mistakes. If you ask people to do something very fast because there’s so much business and consumer pressure, sometimes you cut corners and make mistakes.

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Not deliberately. It’s just as software engineers can never write completely bug-free code. But if you have more bugs in your code because you are moving very, very fast, it creates a greater challenge.

So how do you create safety around it? By catching these security bugs and issues much earlier in your software development life cycle (SDLC). If a developer creates a new API and that API could be exploited by a hacker — because there is a bug in that API around security authentication check — you have to try to find it in your test cycle and your SDLC.

The second way to gain security is by creating a safety net. Even if you find things earlier in your SDLC, it’s impossible to catch everything. In the most ideal world, you’d like to ship software that has zero vulnerabilities and zero gaps of any kind when it comes to security. But that doesn’t happen, right?

You have to create a safety net so that if there are vulnerabilities because the business pressure was there to move fast, that safety net that can still block what occurs and stop those from trying to do things that you didn’t intend from your APIs and applications.

Gardner: And not only do you have to be thinking about APIs you’re generating internally, but there are a lot of third-party APIs out there, along with microservices, when doing extended-enterprise processes. It’s a bit of a Wild West environment when it comes to these third-party APIs.

Bansal: Definitely. The APIs you are building and using internally through your microservices may also have an external consumer calling those APIs. Other microservices may also be calling them — and so there is exposure around that.

Third-party APIs manifest in two different ways. One is that you might be using a third-party API or library in your microservice. There might be a security gap there.

The second way comes when you’re calling on third-party APIs. And now almost everything is exposed as APIs – such as if you want to check for some data somewhere or call some other software as a service (SaaS) service or cloud service, or a payment service. Everything is an API, and those APIs are not always called properly. All of those APIs are not secure, and so your system fundamentally can become more insecure.

It is getting close to a wild, Wild West with APIs. I think we have to take API security quite seriously at this point.

Gardner: We’ve been talking about API security as a function of growing pains, that you’re moving fast, and this isn’t a process that you might be used to.

But there’s also malice out there. We’ve seen advanced, persistent threats in such things as zero-day exploits and with Microsoft Exchange Serversrecently. We’ve certainly seen with the SolarWinds exploits how a supply chain can be made vulnerable.

Have we seen people take advantage of APIs, too, or is that something that we should expect?

API attacks a global threat

Bansal: Well, we should definitely expect that. We are seeing people take advantage of these APIs. If you look at data from Gartner, they stated that by 2022, API abuses will move from an infrequent to the most frequent attack vector. That will result in more data breaches in enterprises and web applications. That is the new direction because of how applications are consumed with APIs.

The API has naturally become a more frequent form of attack vector now.

Gardner: Do you expect, Jyoti, that this is going to become mission-critical? We’re only part way into the “software eats the world” thing. As we expect software to become more critical to the world, APIs are becoming more part of that. Could API vulnerabilities become a massive, global threat vector?

Bansal: Yes, definitely. We are, as you said, only partially into the software-eats-the-world trend. We are still not fully there. We are only 30 to 40 percent there. But as we see more and more APIs, those will create a new kind of attack vector.

For a long time, people didn’t think about APIs. People only thought about APIs as internal. External APIs were very few. Now, APIs are a major source of how other systems integrate across the globe. The traffic coming through APIs is significantly increasing.

It’s a matter of now taking these threats seriously. For a long time, people didn’t think about APIs. People only thought about APIs as internal APIs; that you will put internal APIs between your code and different internal services. The external APIs were very few. Most of your users were coming through a web application or a mobile application, and so you were not exposing your APIs as much to external applications.

If you look at banking, for example, most of the bank services software was about online banking. End users came through a bank web site, and then users came through mobile apps. They didn’t have to worry too much about APIs to do their business.

Now, that’s no longer the case. For any bank, APIs are a major source of how other systems integrate with them. Banks didn’t have to expose their systems through those apps that they built, but now a lot of third-party apps are written on top of those APIs — from a wallet app, to different kinds of payment systems, to all sorts of things that are out there — because that’s what consumers are looking for. So, now — as you start doing that — the amount of traffic coming through that API is not just through the web or mobile front-ends directly. It’s significantly increasing.

The general use of internal APIs is increasing. With the adoption of cloud-native and microservices architectures, the internal-to-external boundary is starting to blur too much. Internal APIs could become external at any point because the same microservice that our engineering team wrote is now being used by your other internal microservices inside of your company. But they are also being exposed to your partners or other third-party systems to do something, right?

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More and more APIs are being exposed out there. We will see this continued explosion of APIs because that’s the nature of how modern software is built. APIs are the building block of modern software systems.

I think we have two options as an industry. Either we say, “Okay, APIs could be risky or someone could attack them, so let’s not use APIs.” But that to me is completely wrong because APIs are what’s driving the flexibility and fluidity of modern software systems and the velocity that we need. We have to just learn as an industry to instead secure APIs and be serious about securing them.

Gardner: Jyoti, your role there as CEO and co-founder at Traceable.ai is not your first rodeo. You’ve been a serial startup leader and a Silicon Valley tech visionary. Tell us about your other major companies, AppDynamics, in particular, and why that puts you in a position to recognize the API vulnerability — but also come up with novel ways of making APIs more secure.

History of troubleshooting

Bansal: Yes. I have a unique advantage in that I have founded companies to solve big problems like these in the past. AppDynamics was my first company, which I started back in 2008. The purpose was to give development teams good solutions to diagnose and troubleshoot when something goes wrong in their distributed software systems.

At that time, we were starting to see a lot of service-oriented architectures (SOA). People were struggling when something was slow and users experienced slowdowns from their websites. How do you figure out where the slowdown is? How do you find the root cause? 

That space eventually became what is called application performance management (APM). What we came up with was, “How about we instrument what’s going on inside the code in production? How about we trace the flow of code from one service to another service, or to a third service or a database? Then we can figure out where the slow down and bottlenecks are.”

By understanding what’s happening in these complex software systems, you can figure out where the performance bottleneck is. We were quite successful as a company. We were acquired by Cisco just a day before we were about to go IPO.

The approach we used there solves problems around performance – so monitoring, diagnosing, and troubleshooting diagnostics. The fundamental approach was about instrumenting and learning what was going on inside the systems.

That’s the same approach we at Traceable.ai apply to solving the problems around API security. We have all these challenges around APIs; they’re everywhere, and it’s the wild, Wild West of APIs.

So how do you get in control? You don’t want to ask developers to slow down and not do any APIs. You don’t want to reduce the velocity. The way you get control over it is fundamentally a very similar approach to what we used at AppDynamics for performance monitoring and troubleshooting. And that is by understanding everything that can be instrumented in the APIs’ environment.

That means for all external APIs, all internal APIs, and all the third-party APIs. It means learning how the data flows between these different APIs, which users call different APIs, what they are trying to achieve out of it, what APIs are changed by developers, and which APIs have sensitive data in them.

Once you are in control of what is there, you can learn if some user is trying to use these APIs in a bad way. You know what seems like an attack, or if something wrong is happening. Then you can quickly go into prevention mode. You can block that attack.

Once you automatically understand that — about all of the APIs – then you start to get in control of what is there. Once you are in control of what’s there, you can learn if some user is trying to use these APIs in a bad way. You know what seems like an attack, or if something wrong is happening. There might be a data breach or something. Then you can quickly go into prevention mode. You can then block that attack.

There are a lot of similarities from my experience at my last company, AppDynamics, in terms of how we solve challenges around API security. I also started a second company, Harness. It’s in a different space, targeting DevOps and software developers, and helping them with CI/CD. Harness is now one of the leading platforms for CI/CD or DevOps.

So I have a lot of experience from the vantage point of what do modern software engineer organizations have to do from a CI/CD DevOps perspective, and what security challenges they start to run into.

We talk to Harness customers doing modern CI/CD about application and API security. And it almost always comes as one big challenge. They are worried about microservices, about cloud-native architectures, and about moving more to APIs. They need to get in control and to create a safety net around all of this.

Gardner: Does your approach of trace, monitor, and understand the behavior apply to what’s going on in operations as well as what goes on in development? Is this a one-size-fits-all solution? Or do you have to attack those problems separately?

One-size-fits-all advantages

Bansal: That’s the beauty of this approach. It is in many ways a one-size-fits-all approach. It’s about how you use the data that comes out of this trace-everything instrument. Fundamentally it works in all of these areas.

It works because the engineering teams put in what we call a lightweight agent. That agent goes inside the runtime of the code itself, running in different programming languages, such as Java, PHP, and Python. The agents could also run in your application proxies in your environment.

You put the same kinds of instruments, lightweight agents, in for your external APIs, your internal microservices APIs, as well as the third-party APIs that you’re calling. It’s all the same.

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About Traceable.ai

When you have such instrumentation tracing, you can take the same approach everywhere. Ideally, you put the same in a pre-production environment while you are going through the software testing lifecycle in a CI/CD system. And then, after some testing, staging, and load testing, you start putting the same instrumentation into production, too. You want the same kind of approach across all of that.

In the testing cycle, we will tell you — based on all instrumentation and tracing, looking at all the calls based on your tests – that these are the places that are vulnerable, such as these are the APIs that have gaps and could be exploited by someone.

Then, once you do the same approach in production, we tell you not only about the vulnerabilities but also where to block attacks that are happening. We say, “This is the place that is vulnerable, right now there is an attacker trying to attack this API and steal data, and this is how we can block them.” This happens in real-time, as they do it.

But it’s fundamentally the same approach that is being used across your full SDLC lifecycle.

Gardner: Let’s look at the people in these roles or personas, be it developer, operations, SecOps, and traditional security. Do you have any examples or metrics of where API vulnerabilities have cropped up? What vulnerabilities are these people already seeing?

Vulnerable endpoints to protect

Bansal: A lot of API vulnerabilities crop up around unauthenticated endpoints, such as exposing an API and it doesn’t have the right kind of authentication. Second is around not using the right authorization, such as calling an API that is supposed to give you data for you as user 1, but the authorization had a flaw that could be exploited for you to take data — not just as user 1 but from someone else, a user 2, or maybe even a large number of users. That’s a common problem that happens too often with APIs.

There are also leaky APIs that give you more data than they should, such as it’s only supposed to give the name of someone, but it also includes more sensitive data.

In the world of application security, we have the OWASP Top Ten list that the app security teams and the security teams have followed for a long time. And normally you would have things like SQL injection and cross-site scripting, and those were always in that list.

Now there’s an additional list called the OWASP API Security Top Ten, which lists the top threats when it comes to APIs. Some of the threats I described are key parts of it. And there are a lot of examples of these API-involved attacks these days.

Just recently in 2020, we had a Starbucks vulnerability in API calls, which potentially exposed 100 million customer records. It was around an authentication vulnerability. In 2019, Capital One was a high-profile example. There was an Amazon Web Services (AWS) configuration API that wasn’t secured properly and an attacker got access to it. It exposed all the AWS resources that Capital One had.

We are starting to see patterns emerge on the vulnerabilities attackers are exploiting in APIs. No one should take API security lightly these days. It’s a big mistake if companies are not getting to this faster.

There was a very high-profile attack that happened on T-Mobile in 2018, where there was an API leaking more data than it was supposed to. Some 2.3 million customers’ data was stolen. In another high-profile attack, at Venmo, a public API was not exposing the data for the right users so 200 million transactions of data were stolen from Venmo. As you can see from these examples, we are starting to see patterns emerge on the vulnerabilities attackers are exploiting in APIs.

Gardner: Now, these types of attacks and headlines are going to get the attention of the very top of any enterprise, especially now where we’re seeing GDPR and other regulations require disclosure of these sorts of breaches and exposures. This is not just nice to have. This sounds like potentially something that could make or break a company if it’s not remediated.

Bansal: Definitely. No one should take API security lightly these days. A lot of the traditional cybersecurity teams have put a lot of their focus and energy in securing the networks and infrastructure. And many of them are just starting to get serious about this next API threat vector. It’s a big mistake if companies are not getting to this faster. They are exposing themselves in a big way.

Gardner: The top lesson for security teams, as they have seen in other types of security vulnerabilities, is you have to know what’s there, protect it, and then be proactive. What is it about the way that you’re approaching these problems that set you up to be able to be proactive — rather than reactive — over time?

Know it, protect it, and go proactive

Bansal: Yes, the fundamentals of security are the same. You have to know what is there, you have to protect it, and then you become proactive about it. And that’s the approach we have taken in our solution at Traceable.ai.

Number one is all about API discovery and risk assessment. You put us there in your environment and very quickly we’ll tell you what all the APIs are. It’s all about discovery and inventory as the very first thing. These are all your external APIs. These are all your internal APIs. These are all the third-party APIs that you are invoking. So it starts with discovery. You have to know what is there. And you create an inventory of everything.

The second part, when you create that inventory, is to give a risk score. We give every API a risk score: internal API, external API, and third-party, all of them. The risk score is based on many dimensions, such as which APIs have sensitive data flowing through them, which APIs are exposed publicly versus not, which APIs have what kind of authentication to them, and what APIs are internally using your critical database systems and reading data from those. Based on all of these factors, we are creating a risk heat map of all of our APIs.

The most important part for APIs security is to do this continuously. Because you’re living in the world of CI/CD, any kind of API discovery or assessment cannot be static, like you do it once a month, once a quarter, or even once a week. You have to do it dynamically all the time because code is changing. Developers are putting new code continuously out there. So the APIs are changing, with new microservices. All of the discovery and risk assessment has to happen continuously. So, that’s really the first challenge we handle at Traceable.ai.

The second problem we handle is to build a learning model. That learning model is based on a very sophisticated machine learning (ML) approach on what is the normal usage behavior of each of these APIs. What users are calling an API? What sequence do they get called? What kind of data passes through them? What kinds of data are they fetching out of where? And on and on.

We are learning all of that automatically. Once you learn that, you start comparing every new API request with what the normal model of how your APIs are supposed to be used.

Now, if you have an attacker trying to use an API to extract much more data than what is normal for that data, you know that something is abnormal about it. You could flag it, and that’s a key part of how we think of the second part, which is how do you protect these APIs from bad behavior.

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That cannot be done with the traditional web application firewall (WAF)and runtime application self-protection (RASP), and those kinds of approaches. Those are very rule-based or static-rules-type of base approaches. For APIs, you have to build a behavioral learning-based system. That’s what our solution is about. That’s how we get to a very high degree of protection for these APIs.

The third element to the solution is the proactive part. After a lot of this learning, we also examine the behavior of these APIs and the potential vulnerabilities, based on the models. The right way to proactively use our system is to feed that into your testing and development cycle. That brings the issues back to the developers to fix the vulnerabilities. We can help find them earlier in the lifecycle so you can integrate that into what you’re doing in your application security testing processes. It closes the loop on you doing all of this – only proactively now.

Gardner: Jyoti, what should businesses do to prepare themselves at an early stage for API security? Who should be tasked with kicking this off?

Build your app security team

Bansal: API security falls under the umbrella of app security. In many businesses, app security teams are now tasked to secure the APIs in addition to the traditional web applications.

The first thing every business has to do is to create a responsibility around securing APIs. You have to bring in something to understand the inventory. They don’t even know what all of the APIs are. Then you can start securing and getting a better posture.

In many places, we are also seeing businesses create teams around what they call product security. If you are a company with FinTech products, your product is an API because your product is primarily exposed to APIs. Then people start building out product security teams who are tasked with securing all of these APIs. In some cases, we see the software engineering team directly responsible for securing APIs.

Whatever the model is, the first thing every business has to do is to create a responsibility around securing APIs. After that, you have to bring in something to understand the inventory. I still am amazed every time I see so many businesses we talk to struggling with just even knowing what APIs are there.

The problem is they don’t even know what all of their APIs are. They may have 500 or 2,000 developers in the company. They are building all of these APIs, and can’t even track them. So most businesses have to get an understanding and some kind of control over the APIs that are there. Then you can start securing and getting a better security posture around those.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Traceable.ai.

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Businesses these days are quick to declare their intention to become data-driven, yet the deployment of analytics and the use of data science remains spotty, isolated, and often uncoordinated.

To fully reach their digital business transformation potential, businesses large and small need to make data science more of a repeatable assembly line — an industrialization, if you will — of end-to-end data exploitation.

The next BriefingsDirect Voice of Analytics Innovation discussion explores the latest methods, tools, and thinking around making data science an integral core function that both responds to business needs and scales to improve every aspect of productivity.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

To learn more about the ways that data and analytics behave more like a factory — and less like an Ivory Tower — please welcome Doug Cackett, EMEA Field Chief Technology Officer at Hewlett Packard Enterprise. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Doug, why is there a lingering gap — and really a gaping gap — between the amount of data available and the analytics that should be taking advantage of it?

Cackett: That’s such a big question to start with, Dana, to be honest. We probably need to accept that we’re not doing things the right way at the moment. Actually, Forrester suggests that something like 40 zettabytes of data are going to be under management by the end of this year, which is quite enormous.

And, significantly, more of that data is being generated at the edge through applications, Internet of Things (IoT), and all sorts of other things. This is where the customer meets your business. This is where you’re going to have to start making decisions as well.

So, the gap is two things. It’s the gap between the amount of data that’s being generated and the amount you can actually comprehend and create value from. In order to leverage that data from a business point of view, you need to make decisions at the edge.

You will need to operationalize those decisions and move that capability to the edge where your business meets your customer. That’s the challenge we’re all looking for machine learning (ML) — and the operationalization of all of those ML models into applications — to make the difference.

Gardner: Why does HPE think that moving more toward a factory model, industrializing data science, is part of the solution to compressing and removing this gap?

Data’s potential at the edge

Cackett: It’s a math problem, really, if you think about it. If there is exponential growth in data within your business, if you’re trying to optimize every step in every business process you have, then you’ll want to operationalize those insights by making your applications as smart as they can possibly be. You’ll want to embed ML into those applications.

Because, correspondingly, there’s exponential growth in the demand for analytics in your business, right? And yet, the number of data scientists you have in your organization — I mean, growing them exponentially isn’t really an option, is it? And, of course, budgets are also pretty much flat or declining.

There’s exponential growth in the demand for analytics in your business. And yet the number of data scientists in your organization, growing them, is not exponential. And budgets are pretty much flat or declining.

So, it’s a math problem because we need to somehow square away that equation. We somehow have to generate exponentially more models for more data, getting to the edge, but doing that with fewer data scientists and lower levels of budget.

Industrialization, we think, is the only way of doing that. Through industrialization, we can remove waste from the system and improve the quality and control of those models. All of those things are going to be key going forward.

Gardner: When we’re thinking about such industrialization, we shouldn’t necessarily be thinking about an assembly line of 50 years ago — where there are a lot of warm bodies lined up. I’m thinking about the Lucille Ball assembly line, where all that candy was coming down and she couldn’t keep up with it.

Perhaps we need more of an ultra-modern assembly line, where it’s a series of robots and with a few very capable people involved. Is that a fair analogy?

Industrialization of data science

Cackett: I think that’s right. Industrialization is about manufacturing where we replace manual labor with mechanical mass production. We are not talking about that. Because we’re not talking about replacing the data scientist. The data scientist is key to this. But we want to look more like a modern car plant, yes. We want to make sure that the data scientist is maximizing the value from the data science, if you like.

We don’t want to go hunting around for the right tools to use. We don’t want to wait for the production line to play catch up, or for the supply chain to catch up. In our case, of course, that’s mostly data or waiting for infrastructure or waiting for permission to do something. All of those things are a complete waste of their time.

As you look at the amount of productive time data scientists spend creating value, that can be pretty small compared to their non-productive time — and that’s a concern. Part of the non-productive time, of course, has been with those data scientists having to discover a model and optimize it. Then they would do the steps to operationalize it.

But maybe doing the data and operations engineering things to operationalize the model can be much more efficiently done with another team of people who have the skills to do that. We’re talking about specialization here, really.

But there are some other learnings as well. I recently wrote a blog about it. In it, I looked at the modern Toyota production system and started to ask questions around what we could learn about what they have learned, if you like, over the last 70 years or so.

It was not just about automation, but also how they went about doing research and development, how they approached tooling, and how they did continuous improvement. We have a lot to learn in those areas.

For an awful lot of organizations that I deal with, they haven’t had a lot of experience around such operationalization problems. They haven’t built that part of their assembly line yet. Automating supply chains and mistake-proofing things, what Toyota called jidoka, also really important. It’s a really interesting area to be involved with.

Gardner: Right, this is what US manufacturing, in the bricks and mortar sense, went through back in the 1980s when they moved to business process reengineering, adopted kaizen principles, and did what Demingand more quality-emphasis had done for the Japanese auto companies.

And so, back then there was a revolution, if you will, in physical manufacturing. And now it sounds like we’re at a watershed moment in how data and analytics are processed.

Cackett: Yes, that’s exactly right. To extend that analogy a little further, I recently saw a documentary about Morgan cars in the UK. They’re a hand-built kind of car company. Quite expensive, very hand-built, and very specialized.

And I ended up by almost throwing things at the TV because they were talking about the skills of this one individual. They only had one guy who could actually bend the metal to create the bonnet, the hood, of the car in the way that it needed to be done. And it took two or three years to train this guy, and I’m thinking, “Well, if you just automated the process, and the robot built it, you wouldn’t need to have that variability.” I mean, it’s just so annoying, right?

In the same way, with data science we’re talking about laying bricks — not Michelangelo hammering out the figure of David. What I’m really trying to say is a lot of the data science in our customer’s organizations are fairly mundane. To get that through the door, get it done and dusted, and give them time to do the other bits of finesse using more skills — that’s what we’re trying to achieve. Both [the basics and the finesse] are necessary and they can all be done on the same production line.

Gardner: Doug, if we are going to reinvent and increase the productivity generally of data science, it sounds like technology is going to be a big part of the solution. But technology can also be part of the problem.

What is it about the way that organizations are deploying technology now that needs to shift? How is HPE helping them adjust to the technology that supports a better data science approach?

Define and refine

Cackett: We can probably all agree that most of the tooling around MLOps is relatively young. The two types of company we see are either companies that haven’t yet gotten to the stage where they’re trying to operationalize more models. In other words, they don’t really understand what the problem is yet.

Forrester research suggests that only 14 percent of organizations that they surveyed said they had a robust and repeatable operationalization process. It’s clear that the other 86 percent of organizations just haven’t refined what they’re doing yet. And that’s often because it’s quite difficult.

Many of these organizations have only just linked their data science to their big data instances or their data lakes. And they’re using it both for the workloads and to develop the models. And therein lies the problem. Often they get stuck with simple things like trying to have everyone use a uniform environment. All of your data scientists are both sharing the data and sharing the computer environment as well.

Data scientists can be very destructive in what they’re doing. Maybe overwriting data, for example. To avoid that, you end up replicating terabytes of data, which can take a long time. That also demands new resources, including new hardware.

And data scientists can often be very destructive in what they’re doing. Maybe overwriting data, for example. To avoid that, you end up replicating the data. And if you’re going to replicate terabytes of data, that can take a long period of time. That also means you need new resources, maybe new more compute power and that means approvals, and it might mean new hardware, too.

Often the biggest challenge is in provisioning the environment for data scientists to work on, the data that they want, and the tools they want. That can all often lead to huge delays in the process. And, as we talked about, this is often a time-sensitive problem. You want to get through more tasks and so every delayed minute, hour, or day that you have becomes a real challenge.

The other thing that is key is that data science is very peaky. You’ll find that data scientists may need no resources or tools on Monday and Tuesday, but then they may burn every GPU you have in the building on Wednesday, Thursday, and Friday. So, managing that as a business is also really important. If you’re going to get the most out of the budget you have, and the infrastructure you have, you need to think differently about all of these things. Does that make sense, Dana?

Gardner: Yes. Doug how is HPE Ezmeral being designed to help give the data scientists more of what they need, how they need it, and that helps close the gap between the ad hoc approach and that right kind of assembly line approach?

Two assembly lines to start

Cackett: Look at it as two assembly lines, at the very minimum. That’s the way we want to look at it. And the first thing the data scientists are doing is the discovery.

The second is the MLOps processes. There will be a range of people operationalizing the models. Imagine that you’re a data scientist, Dana, and I’ve just given you a task. Let’s say there’s a high defection or churn rate from our business, and you need to investigate why.

First you want to find out more about the problem because you might have to break that problem down into a number of steps. And then, in order to do something with the data, you’re going to want an environment to work in. So, in the first step, you may simply want to define the project, determine how long you have, and develop a cost center.

You may next define the environment: Maybe you need CPUs or GPUs. Maybe you need them highly available and maybe not. So you’d select the appropriate-sized environment. You then might next go and open the tools catalog. We’re not forcing you to use a specific tool; we have a range of tools available. You select the tools you want. Maybe you’re going to use Python. I know you’re hardcore, so you’re going to code using Jupyter and Python.

And the next step, you then want to find the right data, maybe through the data catalog. So you locate the data that you want to use and you just want to push a button and get provisioned for that lot. You don’t want to have to wait months for that data. That should be provisioned straight away, right?

You can do your work, save all your work away into a virtual repository, and save the data so it’s reproducible. You can also then check the things like model drift and data drift and those sorts of things. You can save the code and model parameters and those sorts of things away. And then you can put that on the backlog for the MLOps team.

Then the MLOps team picks it up and goes through a similar data science process. They want to create their own production line now, right? And so, they’re going to seek a different set of tools. This time, they need continuous integration and continuous delivery (CICD), plus a whole bunch of data stuff they want to operationalize your model. They’re going to define the way that that model is going to be deployed. Let’s say, we’re going to use Kubeflow for that. They might decide on, say, an A/B testing process. So they’re going to configure that, do the rest of the work, and press the button again, right?

Clearly, this is an ongoing process. Fundamentally that requires workflow and automatic provisioning of the environment to eliminate wasted time, waiting for stuff to be available. It is fundamentally what we’re doing in our MLOps product.

But in the wider sense, we also have consulting teams helping customers get up to speed, define these processes, and build the skills around the tools. We can also do this as-a-service via our HPE GreenLake proposition as well. Those are the kinds of things that we’re helping customers with.

Gardner: Doug, what you’re describing as needed in data science operations is a lot like what was needed for application development with the advent of DevOps several years ago. Is there commonality between what we’re doing with the flow and nature of the process for data and analytics and what was done not too long ago with application development? Isn’t that also akin to more of a cattle approach than a pet approach?

Operationalize with agility

Cackett: Yes, I completely agree. That’s exactly what this is about and for an MLOps process. It’s exactly that. It’s analogous to the sort of CICD, DevOps, part of the IT business. But a lot of that tool chain is being taken care of by things like Kubeflow and MLflow Project, some of these newer, open source technologies.

I should say that this is all very new, the ancillary tooling that wraps around the CICD. The CICD set of tools are also pretty new. What we’re also attempting to do is allow you, as a business, to bring these new tools and on-board them so you can evaluate them and see how they might impact what you’re doing as your process settles down.

The way we’re doing MLOps and data science is progressing extremely quickly. So you don’t want to lock yourself into a corner where you’re trapped in a particular workflow. You want to have agility. It’s analogous to the DevOps movement.

The idea is to put them in a wrapper and make them available so we get a more dynamic feel to this. The way we’re doing MLOps and data science generally is progressing extremely quickly at the moment. So you don’t want to lock yourself into a corner where you’re trapped into a particular workflow. You want to be able to have agility. Yes, it’s very analogous to the DevOps movement as we seek to operationalize the ML model.

The other thing to pay attention to are the changes that need to happen to your operational applications. You’re going to have to change those so they can tool the ML model at the appropriate place, get the result back, and then render that result in whatever way is appropriate. So changes to the operational apps are also important.

Gardner: You really couldn’t operationalize ML as a process if you’re only a tools provider. You couldn’t really do it if you’re a cloud services provider alone. You couldn’t just do this if you were a professional services provider.

It seems to me that HPE is actually in a very advantageous place to allow the best-of-breed tools approach where it’s most impactful but to also start put some standard glue around this — the industrialization. How is HPE is an advantageous place to have a meaningful impact on this difficult problem?

Cackett: Hopefully, we’re in an advantageous place. As you say, it’s not just a tool, is it? Think about the breadth of decisions that you need to make in your organization, and how many of those could be optimized using some kind of ML model.

You’d understand that it’s very unlikely that it’s going to be a tool. It’s going to be a range of tools, and that range of tools is going to be changing almost constantly over the next 10 and 20 years.

This is much more to do with a platform approach because this area is relatively new. Like any other technology, when it’s new it almost inevitably to tends to be very technical in implementation. So using the early tools can be very difficult. Over time, the tools mature, with a mature UI and a well-defined process, and they become simple to use.

But at the moment, we’re way up at the other end. And so I think this is about platforms. And what we’re providing at HPE is the platform through which you can plug in these tools and integrate them together. You have the freedom to use whatever tools you want. But at the same time, you’re inheriting the back-end system. So, that’s Active Directory and Lightweight Directory Access Protocol (LDAP) integrations, and that’s linkage back to the data, your most precious asset in your business. Whether that be in a data lake or a data warehouse, in data marts or even streaming applications.

This is the melting point of the business at the moment. And HPE has had a lot of experience helping our customers deliver value through information technology investments over many years. And that’s certainly what we’re trying to do right now.

Gardner: It seems that HPE Ezmeral is moving toward industrialization of data science, as well as other essential functions. But is that where you should start, with operationalizing data science? Or is there a certain order by which this becomes more fruitful? Where do you start?

Machine learning leads change

Cackett: This is such a hard question to answer, Dana. It’s so dependent on where you are as a business and what you’re trying to achieve. Typically, to be honest, we find that the engagement is normally with some element of change in our custo
mers. That’s often, for example, where there’s a new digital transformation initiative going on. And you’ll find that the digital transformation is being held back by an inability to do the data science that’s required.

There is another Forrester report that I’m sure you’ll find interesting. It suggests that 98 percent of business leaders feel that ML is key to their competitive advantage. It’s hardly surprising then that ML is so closely related to digital transformation, right? Because that’s about the stage at which organizations are competing after all.

So we often find that that’s the starting point, yes. Why can’t we develop these models and get them into production in time to meet our digital transformation initiative? And then it becomes, “Well, what bits do we have to change? How do we transform our MLOps capability to be able to do this and do this at scale?”

Often this shift is led by an individual in an organization. There develops a momentum in an organization to make these changes. But the changes can be really small at the start, of course. You might start off with just a single ML problem related to digital transformation.

We acquired MapR some time ago, which is now our HPE Ezmeral Data Fabric. And it underpins a lot of the work that we’re doing. And so, we will often start with the data, to be honest with you, because a lot of the challenges in many of our organizations has to do with the data. And as businesses become more real-time and want to connect more closely to the edge, really that’s where the strengths of the data fabric approach come into play.

So another starting point might be the data. A new application at the edge, for example, has new, very stringent requirements for data and so we start there with building these data systems using our data fabric. And that leads to a requirement to do the analytics and brings us obviously nicely to the HPE Ezmeral MLOps, the data science proposition that we have.

Gardner: Doug, is the COVID-19 pandemic prompting people to bite the bullet and operationalize data science because they need to be fleet and agile and to do things in new ways that they couldn’t have anticipated?

Cackett: Yes, I’m sure it is. We know it’s happening; we’ve seen all the research. McKinsey has pointed out that the pandemic has accelerated a digital transformation journey. And inevitably that means more data science going forward because, as we talked about already with that Forrester research, some 98 percent think that it’s about competitive advantage. And it is, frankly. The research goes back a long way to people like Tom Davenport, of course, in his famous Harvard Business Reviewarticle. We know that customers who do more with analytics, or better analytics, outperform their peers on any measure. And ML is the next incarnation of that journey.

Gardner: Do you have any use cases of organizations that have gone to the industrialization approach to data science? What is it done for them?

Financial services benefits

Cackett: I’m afraid names are going to have to be left out. But a good example is in financial services. They have a problem in the form of many regulatory requirements.

When HPE acquired BlueData it gained an underlying technology, which we’ve transformed into our MLOps and container platform. BlueData had a long history of containerizing very difficult, problematic workloads. In this case, this particular financial services organization had a real challenge. They wanted to bring on new data scientists. But the problem is, every time they wanted to bring a new data scientist on, they had to go and acquire a bunch of new hardware, because their process required them to replicate the data and completely isolate the new data scientist from the other ones. This was their process. That’s what they had to do.

So as a result, it took them almost six months to do anything. And there’s no way that was sustainable. It was a well-defined process, but it’s still involved a six-month wait each time.

So instead we containerized their Cloudera implementation and separated the compute and storage as well. That means we could now create environments on the fly within minutes effectively. But it also means that we can take read-only snapshots of data. So, the read-only snapshot is just a set of pointers. So, it’s instantaneous.

They scaled out their data science without scaling up their costs or the number of people required. They are now doing that in a hybrid cloud environment. And they only have to change two lines of code to push workloads into AWS, which is pretty magical, right?

They were able to scale-out their data science without scaling up their costs or the number of people required. Interestingly, recently, they’ve moved that on further as well. Now doing all of that in a hybrid cloud environment. And they only have to change two lines of code to allow them to push workloads into AWS, for example, which is pretty magical, right? And that’s where they’re doing the data science.

Another good example that I can name is GM Finance, a fantastic example of how having started in one area for business — all about risk and compliance — they’ve been able to extend the value to things like credit risk.

But doing credit risk and risk in terms of insurance also means that they can look at policy pricing based on dynamic risk. For example, for auto insurance based on the way you’re driving. How about you, Dana? I drive like a complete idiot. So I couldn’t possibly afford that, right? But you, I’m sure you drive very safely.

But in this use-case, because they have the data science in place it means they can know how a car is being driven. They are able to look at the value of the car, the end of that lease period, and create more value from it.

These are types of detailed business outcomes we’re talking about. This is about giving our customers the means to do more data science. And because the data science becomes better, you’re able to do even more data science and create momentum in the organization, which means you can do increasingly more data science. It’s really a very compelling proposition.

Gardner: Doug, if I were to come to you in three years and ask similarly, “Give me the example of a company that has done this right and has really reshaped itself.” Describe what you think a correctly analytically driven company will be able to do. What is the end state?

A data-science driven future

Cackett: I can answer that in two ways. One relates to talking to an ex-colleague who worked at Facebook. And I’m so taken with what they were doing there. Basically, he said, what originally happened at Facebook, in his very words, is that to create a new product in Facebook they had an engineer and a product owner. They sat together and they created a new product.

Sometime later, they would ask a data scientist to get involved, too. That person would look at the data and tell them the results.

Then they completely changed that around. What they now do is first find the data scientist and bring him or her on board as they’re creating a product. So they’re instrumenting up what they’re doing in a way that best serves the data scientist, which is really interesting.

The data science is built-in from the start. If you ask me what’s going to happen in three years’ time, as we move to this democratization of ML, that’s exactly what’s going to happen. I think we’ll end up genuinely being information-driven as an organization.

That will build the data science into the products and the applications from the start, not tack them on to the end.

Gardner: And when you do that, it seems to me the payoffs are expansive — and perhaps accelerating.

Cackett: Yes. That’s the competitive advantage and differentiation we started off talking about. But the technology has to underpin that. You can’t deliver the ML without the technology; you won’t get the competitive advantage in your business, and so your digital transformation will also fail.

This is about getting the right technology with the right people in place to deliver these kinds of results.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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As businesses seek to gain insights for more elements of their physical edge — from factory sensors, myriad machinery, and across field operations — data remains fragmented. But a Data Fabric approach allows information and analytics to reside locally at the edge yet contribute to the global improvement in optimizing large-scale operations.

Stay with us now as we explore how edge-to-core-to-cloud dispersed data can be harmonized with a common fabric to make it accessible for use by more apps and across more analytics.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. 

To learn more about the ways all data can be managed for today’s data-rich but too often insights-poor organizations, we’re joined by Chad Smykay, Field Chief Technology Officer for Data Fabric at Hewlett Packard Enterprise (HPE). The interview is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Chad, why are companies still flooded with data? It seems like they have the data, but they’re still thirsty for actionable insights. If you have the data, why shouldn’t you also have the insights readily available?

Smykay: There are a couple reasons for that. We still see today challenges for our customers. One is just having a common data governance methodology. That’s not just to govern the security and audits, and the techniques around that — but determining just what your data is.

I’ve gone into so many projects where they don’t even know where their data lives; just a simple matrix of where the data is, where it lives, and how it’s important to the business. This is really the first step that most companies just don’t do.

Gardner: What’s happening with managing data access when they do decide they want to find it? What’s been happening with managing the explosive growth of unstructured data from all corners of the enterprise?

Tame your data

Smykay: Five years ago, it was still the Wild West of data access. But we’re finally seeing some great standards being deployed and application programming interfaces (APIs) for that data access. Companies are now realizing there’s power in having one API to rule them all. In this case, we see mostly Amazon S3. 

There are some other great APIs for data access out there, but just having more standardized API access into multiple datatypes has been great for our customers. It allows for APIs to gain access across many different use cases. For example, business intelligence (BI) tools can come in via an API. Or an application developer can access the same API. So that approach really cuts down on my access methodologies, my security domains, and just how I manage that data for API access.

Gardner: And when we look to get buy-in from the very top levels of businesses, why are leaders now rethinking data management and exploitation of analytics? What are the business drivers that are helping technologists get the resources they need to improve data access and management?

Smykay: The business drivers gain when data access methods are as reusable as possible across the different use cases. It used to be that you’d have different point solutions, or different open source tools, needed to solve a business use-case. That was great for the short-term, maybe with some quarterly project or something for the year you did it in.

Gaining a common, secure access layer that can access different types of data is the biggest driver of our HPE Data Fabric. And the business drivers gain when the data access methods are as reusable as possible.

But then, down the road, say three years out, they would say, “My gosh, we have 10 different tools across the many different use cases we’re using.” It makes it really hard to standardize for the next set of use cases.

So that’s been a big business driver, gaining a common, secure access layer that can access different types of data. That’s been the biggest driver for our HPE Data Fabric. That and having common API access definitely reduces the management layer cost, as well as the security cost.

Gardner: It seems to me that such data access commonality, when you attain it, becomes a gift that keeps giving. The many different types of data often need to go from the edge to dispersed data centers and sometimes dispersed in the cloud. Doesn’t data access commonality also help solve issues about managing access across disparate architectures and deployment models?

Smykay: You just hit the nail on the head. Having commonality for that API layer really gives you the ability to deploy anywhere. When I have the same API set, it makes it very easy to go from one cloud provider, or one solution, to another. But that can also create issues in terms of where my data lives. You still have data gravity issues, for example. And if you don’t have portability of the APIs and the data, you start to see some lock-in with the either the point solution you went with or the cloud provider that’s providing that data access for you.

Gardner: Following through on the gift that keeps giving idea, what is it about the Data Fabric approach that also makes analytics easier? Does it help attain a common method for applying analytics?

Data Fabric deployment options

Smykay: There are
a couple of things there. One, it allows you to keep the data where it may need to stay. That could be for regulatory reasons or just depend on where you build and deploy the analytics models. A Data Fabric helps you to start separating out your computing and storage capabilities, but also keeps them coupled for wherever the deployment location is.

For example, a lot of our customers today have the flexibility to deploy IT resources out in the edge. That could be a small cluster or system that pre-processes data. They may typically slowly trickle all the data back to one location, a core data center or a cloud location. Having these systems at the edge gives them the benefit of both pushing information out, as well as continuing to process at the edge. They can choose to deploy as they want, and to make the data analytics solutions deployed at the core even better for reporting or modeling.

Gardner: It gets to the idea of act locally and learn globally. How is that important, and why are organizations interested in doing that?

Smykay: It’s just-in-time, right? We want everything to be faster, and that’s what this Data Fabric approach gets for you.

In the past, we’ve seen edge solutions deployed, but you weren’t processing a whole lot at the edge. You were pushing along all the data back to a central, core location — and then doing something with that data. But we don’t have the time to do that anymore.

Unless you can change the laws of physics — last time I checked, they haven’t done that yet — we’re bound by the speed of light for these networks. And so we need to keep as much data and systems as we can out locally at the edge. Yet we need to still take some of that information back to one central location so we can understand what’s happening across all the different locations. We still want to make the rearview reporting better globally for our business, as well as allow for more global model management.

Gardner: Let’s look at some of the hurdles organizations have to overcome to make use of such a Data Fabric. What is it about the way that data and information exist today that makes it hard to get the most out of it? Why is it hard to put advanced data access and management in place quickly and easily?

Track the data journey

Smykay: It’s tough for most organizations because they can’t take the wings off the airplane while flying. We get that. You have to begin by creating some new standards within your organization, whether that’s standardizing on an API set for different datatypes, multiple datatypes, a single datatype.

Then you need to standardize the deployment mechanisms within your organization for that data. With the HPE Data Fabric, we give the ability to just say, “Hey, it doesn’t matter where you deploy. We just need some x86 servers and we can help you standardize either on one API or multiple APIs.”

We now support more than 10 APIs, as well as the many different data types that these organizations may have.

We see a lot of data silos out there today with customers — and they’re getting worse. They’re now all over the place between multiple cloud providers. And there’s all the networking in the middle. I call it silo sprawl.

Typically, we see a lot of data silos still out there today with customers – and they’re getting worse. By worse, I mean they’re now all over the place between multiple cloud providers. I may use some of these cloud storage bucket systems from cloud vendor A, but I may use somebody else’s SQL databases from cloud vendor B, and those may end up having their own access methodologies and their own software development kits (SDKs).

Next you have to consider all the networking in the middle. And let’s not even bring up security and authorization to all of them. So we find that the silos still exist, but they’ve just gotten worse and they’ve just sprawled out more. I call it the silo sprawl.

Gardner: Wow. So, if we have that silo sprawl now, and that complexity is becoming a hurdle, the estimates are that we’re going to just keep getting more and more data from more and more devices. So, if you don’t get a handle on this now, you’re never going to be able to scale, right?

Smykay: Yes, absolutely. If you’re going to have diversity of your data, the right way to manage it is to make it use-case-driven. Don’t boil the ocean. That’s where we’ve seen all of our successes. Focus on a couple of different use cases to start, especially if you’re getting into newer predictive model management and using machine learning (ML) techniques.

But, you also have to look a little further out to say, “Okay, what’s next?” Right? “What’s coming?” When you go down that data engineering and data science journey, you must understand that, “Oh, I’m going to complete use case A, that’s going to lead to use case B, which means I’m going to have to go grab from other data sources to either enrich the model or create a whole other project or application for the business.”

You should create a data journey and understand where you’re going so you don’t just end up with silo sprawl.

Gardner: Another challenge for organizations is their legacy installations. When we talk about zettabytes of data coming, what is it about the legacy solutions — and even the cloud storage legacy — that organizations need to rethink to be able to scale?

Zettabytes of data coming

Smykay: It’s a very important point. Can we just have a moment of silence? Because now we’re talking about zettabytes of data. Okay, I’m in.

Some 20 years ago, we were talking about petabytes of data. We thought that was a lot of data, but if you look out to the future, we’re talking about some studies showing connected Internet of Things (IoT) devices generating this zettabytes amount of data.

If you don’t get a handle on where your data points are going to be generated, how they’re going to be stored, and how they’re going to be accessed now, this problem is just going to get worse and worse for organizations.

Look, Data Fabric is a great solution. We have it, and it can solve a ton of these problems. But as a consultant, if you don’t get ahead of these issues right now, you’re going to be under the umbrella of probably 20 different cloud solutions for the next 10 years. So, really, we need to look at the datatypes that you’re going to have to support, the access methodologies, and where those need to be located and supported for your organization.

Gardner: Chad, it wasn’t that long ago that we were talking about how to manage big data, and Hadoop was a big part of that. NoSQL and other open source databases in particular became popular. What is it about the legacy of the big data approach that also needs to be rethought?

Smykay: One common issue we often see is the tendency to go either/or. By that I mean saying, “Okay, we can do real-time analytics, but that’s a separate data deployment. Or we can do batch, rearview reporting analytics, and that’s a separate data deployment.” But one thing that our HPE Data Fabric has always been able to support is both — at the same time — and that’s still true.

So if you’re going down a big data or data lake journey — I think now the term now is a data lakehouse, that’s a new one. For these, basically I need to be able to do my real-time analytics, as well as my traditional BI reporting or rearview mirror reporting — and that’s what we’ve been doing for over 10 years. That’s probably one of the biggest limitations we have seen.

But it’s a heavy lift to get that data from one location to another, just because of the metadata layer of Hadoop. And then you had dependencies with some of these NoSQL databases out there on Hadoop, it caused some performance issues. You can only get so much performance out of those databases, which is why we have NoSQL databases just out of the box of our Data Fabric — and we’ve never run into any of those issues.

Gardner: Of course, we can’t talk about end-to-end data without thinking about end-to-end security. So, how do we think about the HPE Data Fabric approach helping when it comes to security from the edge to the core?

Secure data from edge to core

Smykay: This is near-and-dear to my heart because everyone always talks about these great solutions out there to do edge computing. But I always ask, “Well, how do you secure it? How do you authorize it? How does my application authorization happen all the way back from the edge application to the data store in the core or in the cloud somewhere?”

That’s what I call off-sprawl, where those issues just add up. If we don’t have one way to secure and manage all of our different data types, then what happens is, “Okay, well, I have this object-based system out there, and it has its own authorization techniques.” It has its own authentication techniques. By the way, it has its own way of enforcing security in terms of who has access to what, unless … I haven’t talked about monitoring, right? How do we monitor this solution?

So, now imagine doing that for each type of data that you have in your organization — whether it’s a SQL database, because that application is just a driving requirement for that, or a file-based workload, or a block-based workload. You can see where this starts to steamroll and build up to be a huge problem within an organization, and we see that all the time.

We’re seeing a ton of issues today in the security space. We’re seeing people getting hacked. It happens all the way down to the application layer, as you often have security sprawl that makes it very hard to manage all of the different systems.

And, by the way, when it comes to your application developers, that becomes the biggest annoyance for them. Why? Because when they want to go and create an application, they have to go and say, “Okay, wait. How do I access this data? Oh, it’s different. Okay. I’ll use a different key.” And then, “Oh, that’s a different authorization system. It’s a completely different way to authenticate with my app.”

I honestly think that’s why we’re seeing a ton of issues today in the security space. It’s why we’re seeing people get hacked. It happens all the way down to the application layer, as you often have this security sprawl that makes it very hard to manage all of these different systems.

Gardner: We’ve come up in this word sprawl several times now. We’re sprawling with this, we’re sprawling with that; there’s complexity and then there’s going to be even more scale demanded.

The bad news is there is quite a bit to consider when you want end-to-end data management that takes the edge into consideration and has all these other anti-sprawl requirements. The good news is a platform and standards approach with a Data Fabric forms the best, single way to satisfy these many requirements.

So let’s talk about the solutions. How does HPE Ezmeral generally — and the Ezmeral Data Fabric specifically — provide a common means to solve many of these thorny problems?

Smykay: We were just talking about security. We provide the same security domain across all deployments. That means having one web-based user interface (UI), or one REST API call, to manage all of those different datatypes.

We can be deployed across any x86 system. And having that multi-API access — we have more than 10 – allows for multi-data access. It includes everything from storing data into files and storing data in blocks. We’re soon going to be able to support blocks in our solution. And then we’ll be storing data into bit streams such as Kafka, and then into a NoSQL database as well.

Gardner: It’s important for people to understand that HPE Ezmeral is a larger family and that the Data Fabric is a subset. But the whole seems to be greater than the sum of the parts. Why is that the case? How has what HPE is doing in architecting Ezmeral been a lot more than data management?

Smykay: Whenever you have this “whole is greater than the sum of the parts,” you start reducing so many things across the chain. When we talk about deploying a solution, that includes, “How do I manage it? How do I update it? How do I monitor it?” And then back to securing it.

Honestly, there is a great report from IDC that says it best. We show a 567-percent, five-year return on investment (ROI). That’s not from us, that’s IDC talking to our customers. I don’t know of a better business value from a solution than that. The report speaks for itself, but it comes down to these paper cuts of managing a solution. When you start to have multiple paper cuts, across multiple arms, it starts to add up in an organization.

Gardner: Chad, what is it about the HPE Ezmeral portfolio and the way the Data Fabric fits in that provides a catalyst to more improvement?

All data put
to future use

Smykay: One, the HPE Data Fabric can be deployed anywhere. It can be deployed independently. We have hundreds and hundreds of customers. We have to continue supporting them on their journey of compute and storage, but today we are already shipping a solution where we can containerize the Data Fabric as a part of our HPE Ezmeral Container Platform and also provide persistent storage for your containers.

The HPE Ezmeral Container Platform comes with the Data Fabric, it’s a part of the persistent storage. That gives you full end-to-end management of the containers, not only the application APIs. That means the management and the data portability.

So, now imagine being able to ship the data by containers from your location, as it makes sense for your use case. That’s the powerful message. We have already been on the compute and storage journey; been down that road. That road is not going away. We have many customers for that, and it makes sense for many use cases. We’ve already been on the journey of separating out compute and storage. And we’re in general availability today. There are some other solutions out there that are still on a road map as far as we know, but at HPE we’re there today. Customers have this deployed. They’re going down their compute and storage separation journey with us.

Gardner: One of the things that gets me excited about the potential for Ezmeral is when you do this right, it puts you in a position to be able to do advanced analytics in ways that hadn’t been done before. Where do you see the HPE Ezmeral Data Fabric helping when it comes to broader use of analytics across global operations?

Smykay: One of our CMOs used to say it best, and which Jack Morris has said: “If it’s going to be about the data, it better be all about the data.”

When you improve automating data management across multiple deployments — managing it, monitoring it, keeping it secure — you can then focus on those actual use cases. You can focus on the data itself, right? That’s living in the HPE Data Fabric. That is the higher-level takeaway. Our users are not spending all their time and money worrying about the data lifecycle. Instead, they can now go use that data for their organizations and for future use cases.

HPE Ezmeral sets your organization up to use your data instead of worrying about your data. We are set up to start using the Data Fabric for newer use cases and separating out compute and storage, and having it run in containers. We’ve been doing that for years. The high-level takeaway is you can go focus on using your data and not worrying about your data.

Gardner: How about some of the technical ways that you’re doing this? Things like global namespaces, analytics-ready fabrics, and native multi-temperature management. Why are they important specifically for getting to where we can capitalize on those new use cases?

Smykay: Global namespaces is probably the top feature we hear back from our customers on. It allows them to gain one view of the data with the same common security model. Imagine you’re a lawyer sitting at your computer and you double-click on a Data Fabric drive, you can literally then see all of your deployments globally. That helps with discovery. That helps with bringing onboard your data engineers and data scientists. Over the years that’s been one of the biggest challenges, they spend a lot of time building up their data science and data engineering groups and on just discovering the data.

Global namespace means I’m reducing my discovery time to figure out where the data is. A lot of this analytics-ready value we’ve been supporting in the open source community for more than 10 years. There’s a ton of Apache open source projects out there, like Presto, Hive, and Drill. Of course there’s also Spark-ready, and we have been supporting Spark for many years. That’s pretty much the de facto standard we’re seeing when it comes to doing any kind of real-time processing or analytics on data.

As for multi-temperature, that feature allows you to decrease your cost of your deployment, but still allows managing all your data in one location. There are a lot of different ways we do that. We use erasure coding. We can tear off to Amazon S3-compliant devices to reduce the overall cost of deployment.

These features contribute to making it still easier. You gain a common Data Fabric, common security layer, and common API layer.

Gardner: Chad, we talked about much more data at the edge, how that’s created a number of requirements, and the benefits of a comprehensive approach to data management. We talked about the HPE Data Fabric solution, what it brings, and how it works. But we’ve been talking in the abstract.

What about on the ground? Do you have any examples of organizations that have bitten off and made Data Fabric core for them? As an adopter, what do they get? What are the business outcomes?

Central view benefits businesses 

Smykay: We’ve been talking a lot about edge-to-core-to-cloud, and the one example that’s just top-of-mind is a big, tier-1 telecoms provider. This provider makes the equipment for your AT&Ts and your Vodafones. That equipment sits out on the cell towers. And they have many Data Fabric use cases, more than 30 with us. 

But the one I love most is real-time antenna tuning. They’re able to improve customer satisfaction in real time and reduce the need to physically return to hotspots on an antenna. They do it via real-time data collection on the antennas and then aggregating that across all of the different layers that they have in their deployments.

One example is real-time antennae tuning. They’re able to improve customer satisfaction in real time and reduce the need to physically return to hotspots on an antennae. They do it instead via real-time data collection and aggregating that across all of their deployments.

They gain a central view of all of the data using a modern API for the DevOps needs. They still centrally process data, but they also process it at the edge today. We replicate all of that data for them. We manage that for them and take a lot of the traditional data management tasks off the table for them, so they can focus on the use case of the best way to tune antennas.

Gardner: They have the local benefit of tuning the antenna. But what’s the global payback? Do we have a business quantitative or qualitative returns for them in doing that?

Smyk
ay: Yes, but they’re pretty secretive. We’ve heard that they’ve gotten a payback in the millions of dollars, but an immediate, direct payback for them is in reducing the application development spend everywhere across the layer. That reduction is because they can use the same type of API to publish that data as a stream, and then use the same API semantics to secure and manage it all. They can then take that same application, which is deployed in a container today, and easily deploy it to any remote location around the world.

Gardner: There’s that key aspect of the application portability that we’ve danced around a bit. Any other examples that demonstrate the adoption of the HPE Data Fabric and the business pay-offs?

Smykay: Another one off the top of my head is a midstream oil and gas customer in the Houston area. This one’s not so much about edge-to-core-to-cloud. This is more about consolidation of use cases.

We discussed earlier that we can support both rearview reporting analytics as well as real-time reporting use cases. And in this case, they actually have multiple use cases, up to about five or six right now. Among them, they are able to do predictive failure reports for heat exchangers. These heat exchangers are deployed regionally and they are really temperamental. You have to monitor them all the time.

But now they have a proactive model where they can do a predictive failure monitor on those heat exchangers just by checking the temperatures on the floor cameras. They bring in all real-time camera data and they can predict, “Oh, we think we’re having an issue with this heat exchanger on this time and this day.” So that decreases management cost for them.

They also gain a dynamic parts management capability for all of their inventory in their warehouses. They can deliver faster, not only on parts, but reduce their capital expenditure (CapEx) costs, too. They have gained material measurement balances. When you push oil across a pipeline, they can detect where that balance is off across the pipeline and detect where they’re losing money, because if they are not pushing oil across the pipe at x amount of psi, they’re losing money.

So they’re able to dynamically detect that and fix it along the pipe. They also have a pipeline leak detection that they have been working on, which is modeled to detect corrosion and decay.

The point is there are multiple use cases. But because they’re able to start putting those data types together and continue to build off of it, every use case gets stronger and stronger.

Gardner: It becomes a virtuous adoption cycle; the more you can use the data generally, then the more value, then the more you invest in getting a standard fabric approach, and then the more use cases pop up. It can become very powerful.

This last example also shows the intersection of operational technology (OT) and IT. Together they can start to discover high-level, end-to-end business operational efficiencies. Is that what you’re seeing?

Data science teams work together

Smykay: Yes, absolutely. A Data Fabric is kind of the Kumbaya set among these different groups. If they’re able to standardize on the IT and developer side, it makes it easier for them to talk the same language. I’ve seen this with the oil and gas customer. Now those data science and data engineering teams work hand in hand, which is where you want to get in your organization. You want those IT teams working with the teams managing your solutions today. That’s what I’m seeing. As you get a better, more common data model or fabric, you get faster and you get better management savings by having your people working better together.

Gardner: And, of course, when you’re able to do data-driven operations, procurement, logistics, and transportation you get to what we’re referring generally as digital business transformation.

Chad, how does a Data Fabric approach then contribute to the larger goal of business transformation?

Smykay: It allows organizations to work together through a common data framework. That’s been one of the biggest issues I’ve seen, when I come in and say, “Okay, we’re going to start on this use case. Where is the data?”

Depending on size of the organization, you’re talking to three to five different groups, and sometimes 10 different people, just to put a use case together. But as you create a common data access method, you see an organization where it’s easier and easier for not only your use cases, but your businesses to work together on the goal of whatever you’re trying to do and use your data for.

Listen to the podcast. Find it on iTunes. Read a full transcript or downloada copy. Sponsor:Hewlett Packard Enterprise.

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Just as the nature of risk has been a whirling dervish of late, the counter-forces of business continuity measures have had to turn on a dime as well. What used to mean better batteries for servers and mirrored, distributed datacenters has recently evolved into anywhere, any-circumstance solutions that keep workers working — no matter what.

Out-of-the-blue workplace disruptions — whether natural disasters, political unrest, or the current coronavirus pandemic — have shown how true business continuity means enabling all employees to continue to work in a safe and secure manner.

The next BriefingsDirect business agility panel discussion explores how companies and communities alike are adjusting to a variety of workplace threats using new ways of enabling enterprise-class access and distribution of vital data resources and applications.

And in doing so, these public and private sector innovators are setting themselves up to be more agile, intelligent, and responsive to their workers, customers, and citizens once the disaster inevitably passes.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

Here to share stories on making IT systems and people evolve together to overcome workplace disruptions is Chris McMasters, Chief Information Officer (CIO) at the City of Corona, California; Jordan Catling, Associate Director of Client Technology at The University of Sydney in Australia, and Tim Minahan, Executive Vice President of Strategy and Chief Marketing Officer at Citrix. The panel is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tim, how has business readiness changed over the past few years? It seems to be a moving target.

Minahan: The very nature of business readiness is not about preparing for what’s happening today — or responding to a specific incident. It’s a signal for having a plan to ensure that your work environment is ready for any situation.

That certainly means having in place the right policies and contingency plans, but it also — with today’s knowledge workforce — goes to enabling a very flexible and dynamic workspace infrastructure that allows you to scale up, scale down, and move your entire workforce on a moment’s notice.

You need to ensure that your employees can continue to work safely and remotely while giving your company the confidence that they’re doing that all in a very secure way, so the company’s information and infrastructure remains secure.

Gardner: Chris McMasters, as a CIO, you surely remember the days when IT systems were brittle, not easily adjusted, and hard to change. Has the nature of work and these business continuity challenges forced IT to be more agile?

McMasters: Yes, absolutely. There’s no better example than in government. Government IT is known for being on-premises and very resistant to change. In the current environment everything has been flipped on its head. We’re having to be flexible, more dynamic in how we deploy services, and in how users get those services.

Gardner: Jordan, higher education hasn’t necessarily been the place where we’d expect business continuity challenges to be overcome. But you’ve been dealing with an aggressive outbreak of the coronavirus in China.

Catling: It’s been a very interesting six months for us, particularly in higher education, with the Australian fires, floods, and now the coronavirus. But generally, as an institution that operates over 22 locations, with teaching hospitals and campuses — our largest campus has its own zip code — this is part of our day, enabling people to work from wherever they are.

The really interesting thing about this situation is we’re having to enable teaching from places that we wouldn’t ordinarily. We’re having to make better use of the tools that we have available to come up with innovative solutions to keep delivering a distinctive education that The University of Sydney is known for.

Gardner: And when you’re trying to anticipate challenges, something like COVID-19, the disease that emanates from the coronavirus, did you ever think that you’d have to virtually overnight provide students stuck in one location with the opportunity to continue to learn from a distance?

Catling: We need to always be preparing for a number of scenarios. We need to be able to rapidly deploy solutions to enable people to work from wherever they are. The flexibility and dynamic toolsets are really important for us to be able to scale up safely and securely.

Gardner: Tim, the idea of business continuity including workers not only working at home but perhaps in far-flung countries where they’ve been stuck because of a quarantine, for example — these haven’t always been what we consider IT business continuity. Why is worker continuity more important than ever?

Minahan: Globally we’re recognizing the importance of the overall employee experience and how it’s becoming a key differentiator for companies and organizations. We have a global shortage of medium- to high-skilled talent. We’re short about 85 million workers. 

Companies are battling for the high ground on providing preferred ways to work. One way they do that is ensuring that they can provide flexible work environments that rely on effective workplace technologies that enable employees to do their very best work.

So companies are battling for the high ground on providing preferred ways to work. One way they do that is ensuring that they can provide flexible work environments, ones that rely on effective workplace technologies that enable employees to do their very best work wherever that might be. That might be in an office building. It might be in a remote location, or in certain situations they may need to turn on a dime and move from their office to the home force to keep operations going. Companies are planning to be flexible not just for business readiness but also for competitive advantage.

Gardner: Making this happen with enterprise-caliber, mission-critical reliability isn’t just a matter of renting some new end-devices and throwing up a few hotspots. Why is this about an end-to-end solution, and not just point solutions?

Be proactive not reactive

Minahan: One of the most important things to recognize is companies often first react to a crisis environment. Currently, you’re hearing a lot of, “Hey, we just,” like the school system in Miami, for example, “purchased 250,000 laptops to distribute to students and teachers to maintain their education.”

However, that may enable and empower students and employees, but it may be less associated with proper security measures and put both the companies’, workers’, and customers’ personal information at risk.

You need to plan from the get-go for having a very flexible, remote workplace infrastructure — one that embeds security. That way — no matter where the work needs to get done, no matter on what device, or even on whatever unfamiliar network — you can be assured that the appropriate security policies are in place to protect the private information of your employees. The critical information of your business, and certainly any kinds of customer or constituent information, is at stake.

Gardner: Let’s hear what you get when you do this right. Jordan at The University of Sydney, you had more than 14,000 students unexpectedly quarantined in China, yet they still needed to somehow do their coursework. Tell us how this came about, and what you’ve done to accommodate them.

Quality IT during quarantine

Catling: Exactly right. As this situation began to develop in late January, we quite quickly began to scenario plan around the possible eventualities. A significant part of our role, as the technologists within the university, is making sure that we’re providing a toolset that can adapt to the needs of the community.

So we looked at various platforms that we were already using — and some that we hadn’t — to work out what do. Within the academic community, we needed the best set of tools for our staff to use in different and innovative ways. We quickly had to develop solutions and had to lean on our partners to help us out with developing those.

Gardner: Did you know where your students were going to be housed? Was this a case where you knew that they were going to be in a certain type of facility with certain types of resources or are they scattered around? How did you deal with that last mile issue, so to speak?

Catling: The last mile issue is a real tricky one. We knew that people were going to be in various locations throughout mainland China, and elsewhere. We needed to quickly build a solution capable of supporting our students — no matter where they were, no matter what device that they were using, and no matter what their local Internet connection was like.

We have had variability in the quality of our connections even within Australia. But now we needed a solution that would cater to as many people as possible and be considerate of quite a number of different scenarios that our students and staff would be facing.

Gardner: How were you are able to provide that quality of service across so many applications given that level of variability?

Catling: The biggest focus for us, of course, is the safety and security of our staff and students. It’s paramount. We very quickly tried to work out where our people would be connecting from and tried to make sure that the resources we were providing, the connection to the resources, would be as close to them as possible to minimize the impact of that last mile. 

We worked with Citrix to put together a set of application delivery controllers into Hong Kong to make sure that the access to the solutions was nice and fast. We then worked to optimize the connection from Hong Kong to Sydney to maximize the user experience. 

We worked with Citrix to put together a set of application delivery controllers into Hong Kong to make sure that the access to the solution was nice and fast. Then we worked to optimize the connection back from Hong Kong to Sydney to maximize the user experience for our staff and students.

Gardner: So this has very much been a cloud-enabled solution. You couldn’t have really done this eight or 10 years ago.

Catling: Certainly not this quickly. Literally from putting a call into Citrix, we worked from design to a production environment within seven days. For me, that’s unheard of, really. Regardless of whether it’s 10 years ago or 10 weeks ago, it was quite a monumental effort. It’s highlighted the importance of having partners that both seek to understand the business problems you’re facing and coming up with innovative solutions rapidly and are able to deploy those at scale. And cloud is obviously a really important part of that.

We are still delivering on this solution. We have the capabilities now that we didn’t have a couple of months ago. We’re able to provide applications to students no matter where they are. They’re able to continue their studies.

Obviously, the solution needs to remain flexible to the evolving needs. The situation is changing frequently and we are discovering new needs and new requirements. As our academics start to use the technology in different ways, we’re evolving the solution based on their feedback to try and maximize the experience for both our staff and students.

Gardner: Tim, when you hear Jordan describe this solution, does it strike you as a harbinger of more business continuity things to come? How has the coronavirus issue — and not just China but in Europe and in North America — reinforced your idea of what a workplace-enhanced business continuity solution should be?

Business continuity in crisis

Minahan: We continue to field a rising a number of inquiries from customers and other companies. They are trying to assess the best ways to ensure continuity of their business operations and switch to a remote workforce in a very short period of time.

Situations like this remind us that we need to be planning today for any kind of business-ready situation. Using these technologies ensures that you can quickly adapt your work models, moving entire employee groups from an office to a remote environment, if needed, whether it’s because of virus, flood, or any other unplanned event.

What’s exciting for me is being able to use such agile work models and digital workspace technology to arm companies with new sources for growth and competitive advantage.

One good example is we recently partnered with the Center for Economics and Business Research to examine the impact remote work models and technologies have on business and economic growth. We found that 69 percent of people who are currently unemployed or economically inactive would be willing to start working if given the opportunity to work flexibly by having the right technology.

They further estimate that activating these, if you will, untapped pools of talent by enabling these flexible work-from-home models — especially for parents, workers in rural areas, retirees, part-time, and gig workers, folks that are normally outside of the traditional work pool and reactivating them through digital workspace technologies — could drive upward of an initial $2 trillion in economic gains across the US economy. So, the investment in readiness that folks are making is now being applied to drive ongoing business results even in non-crisis times.

Gardner: The coronavirus has certainly been leading the headlines recently, but it wasn’t that long ago that we had other striking headlines.

In California last fall, Chris McMasters, the wildfires proved a recurring problem. Tell us about Corona and why adjusting to a very dangerous environment — but requiring your key employees to continue to work – allowed you to adjust to a major business continuity challenge.

Fighting fire with cloud

McMasters: Corona is like a lot of local governments within the United States. I came from the private sector and have been in the city IT for about four years now. When I first got there, everything was on-premises. Our back-up with literally three miles away on the other side of the freeway.

If there was a disaster and something totaled the city, literally all of our technology assets would be down, which concerned me. I used to work for a national company and we had offices all over and we backed up across the country. So this was a much different environment. Yet we were dealing with public safety, which with police and fire service, 911 service, and they can never go down. Citizens depend on all of that.

That was a wake-up call for me. At that time, we didn’t really have any virtual desktop infrastructure (VDI) going on. We did have server virtualization, but nothing in the cloud. In the government sector, we have a lot of regulation that revolves around the cloud and its security, especially when we are dealing with police and fire types of information. We have to be very careful. There are requirements both from the State of California and the federal government that we have to comply with.

At first, we used a government cloud, which was a little bit slower in terms of innovation because of all the regulations. But that was a first step to understanding what was ahead for us. We started this process about two years ago. At the time, we felt like we needed to push more of our assets to the cloud to give us more continuity. 

At the end of the day, we realized we also needed to get the desktops up there, too: Using VDI and the cloud. And at the time, no one was doing that. We went and talked to Citrix on how that would extend to support our environment for public safety. Citrix has been there since day-one.

At the end of the day, we realized we also needed to get the desktops up there, too: Using VDI and the cloud. And at the time, no one was doing that. But we went and talked to Citrix. We flew out to their headquarters, sat with their people, and discussed our initiative, what we are trying to accomplish, and how that would extend out to support our environment for public safety. And that means all of the people out at the edge who actually touch citizens and provide emergency support services.

That was the beginning of the journey and Citrix has been there since day-one. They develop the products around that particular idea for us right up to today. 

In the last two years, we’ve had quite a few fires in the State of California. Corona butts right up against the forest line and so we have had a lot of damage done by fires, both in our city and in the surrounding county. And there have been the impacts that occur after fires, too, which include mudslides. We get the whole gamut of that stuff.

But now we find that those first responders have the data to take action. We get the data into their hands quickly, make sure it’s secure on the way there, and we make that continuative so that it never fails. Those are the last people that we want to have fail.

We’ve been able to utilize this type of a platform where our data currently resides in two different datacenters in two different states. It’s on encrypted arrays at rest.

We are operating on a software-defined network so we can look at security from a completely different perspective. The old way was, “Let’s build a moat around it and a big wall, and hopefully no one gets in.” Now, instead we look at it quite differently. Our assets are protected outside of our facilities.

Those personnel riding in fire engines, in police cars, right up at the edge — they have to be secure right up to that edge. We have to maintain and understand the identity of that person. We need to know what applications they are accessing, or should not be accessing, and be secure all along that path.

This has all changed our outlook on how we deal with things and what a modern-day work environment looks like. The money we use comes from taxes, the people pay, and we provide services for our citizens. The interesting thing about that is we’re now driving toward the idea of government on-demand.

Before, when you would come home, right after a hard day’s work, city hall would be closed. Government was open 8 to 5, when people are normally working. So, when you want to conduct business at city hall, you have to take some time off of work. You try to find one day of the week, or a time when you might sneak in there to get your permits for something and proceed with your business.

But our new idea is different. Most of our services can be provided online for people. If we can do that, that’s fantastic, right? So, you can come home and say, “Hey, you know what? I was thinking about building an addition to my house.” So you go online, file your permits, and submit all of your documents electronically to us. 

The difference that VDI provides for our employees is that I can now tap into a workforce of let’s say, a single mother who has a special needs child who can’t work normal hours, but she can work at night. So that person can take that permit, look at that permit at 6 or 7 pm, process the permit, and then at 5 am the next day, that process is done. You wake up in the morning, your permit has been processed by the city and completed. That type of flexibility is integral for us to make government more effective for people.

It’s not the necessarily the public safety support, which we are concerned about. But it’s about also generally providing flexible services for people and making sure government continues to operate.

Gardner:  Tim, it’s interesting that by addressing business continuity issues and disasters we are able to move very rapidly to a government on-demand or higher education on-demand. So, what are some of the larger goals when it comes to workforce agility?

Flexibility extends the business

Minahan: The examples that Chris and Jordan just gave are what excites me about flexible work models, empowered by digital workplace technologies, and the ability to embrace entirely new business models.

I used the example from the Center of Economic Business Research and how to tap into untapped talent pools. Another example of a company using similar technology is eBay. So eBay, like many of their competitors, would build a big call center and hire a bunch of people, train them up, and then one of the competitors will build a call center down the street and steal them away. They would have rapid turnover. They finally said, “Enough is enough, we have to think of a different model.”

eBay used the same approach of providing a secure digital workspace to reach into new talent pools outside of big cities. They could now hire gig workers and re-engage them in the workforce by using a workplace platform to arm them at the edge.

Well, they used the same approach of providing a secure digital workspace to reach into new talent pools outside of big cities. They could now hire gig workers, stay-at-home parents, etc., and re-engage them in the workforce by using the workplace platform to arm them at the edge and provide a service that was formally only provided in a big work hub, a big call center.

They went from having zero home force workers to 600 by the end of last year, and they are on a path to 4,000 by the end of this year. eBay solved a big problem, which is providing support for customers. How do I have a call center in a very competitive market? Well, I turn the tables and create new pools of talent, using technology in an entirely different way.

Gardner: Jordan, now that you’ve had help from organizations like Citrix to deal with your tough issue of students stuck in China, or other areas where there’s a quarantine, are you going to take that innovation and use it in other ways? Is this a gift that keeps giving?

Catling: It’s a really interesting question. What it’s demonstrated to me is that, as technologists, we need to be working with all of our people across the organization to understand their needs and to provide the right tools, but not necessarily to be prescriptive in how they are used. This current coronavirus situation has demonstrated to us that a combination of just a few tools — for example, the Citrix platform, Zoom, Echo, and Canvas — means a very different thing to one person than to another person.

There’s such large variability in the way that education is delivered across the university, across so many disciplines, that it becomes about providing a flexible set of tools that all of our people can use in different and exciting ways. That extends not only to the current situation but to more normal times.

If we can provide the right toolset that’s flexible and meets the users where they are, and also make sure that the solutions provide a natural experience, that’s when you are really geared up well for success. The technology kind of fades into the background and becomes a true enabler of the bright minds across the institution.

Gardner: Chris, now that you’re able to do more with virtual desktops and delivering data regardless of the circumstances to your critical workers as well as to your citizens, what’s the next step?

Can you add a layer of intelligence rather than just being about better feeds and speeds? What comes next, and how would Citrix help you with that?

Intelligence improves government

McMasters: We’re neck deep in data analytics and in trying to understand how we can make impacts correctly by analyzing data. So adding artificial intelligence (AI) on top of those layers, understanding utilization of our resources, is the amazing part of where we’re going.

There’s so much unused hardware and processing power tied up in our normal desktop machines. Being able to disrupt that and flip it up on its end is a fundamental change in how government operates. This is literally turning it on-end. I mean, AI can impact all the way down to how we do helpdesk, how it minimizes our response times and turnaround times, to increased productivity, and in how we service 160,000 people in my city. All of that changes.

Already I’m saving hundreds of thousands of dollars by using the cloud and VDI models and at the same time increasing all my service levels across the board. And now we can add this layer of business continuity to it, and that’s before we start benefitting from predictive AI and using data to determine asset utilization.

Moving from a CAPEX model to this OPEX model for government is something very new, it’s something that public sector or a private sector has definitely capitalized on and I think public sector is ripe for doing that. So for us, it’s changing everything, including our budget, how we deliver services, how we do helpdesk support, and on to the ways that we’re assessing our assets and leveraging citizens’ tax dollars correctly.

Gardner: Tim, organizations, both public and private sector, get involved with these intelligent workspaces in a variety of ways. Sometimes it might be a critical issue such as business continuity or a pandemic.

But ultimately, as Chris just mentioned, this is about digital business transformation. How are you able to take whatever on-ramp organizations are getting into an intelligent workspace and then give them more reasons to see ongoing productivity? How is this something that has a snowball effect on productivity?

AI, ML works with you

Minahan: Chris hit the nail on the head. Certainly, the initial on-ramps to digital workspace provides employees with unified and secure access to everything they need to be productive and in one experience. That means all of their apps, all of their content, regardless of where that’s stored, regardless of what device they’re accessing it from and regardless of where they’re accessing it from.

However, it gets really exciting when you go beyond that foundation of unified experience in a secure environment toward infusing things like machine learning (ML), digital assistants, and bots to change the way that people work. They can newly extract out some of the key insights and tasks that they need to do and offer them up to employees in real-time in a very personalized way. Then they can quickly take care of those tasks and the things they need to remove that noise from their day, and even guide them toward the right next steps to take to be even more productive, more engaged, and do much more innovative and creative work.

So, absolutely, AI and ML and the rise of bots are the next phase of all of this, where it’s not just a place you go to launch apps and work securely, but a place where you go to get your very best work done.

Gardner: Jordan, you were very impressively able to get more than 14,000 students to continue their education regardless of what Mother Nature threw at them. And you were able to do it in seven days. For those organizations that don’t want to be caught under such circumstances, that want to become proactive and prepared, what lessons have you have learned in your most recent journey that you can share with them? How can they be better positioned to combat any unfortunate circumstances they might face?

Prioritize when and how you work

Catling: It’s almost becoming cliché to say, but work is something that you do — it’s not a place anymore. So when we’re looking at and assessing tools for how we support the university, we’re focusing on taking a cloud-first approach where it doesn’t matter where a student or staff member is. They have access to all the resources they need on-demand. That’s one of the real guiding principles we should be using in our decision-making process.

Scalability is also a very important thing to us. The nature of the way that education is delivered today with an on-campus model is that demand is very peaky. We need to be mindful of how scalable and rapidly scalable a solution can be. That’s important to consider, particularly in the higher education context. How quickly can you scale up and down your environments to meet varying demands?

We can use the Citrix platform in many different ways. It’s not only for us to provide applications out to students to complete coursework. It can also be used for providing secure access to data and workspaces. 

Also, it’s important to consider the number of flexible ways that each of the technology products you choose can be used. For example, with the Citrix platform we can use it in many different ways. It’s not only for us to provide applications out to students to complete their coursework. It can also be used for providing secure access to data and to workspaces. There are so many different ways it can be extended, and that’s a real important thing when deciding which platform to use.

The final really important takeaway for us has been the establishment of true partnerships. We’ve had extremely good support from our partners, such as Citrix and Zoom, where they very rapidly sought to understand
and work with us to solve the unique business problems that we’re facing. The real, true partnership is not one of just providing products, but of really sitting down shoulder-to-shoulder, trying to understand, but also suggesting ways to use a technology we may not be thinking of — or maybe it’s never been done before.

As Chris mentioned earlier, virtual desktops in the cloud weren’t a big thing that many years ago. About a decade ago, we began working with Citrix to provide streams of desktops to physical devices across campus.

That was something — that was a very unusual use of technology. So I think that the partnership is very important and something that organizations should develop and be ready to use. It goes in both directions at all times.

Gardner: Chris, now that you have, unfortunately, dealt with these last few harsh wildfire seasons in Southern California, what lessons have you learned? How do you make yourselves more like local government on demand?

Public-private partnerships

McMasters: That’s a big question. For us, we looked at breaking some of the paradigms that exist in government. They don’t have the same impetus to change as in the private sector. They are less willing to take risks. However, there are ways to work with vendors and partners to mitigate a lot of that risk, ways to pilot and test cutting-edge technologies that don’t put you at risk as you push these things out.

There are very few vendors that I would consider such partners. I probably can count them on one hand in total, and the interesting thing is that when we were selecting a vendor for this particular project, we were looking for a true partner. In our case, it was Citrix and Microsoft who came to the table. And when I look back at what’s happened in our relationship with those two in particular, I couldn’t ask for anything better.

We have literally had technicians, engineers, everyone on-site, on the phone every step of the way as we have been developing this. They took a lot of the risk out for us, because we are dealing with public dollars and we need to make sure these projects work. To have that level of comfort and stability in the background and knowing that I can rely on these people was huge. It’s what allowed us to develop to where we are today, which is far advanced in the government world.

That’s where things have to change. This kind of public-private partnership is what the public sector needs to start maturing. It’s bidirectional; it goes both ways. There is a lot of information that we offer to them; there is a lot of things they do for us. And so it goes back and forth as we develop this through this product cycle. It’s advantageous for both of us to be in it.

That’s where sometimes, especially in the public sector, we lose focus. They don’t understand what the private sector wants and what they are moving toward. It’s about being aligned on both sides of that equation — and it benefits both parties.

Technology is going to change, and it just keeps driving faster. There’s always another thing around the corner, but building these types of partnerships with vendors and understanding what they want helps them understand what you want, and then be able to deliver.

Gardner: Tim, how should businesses better work with vendor organizations to prepare themselves and their workers for a flexible future?

Minahan: First off, I would echo Chris’s comments. We all want government on-demand. You need a solution like that. But how they should work together? There are two great examples here in The University of Sydney and the City of Corona.

It really starts by listening. What are the problems we are trying to solve in planning for the future? How do we create a digitally agile organization and infrastructure that allows us to pursue new business opportunities, and just as easily ensure business continuity? So start by listening, map out a joint roadmap together and innovate toward that.

We are collectively as an industry constantly looking to innovate, constantly looking to leverage new technologies to drive business outcomes — whether those are for our citizens, students, or clientele. Start by listening, doing joint and co-development work, and constantly sharing that innovation with the rest of the market. It raises all boats.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Citrix.

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The next BriefingsDirect Voice of artificial intelligence (AI) Innovation discussion explores the latest strategies and use cases that simplify the use of analytics to solve more tough problems.

Access to advanced algorithms, more cloud options, high-performance compute (HPC) resources, and an unprecedented data asset collection have all come together to make AI more attainable — and more powerful — than ever.

Major trends in AI and advanced analytics are now coalescing into top competitive differentiators for most businesses. Stay with us as we examine how AI is indispensable for digital transformation through deep-dive interviews on prominent AI use cases and their escalating benefits.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

To learn more about analytic infrastructure approaches that support real-life solutions, we’re joined by two experts, Andy Longworth, Senior Solution Architect in the AI and Data Practice at Hewlett Packard Enterprise (HPE) Pointnext Services, and Iveta Lohovska, Data Scientist in the Pointnext Global Practice for AI and Data at HPE. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Andy, what are the top drivers for making AI more prominent in business use cases?

Longworth: We have three main things driving AI at the moment for businesses. First of all, we know about the data explosion. These AI algorithms require huge amounts of data. So we’re generating that, especially in the industrial setting with machine data.

Also, the relative price of computing is coming down, giving the capability to process all of that data at accelerating speeds as well. You know, the graphics processing units (GPUs) and tensor processing units (TPUs) are becoming more available, enabling us to get through that vast volume of data.

And thirdly, the algorithms. If we look to organizations likeFacebook, Google, and academic institutions, they’re making algorithms available as open source. So organizations don’t have to go and employ somebody to build an algorithm from the ground up. They can begin to use these pre-trained, pre-created models to give them a kick-start in AI and quickly understand whether there’s value in it for them or not.

Gardner: And how do those come together to impact what’s referred to as digital transformation? Why are these actually business benefits?

Longworth: They allow organizations to become what we call data driven. They can use the massive data that they’ve previously generated but never tapped into to improve business decisions, impacting the way they drive the business through AI. It’s transforming the way they work.

AI data boost to business 

Across several types of industry, data is now driving the decisions. Industrial organizations, for example, improve the way they manufacture. Without the processing of that data, these things wouldn’t be possible.

Gardner: Iveta, how do the trends Andy has described make AI different now from a data science perspective? What’s different now than, say, two or three years ago?

Lohovska: Most of the previous AI algorithms were 30, 40, and even 50 years old in terms of the linear algebra and their mathematical foundations. The higher levels of computing power enable newer computations and larger amounts of data to train those algorithms.

Those two components are fundamentally changing the picture, along with the improved taxonomies and the way people now think of AI as differentiated between classical statistics and deep learning algorithms. Now, not just technical people can interact with these technologies and analytic models. Semi-technical people can with a simple drag-and-drop interaction, based on the new products in the market, adopt and fail fast — or succeed faster — in the AI space. The models are also getting better and better in their performance based on the amount of data they get trained on and their digital footprint.

Gardner: Andy, it sounds like AI has evolved to the point where it is mimicking human-like skills. How is that different and how does such machine learning (ML) and deep learning change the very nature of work?

Let simple tasks go to machines 

Longworth: It allows organizations and people to move some of the jobs that were previously very tedious for people so they can be done by machines and repurposes the people’s skills into more complex jobs. For example, in computer vision and applying that in quality control. If you’re creating the same product again and again and paying somebody to look at that product to say whether there’s a defect on it, it’s probably not the best use of their skills. And, they become fatigued.

If you look at the same thing again and again, you start to miss features of that and miss the things that have gone wrong. A computer doesn’t get that same fatigue. You can train a model to perform that quality-control step and it won’t become tired over time. It can keep going for longer than, for example, an eight-hour shift that a typical person might work. So, you’re seeing these practical applications, which then allows the workforce to concentrate on other things.

Gardner: Iveta, it wasn’t that long ago that big data was captured and analyzed mostly for the sake of compliance and business continuity. But data has become so much more strategic. How are businesses changing the way they view their data? 

Lohovska: They are paying more attention to the quality of the data and the variety of the data collection that they are focused on. From a data science perspective, even if I want to say that the performance of models is extremely important, and that my data science skills are a critical component to the AI space and ecosystem, it’s ultimately about the quality of the data and the way it’s pipelined and handled. 

Organizations will realize that being more selective and paying more attention to the foundations of how they handle big data — or small data — will get them to the data science part of the process.

This process of data manipulation, getting to the so-called last mile of the data science contribution, is extremely important. I believe it’s the critical step and foundation. Organizations will realize that being more selective and paying more attention to the foundations of how they handle big data — or small data – will get them to the data science part of the process.

You can already see the maturity as many customers, partners, and organizations pay more attention to the fundamental layers of AI. Then they can get better performance at the last mile of the process.

Gardner: Why are the traditional IT approaches not enough? How do cloud models help?

Cloud control and compliance 

Longworth: The cloud brings opportunities for organizations insomuch as they can try before they buy. So if you go back to the idea of processing all of that data, before an organization spends real money on purchasing GPUs, they can try them in the cloud to understand whether they work and deliver value. Then they can look at the delivery model. Does it make sense with my use case to make a capital investment, or do I go for a pay-per-use model using the cloud?

You also have the data management piece, which is understanding where your data is. From that sense, cloud doesn’t necessarily make life any less complicated. You still need to know where the data resides, control that data, and put in the necessary protections in line with the value of the data type. That becomes particularly important with legislation like the General Data Protection Regulation (GDPR) and the use of personally identifiable information (PII).

If you don’t have your data management under control and understand where all those copies of that data are, then you can’t be compliant with GDPR, which says you may need to delete all of that data.

So, you need to be aware of what you’re putting in the cloud versus what you have on-premises and where the data resides across your entire ecosystem.

Gardner: Another element of the past IT approaches has to do with particulars vs. standards. We talk about the difference between managing a cow and managing a herd.

How do we attain a better IT infrastructure model to attain digital business transformation and fully take advantage of AI? How do we balance between a standardized approach, but also something that’s appropriate for specific use cases? And why is the architecture of today very much involved with that sort of a balance, Andy?

Longworth: The first thing to understand is the specific use case and how quickly you need insights. We can process, for example, data in near real-time or we can use batch processing like we did in days of old. That use case defines the kind of processing.

If, for example, you think about an autonomous vehicle, you can’t batch-process the sensor data coming from that car as it’s driving on the road. You need to be able to do that in near real-time — and that comes at a cost. You not only need to manage the flow of data; you need the compute power to process all of that data in near real-time.

So, understand the criticality of the data and how quickly you need to process it. Then we can build solutions to process the data within that framework and within the right time that it needs to be processed. Otherwise, you’re putting additional cost into a use case that doesn’t necessarily need to be there. 

When we build those use cases we typically use cloud-like technologies. That allows us portability of the use case, even if we’re not necessarily going to deploy it in the cloud. It allows us to move the use case as close to the data as possible.

When we build those use cases we typically use cloud-like technologies — be that containers or scalar technologies. That allows us portability of the use case, even if we’re not necessarily going to deploy it in the cloud. It allows us to move the use case as close to the data as possible.

For example, if we’re talking about a computer vision use case on a production line, we don’t want to be sending images to the cloud and have the high latency and processing of the data. We need a very quick answer to control the production process. So you would want to move the inference engine as close to the production line as possible. And, if we use things like HPE Edgeline computing and containers, we can place those systems right there on the production line to get the answers as quickly as we need.

So being able to move the use case where it needs to reside is probably one of the biggest things that we need to consider.

Gardner: Iveta, why is the so-called explore, experiment, and evolve approach using such a holistic ecosystem of support the right way to go?

Scientific methods and solutions

Lohovska: Because AI is not easy. If it were easy, then everyone would be doing it and we would not be having this conversation. It’s not a simple statistical use case or a program or business intelligence app where you already have the answer or even an idea of the questions you are asking.

The whole process is in the data science title. You have the word “science,” so there is a moment of research and uncertainty. It’s about the way you explore the data, the way you understand the use cases, starting from the fact that you have to define your business case, and you have to define the scope.

My advice is to start small, not exhaust your resources or the trust of the different stakeholders. Also define the correct use case and the desired return on investment (ROI). HPE is even working on the definitions and the business case when approaching an AI use case, trying to understand the level of complexity and the required level of prediction needed to achieve the use case’s success.

Such an exploration phase is extremely important so that everyone is aligned and finds a right path to minimize failure and get to the success of monetizing data and AI. Once you have the fundamentals, once you have experimented with some use cases, and you see them up and running in your production environment, then it is the moment to scale them.

I think we are doing a great job bringing all of those complicated environments together, with their data complexity, model complexity, and networking and security regulations into one environment that’s in production and can quickly bring value to many use cases.

This flow is extremely important, of experimenting and not approaching things like you have a fixed answer or fixed approach. It’s extremely important, and this is the way we at HPE are approaching AI.

Gardner: It sounds as if we are approaching some sort of a unified reference architecture that’s inclusive of systems, cloud models, data management, and AI services. Is that what’s going to be required? Andy, do we need a grand unifying theory of AI and data management to make this happen?

Longworth: I don’t think we do. Maybe one day we will get to that point, but what we are reaching now is a clear understanding of what architectures work for which use cases and business requirements. We are then able to apply them without having to experiment every time we go into this because it’s a complement to what Iveta said.

When we start to look at these use cases, when we engage with customers, what’s key is making sure there is business value for the organization. We know AI can work, but the question is, does it work in the customer’s business context?

If we can take out a good deal of that experimentation and come in with a fairly good answer to the use case in a specific industry, then we have a good jump start on that.

As time goes on and AI develops, we will see more generic AI solutions that can be used for many different things. But at the moment, it’s really still about point solutions.

Gardner: Let’s find out where AI is making an impact. Let’s look first, Andy, at digital prescriptive maintenance and quality control. You mentioned manufacturing a little earlier. What’s the problem, context, and how are we getting better business outcomes?

Monitor maintenance with AI

Longworth: The problem is the way we do maintenance schedules today. If you look back in history, we had reactive maintenance that was basically … something breaks and then we fix it.

Now, most organizations are in a preventative mode so a manufacturer gives a service window and says, “Okay, you need to service this machinery every 1,000 hours of running.” And that happens whether it’s needed or not. 

Read the White Paper on Digital Prescriptive

 Maintenance and Quality Control 

When we get into prescriptive and predictive maintenance, we only service those assets as they actually need it, which means having the data, understanding the trends, recognizing if problems are forthcoming, and then fixing them before they impact the business.

That data from machinery may sense temperature, vibration, speed, and getting a condition-based monitoring view and understanding in real time what’s happening with the machinery. You can then also use past history to be able to predict what is going to happen in the future with that machine.

We can get to a point where we know in real time what’s happening with the machinery and have the capability to predict the failures before they happen.

The prescriptive piece comes in when we understand the business criticality or the business impact of an asset. If you have a production line and you have two pieces of machinery on that production line, both may have the identical probability of failure. But one is on your critical manufacturing path, and the other is some production buffer. 

The prescriptive piece goes beyond the prediction to understand the business context of that machinery and applying that to how you are behaving, and then how you react when something happens with that machine.

As a business, the way that you are going to deal with those two pieces of machinery is different. You will treat the one on the critical path differently than the one where you have a product buffer. And so the prescriptive piece goes beyond the prediction to understanding the business context of that machinery and applying that to how you are behaving, and then how you react when something happens with that machine.

That’s the idea of the solution when we build digital prescriptive maintenance. The side benefit that we see is the quality control piece. If you have a large piece of machinery that you can test to it running perfectly during a production run, for example, then you can say with some certainty what the quality of the outcoming product from that machine will be.

Gardner: So we have AI overlooking manufacturing and processing. It’s probably something that would make you sleep a little bit better at night, knowing that you have such a powerful tool constantly observing and reporting. 

Let’s move on to our next use case. Iveta, video analytics and surveillance. What’s the problem we need to solve? Why is AI important to solving it? 

Scrutinize surveillance with AI 

Lohovska: For video surveillance and video analytics in general, the overarching field is computer vision. This is the most mature and currently the trendiest AI field, simply because the amount of data is there, the diversity is there, and the algorithms are getting better and better. It’s no longer state-of-the-art, where it’s difficult to grasp, adopt, and bring into production. So, now the main goal is moving into production and monetizing these types of data sources. 

Read the White Paper on

Video Analytics and Surveillance 

When you talk about video analytics or surveillance, or any kind of quality assurance, the main problem is improving on or detecting human errors, behaviors, and environments. Telemetry plays a huge role here, and there are many complements and constraints to consider in this environment. 

That makes it hardware-dependent and also requires AI at the edge, where most of the algorithms and decisions need to happen. If you want to detect fire, detect fraud or prevent certain types of failure, such as quality failure or human failure — time is extremely important. 

As HPE Pointnext Services, we have been working on our own solution and reference architectures to approach those problems because of the complexity of the environment, the different cameras, and hardware handling the data acquisition process. Even at the beginning it’s enormous and very diverse. There is no one-size-fits-all. There is no one provider or one solution that can handle surveillance use cases or broad analytical use cases at the manufacturing plant or oil and gas rig where you are trying to detect fire or oil and gas spills from the different environments. So being able to approach it holistically, to choose the right solution for the right complement, and design the architecture is key. 

Also, it’s essential to have the right hardware and edge devices to acquire the data and handle the telemetry. Let’s say when you are positioning cameras in an outside environment and you have different temperatures, vibrations, and heat. This will reflect on the quality of the acquired information going through the pipeline. 

Some of the benefits in use cases using computer vision and video surveillance include real time information coming from manufacturing plants, knowing that all the safety and security standards there are met, and that the people operating are following the instructions and have the safeguards required for a specific manufacturing plant is also extremely important.

When you have a quality assurance use case, video analytics is one source of information to tackle the problem. For example, improving the quality of your products or batches is just one source in the computer vision field. Having the right architecture, being agile and flexible, and finding the right solution for the problem and the right models deployed at the right edge device — or at the right camera — is something we are doing right now. We have several partners working to solve the challenges of video analytics use cases.

Gardner: When you have a high-scaling, high-speed AI to analyze video, it’s no longer a gating factor that you need to have humans reviewing the processes. It allows video to be used in so many more applications, even augmented reality, so that you are using video on both ends of the equation, as it were. Are we seeing an explosion of applications and use cases for video analytics and AI, Iveta?

Lohovska: Yes, absolutely. The impact of algorithms in this space is enormous. Also, all the open source datasets, such as ImageNet and ResNet, allow a huge amount of data to train any kind of algorithms on those open source datasets. You can adjust them and pre-train them for your own use cases, whether it’s healthcare, manufacturing, or video surveillance. It’s very enabling. 

You can see the diversity of the solutions people are developing and the different programs they are tackling using computer vision capabilities, not only from the algorithms, but also from the hardware side, because the cameras are getting more and more powerful. 

Currently, we are working on several projects in the non-visible human spectrum. This is enabled by the further development of the hardware acquiring those images that we can’t see.

Gardner: If we can view and analyze machines and processes, perhaps we can also listen and talk to them. Tell us about speech and natural language processing (NLP), Iveta. How is AI enabling those businesses and how they transform themselves?

Speech-to-text to protect

Lohovska: This is another strong field for how AI is used and still improving. It’s not as mature as computer vision, simply because the complexity of human language and speech, and the way speech gets recorded and transferred. It’s a bit more complex, so it’s not only a problem of technologies and people writing algorithms, but also linguists being able to combine the grammar problems and write the right equation to solve those grammar problems. 

Read the White Paper on

Speech and Natural Language Processing 

But one very interesting field in the speech and NLP area is speech-to-text, so basically being able to transcribe speech into text. It’s very helpful for emergency organizations handling emergency calls or fraud detection, where you need, in real time, to detect fraud or danger. If someone is in danger, it’s a very common use case for law enforcement or for security organizations or for simply improving the quality of your service for call centers. 

This example is industry- or vertical-independent. You can have finance, manufacturing, retail — but all of them have some kind of customer support. This is the most common use case, being able to record and improve the quality of your services, based on the analysis you can apply. Similar to the video analytics use case, the problem here, too, is handling the complexity of different algorithms, different languages, and the varying quality of the recordings.

A reference architecture, where you have the different components designed on exactly this holistic approach, allows the user to explore, evolve, and experiment in this space. We choose the right complement for the right problem and how to approach it. 

And in this case, if we combine the right data science tool with the right processing tool and the right algorithms on top of it, then you can simply design the solution and solve the specific problem. 

Gardner: Our next and last use case for AI is one people are probably very familiar with, and that’s the autonomous driving technology (ADT).

Andy, how are we developing highly automated-driving infrastructures that leverage AI and help us get to that potential nirvana of truly self-driving and autonomous vehicles?

Data processing drives vehicles 

Longworth: There are several problems around highly autonomous driving as we have seen. It’s taking years to get to the point where we have fully autonomous cars and there are clear advantages to it. 

If you look at, for example, what the World Health Organization (WHO) says, there are more than 1 million deaths per year in road traffic accidents. One of the primary drivers for ADT is that we can reduce the human error in cars on the road — and reduce the number of fatalities and accidents. But to get to that point we need to train these immensely complex AI algorithms that take massive amounts of data from the car.

Just purely from the sensor point of view, we have high-definition cameras giving 360-degree views around the car. You have radar, GPS, audio, and vision systems. Some manufacturers use light detection and ranging (LIDAR), some not. But you have all of these sensors giving massive amounts of data. And to develop those autonomous cars, you need to be able to process all of that raw data. 

Read the White Paper on

Development of Self-Driving Infrastrcuture 

Typically, in an eight-hour shift, an ADT car generates somewhere between 70 and 100 terabytes of data. If you have an entire fleet of cars, then you need to be able to very quickly get that data off of the car so that you can get them back out on the road as quickly as possible. Then you need to get that data from where you offload it into the data center so that the developers, data scientists, analysts, and engineers can build to the next iteration of the autonomous driving strategy. 

When you have built that, tested it, and done all the good things that you need to do, you need to next be able to get those models and that strategy from the developers back into the cars again. It’s like the other AI problems that we have been talking about, but on steroids because of the sheer volume of data and because of the impact of what happens if something should go wrong. 

At HPE Pointnext Services, we h
ave developed a set of solutions that address several of the pain points in the ADT development process. First is the ingest; how can we use HPE Edgeline processing in the car to pre-process data and reduce the amount of data that you have to send back to the data center. Also, you have to send back the most important data after the eight-hour drive first, and then send the run-of-the-mill, backup data later. 

At HPE Pointnext Services, we have developed a set of solutions that address several of the pain points in the ADT development process. 

The second piece is the data platform itself, building a massive data platform that is extensible to store all the data coming from the autonomous driving test fleet. That needs to also expand as the fleet grows as well as to support different use cases. 

The data platform and the development platform are not only massive in terms of the amount of data that it needs to hold and process, but also in terms of the required tooling. We have been developing reference architectures to enable automotive manufacturers, along with the suppliers of those automotive systems, to build their data platforms and provide all the processing that they need so their data scientists can continuously develop autonomous driving strategies and be able to test them in a highly automated way, while also giving access to the data to the additional suppliers. 

For example, the sensor suppliers need to see what’s happening to their sensors while they are on the car. The platform that we have been putting together is really concerned with having the flexibility for those different use cases, the scalability to be able to support the data volumes of today, but also to grow — to be able to have the data volumes of the not-too-distant future.

The platform also supports the speed and data locality, so being able to provide high-speed parallel file systems, for example, to feed those ML development systems and help them train the models that they have.

So all of this pulls together the different components we have talked about with the different use cases, but at a scale that is much larger than several of the other use cases, probably put together.

Gardner: It strikes me that the ADT problem, if solved, enables so many other major opportunities. We are talking about micro-data centers that provide high-performance compute (HPC) at the edge. We are talking about the right hybrid approach to the data management problem — what to move, what to keep local, how to then have a lifecycle approach to. So, ADT is really a key use-case scenario. 

Why is HPE uniquely positioned to solve ADT that will then lead to so many enabling technologies for other applications?

Longworth: Like you said, the micro-data center — every autonomous driving car essentially becomes a data center on wheels. So being able to provide that compute at the edge to enable the processing of all that sensor data.

If you look at the HPE portfolio of products, there are very few organizations that have edge compute solutions and the required processing power in such small packages. But it’s also about being able to wrap it up in, not only the hardware, but the solution on top, the support, and being able to provide a flexible delivery model.

Lots of organizations want to have a cloud-like experience, not just from the way they consume the technology, but also in the way they pay for the technology. So, by HPE providing everything as-a-service allows being able to pay for it all, as you use it, for your autonomous driving platform. Again, there are very few organizations in the world that can offer that end-to-end value proposition.

Collaborate and corroborate 

Gardner: Iveta, why does it take a team-sport and solution-approach from the data science perspective to tackle these major use cases?

They can attack the complexity of those use cases from each side because it requires not just data science and the hardware but a lot of domain-specific expertise to solve those problems, too. 

Lohovska: I agree with Andy. The way we approach those complex use cases and the fact that you can have them as a service — and not only infrastructure-as-a-service (IaaS) or data-as-a-service (DaaS) — but working on AI and modeling-as-a-service (MaaS). You can have a marketplace for models and being able to plug-and-play different technologies, experiment, and rapidly deploy them allows you to rapidly get value out of those technologies. That is something we are doing on a daily basis with amazing experts and people with the knowledge of the different layers. They can then attack the complexity of those use cases from each side, because it requires not just data science and the hardware, but a lot of domain-specific expertise to solve those problems. This is something we are looking at and we are doing in-house. 

And I am extremely happy to say that I have the pleasure to work with all of those amazing people and experts within HPE.

Gardner: And there is a great deal more information available on each of these use cases for AI. There are white papers on the HPE website in Pointnext Services. 

What else can people do, Andy, to get ready for these high-level AI use cases that lead to digital business transformation? How should organizations be setting themselves up on a people, process, and technology basis to become adept at AI as a core competency? 

Longworth: It is about people, technology, process, and all these things combined. You don’t go and buy AI in a box. You need a structured approach. You need to understand what the use cases are that give value to your organization and to be able to quickly prototype those, quickly experiment with them, and prove the value to your stakeholders. 

Where a lot of organizations get stuck is moving from that prototyping, proof of concept (POC), and proof of value (POV) phase into full production. It is tough getting the processes and pipelines that enable you to transition from that small POV phase into a full production environment. If you can crack that nut, then the next use-cases that you implement, and the next business problems that you want to solve with AI, become infinitely easier. It is a hard step to go from POV through to the full production because there are so many bits involved. 

You have that whole value chain from grabbing hold of the data at the point of creation, processing that data, making sure you have the right people and process around that. And when you come out with an AI solution that gives some form of inference, it gives you some form of answer, you need to be able to act upon that answer.

You can have the best AI solution in the world that will give you the best predictions, but if you don’t build those predictions into your business processes, you may well have never made them in the first place.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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As 2020 ushers in a new decade, the forces shaping data center decisions are extending compute resources to new places. 

With the challenging goals of speed, agility, and efficiency, enterprises and service providers alike will be seeking new balance between the need for low latency and optimal utilization of workload placement. Hybrid models will therefore include more distributed, confined, and modular data centers at or near the edge.

These are but some of a few top-line predictions on the future state of the modern data center design. The next BriefingsDirect data center strategies discussion with two leading IT and critical infrastructure executives examines how these new data center variations nonetheless must also interoperate seamlessly from core to cloud to edge. 

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

Here to help us learn more about the new state of extensible data centers is Peter Panfil, Vice President of Global Power at VertivTM, and Steve Madara, Vice President of Global Thermal at Vertiv. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: The world is rapidly changing in 2020. Organizations are moving past the debate around hybrid deployments, from on-premises to public clouds. Why do we need to also think about IT architectures and hybrid computing differently?

Panfil: We noticed a trend at Vertiv in our customer base. That trend is toward a new generation of data centers. We have been living with distributed IT, client-server data centers moving to cloud, either a public cloud or a private cloud.

But what we are seeing is the evolution of an edge-to-core, near-real-time data center generation. And it’s being driven by devices everywhere, the “connected-all-the-time” model that all of us seem to be going to. 

And so, when you are in a near-real-time world, you have to have infrastructure that supports your near-real-time applications. And that is what the technology folks are facing. I refer to it as a pack of dogs chasing them — the amount of data that’s being generated, the applications running remotely, and the demand for availability, low latency, and driving cost down as much as you possibly can. This is what’s changing how they approach their critical infrastructure space.

Gardner: And so, a new equilibrium is emerging. How is this different from the past?

Madara: If we go back 20 years, everything was centralized at enterprise data centers. Then we decided to move to decentralized, and then back to centralized. We saw a move to colocation as people decided that’s where they could get lower cost to run their apps. And then things went to the cloud, as Peter said earlier.

And now, we have a huge number of devices connected locally. Cisco says by late 2020 that it’s going to have 23 billion connected devices, and over half of those are going to be machine-to-machine communications, which, as Peter mentioned earlier, the latency is going to be very, very critical.

An interesting read is Michael Lewis’s book Flash Boys about the arbitrage that’s taking place with the low latency that you have in stock market trading. I think we are going to see more of that moving to the edge. The edge is more like a smart rack or smart row deployment in an existing facility. It’s going to be multi-tenant, because it’s going to be able to be throughout large cities. There could be 20 or 30 of these edge data center sites hosting different applications for customers.

This move to the edge is also going to provide IT resources in a lot of underserved markets that don’t yet have pervasive compute, especially in emerging countries

Gardner: Why is speed so important? We have been talking about this now for years, but it seems like the need for speed to market and speed to value continues to ramp up. What’s driving that?

Moving to the edge, with momentum 

Panfil: There is more than one kind of speed. There is speed of response of the application, that’s something that all of us demand — speed of response of the applications. I have to have low latency in the transactions I am performing with my data or with my applications. So there is the speed of the actual data being transmitted. 

There is also speed of deployment. When Steve talked earlier about centralized cloud deployments in these core data centers, your data might be going over a significant distance, hopping along the way. Well, if you can’t live with that latency that gets inserted, then you have to take the IT application and put it closer to the source and consumer of the data. So there is a speed of deployment, from core to edge, that happens.

And the third type of speed is you have to have low-first-cost, high-asset-utilization, and rapid-scalability. So that’s a speed of infrastructure adaptation to what the demands for the IT applications are.

So when we mean speed, I often say it’s speed, speed, and speed. First it’s the data speed, then deploying fast, and then at scale at business-friendly cost and reliability. 

So when we mean speed, I often say it’s speed, speed, and speed. First, it’s the data IT. Once I have data IT speed, how did I achieve that? l did it by deploying fast, in the scale needed for the applications, and lastly at a cost and reliability that makes it tolerable for the businesses.

Gardner: So I guess it’s speed-cubed, right?

Panfil: At least, speed-cubed. Steve, if we had a nickel for every time one of our customers said “speed,” we wouldn’t have to work anymore. They are consumed with the different speeds that they have to deal with — and it’s really the demands of their customers.

Gardner: Vertiv for years has been looking at the data center of the future and making some predictions around what to expect. You have been rather prescient. To continue, you have now identified several areas for 2020, too. Let’s go through those trends. 

Steve, Vertiv predicts that “hybrid architectures will go mainstream.” Why did you identify that, and what do you mean?

The future goes hybrid 

Madara: If we look at the history of going from centralized to decentralized, and going to colocation and cloud applications, it shows the ongoing evolution of Internet of Things (IoT) sensors, 5G networks, smart cities, autonomous cars, and how more and more of that data is generated and will need to be processed locally. A lot of that is from machine-to-machine applications. 

So when we now talk about hybrid, we have to get very, very close to the source, as far as the processing is concerned. That’s going to be a large-scale evolution that’s going to drive the need for hybrid applications. There is going to be processing at the edge as well as centralized applications — whether it’s in a cloud or hosted in colocation-based applications.

Panfil: Steve, you and I both came up through the ranks. I remember when the data closet down the hall was basically a communications matrix. Its intent was to get communications from wherever we were to wherever our core data center was.

Well, the cloud is not going away. Number two, enterprise IT is not going away. What the enterprise is saying is, “Okay, I am going to take my secret sauce and I am going to put it in an edge data center. I am going to put the compute power as close to my consumer of that data and that application as I possibly can. And then I am going to figure out where the rest of it’s going to go.”

If I can live with the latency I get out of a core data center, I am going to stay in the cloud. If I can’t, I might even break up my enterprise data center into small or micro data centers that give me even better responses.

“If I can live with the latency I get out of a core data center, I am going to stay in the cloud. If I can’t, I might even break up my enterprise data center into small or micro data centers that give me even better responses.”

Dana, it’s interesting, there was a recent wholesale market summary published that said the difference between the smaller and the larger wholesale deals widened. So what that says is the large wholesale deals are getting bigger, the small wholesale deals are getting smaller, and that the enterprise-based demand, in deployments under 600 kilowatts, is focused on low-latency and multi-cloud access. 

That tells us that our customers, the users of that critical space, are trying to place their IT appliances as close as they can to their customers, eliminating the latency, responding with speed, and then figuring out how to mesh that edge deployment with their core strategy.

Gardner: Our second trend gets back to the speed-cubed notion. I have heard people describe this as a new arms race, because while it might be difficult to differentiate yourself when everyone is using the same public cloud services, you can really differentiate yourself on how well you can conduct yourself at speed.

What kinds of capabilities across your technologies will make differentiation around speed work to an advantage as a company?

The need for speed 

Panfil: Well, I was with an analyst recently, and I said the new reality is not that the big will eat the small — it’s that the fast will eat the slow. And any advantage that you can get in speed of applications, speed of deployment, deploying those IT assets — or morphing the data center infrastructure or critical space infrastructure – helps improve capital efficiency. What many customers tell us is that they have to shorten the period of time between deciding to spend money on IT assets and the time that those asset start creating revenue.

They want help being creative in lowering their first-cost, in increasing asset utilization, and in maintaining reliability. If, holy cow, my application goes down, I am out of business. And then they want to figure out how to manage things like supply chains and forecasting, which is difficult to do in this market, and to help them be as responsive as they can to their customers.

Madara: Forecasting and understanding the new applications — whether it’s artificial intelligence (AI) or 5G — the CIOs need to decide where they need to put those applications whether they should be in the cloud or at the edge. Technology is changing so fast that nobody can predict far out into the future as far as to where I will need that capacity and what type of capacity I will need.

So, it comes down to being able to put that capacity in the place where I need it, right when I need it, and not too far in advance. Again, I don’t want to spend the capital, because I may put it in the wrong place. So it’s got to be about tying the demand with the supply, and that’s what’s key as far as the infrastructure.

And the other element I see is technology is changing fast, even on the infrastructure side. For our equipment, we are constantly making improvements every day, making it more efficient, lower cost, and with more capability. And if you put capacity in today that you don’t need for a year or two down the road, you are not taking advantage of the latest, greatest technology. So really it’s coupling the demand to the actual supply of the infrastructure — and that’s what’s key.

Another consideration is that many of these large companies, especially in the colocation market, have their financial structure as a real estate investment trust (REIT). As a result, they need to tie revenue with expenses tighter and tighter, along with capital spending.

Panfil: That’s a good point, Steve. We redesigned our entire large power portfolio at Vertiv specifically to be able to address this demand. 

In previous generations, for example, the uninterruptible power supply (UPS) was built as a complete UPS. The new generation is built as a power converter, plus an I/O section, plus an interface section that can be rapidly configured to the customer, or, in some cases, put into a vendor-managed inventory program. This approach allows us to respond to the market and customers quicker.

We were forced to change our business model in such a way that we can respond in real time to these kinds of capacity-demand changes.

Madara: And to add to that, we have to put together more and more modules and solutions where we are bundling the equipment to deliver it faster, so that you don’t have to do testing on site or assembly on site. Again, we are putting together solutions that help the end-user address the speed of the construction of the infrastructure. 

I also think that this ties into the relationship that the person who owns the infrastructure has with their supplier base. Those relationships have to build in, as Peter mentioned earlier, the ability to do stocking of inventory, of having parts available on-site to go fast. 

Gardner: In summary so far, we have this need for speed across multiple dimensions. We are looking at more hybrid architectures, up and down the scale — from edge to core, on-premises to the cloud. And we are also looking at crunching more data and making real-time analytics part of that speed advantage. That means being able to have intelligence brought to bear on our business decisions and making that as fast as possible.

So what’s going on now with the analytics efficiency trend? Even if average rack density remains static due to a lack of space, how will such IT developments as high performance computing (HPC) help make this analysis equation work to the business outcome’s advantage? 

High-performance, high-density pods 

Madara: The development of AI applications, machine learning (ML), and what could be called deep learning are evolving. Many applications are requiring these HPC systems. We see this in the areas of defense, gaming, the banking industry, and people doing advanced analytics and tying it to a lot of the sensor data we talked about for manufacturing. 

It’s not yet widespread, it’s not across the whole enterprise or the entire data center, and these are often unique applications. What I hear in large data centers, especially from the banks, is that they will need to put these AI applications up on 30-, 40-, 50- or 60-kW racks — but they only have three or four of these racks in the whole data center.

The end-user will need to decide how to tune or adjust facilities to accommodate these small but growing pods of high-density compute. They are going to need to decide how they are going to facilitize for that type of equipment.

The end-user will need to decide how to tune or adjust facilities to accommodate these small but growing pods of high-density compute. And if they are in their own facility, if it’s an enterprise that has its own data center, they will need to decide how they are going to facilitize for that type of equipment. 

A lot of the colocation hosting facilities have customers saying, “Hey, I am going to be bringing in the future a couple of racks that are very high density. A lot of these multi-tenant data centers are saying, ‘Oh, how do I provision for these, because my data center was laid out for this average of maybe 8 kW per rack? How do I manage that, especially for data centers that didn’t previously have chilled water to provide liquid to the rack?’”

We are now seeing a need to provide chilled water cooling that would go to a rear door heat exchanger on the back of the rack. It could be chilled water that would go to a rack for chip cooling applications. And again, it’s not the whole data center; it’s a small segment of the data center. But it raises questions of how I do that without overkill on the infrastructure needed. 

Gardner: Steve, do you expect those small pods of HPC in the data center to make their way out to the edge when people do more data crunching for the low-latency requirements, where you can’t move the data to a data center? Do you expect to have this trend grow more distributed? 

Madara: Yes, I expect this will be for more than the enterprise data center and cloud data centers. I think you are going to see analytics applications developed that are going to be out at the edge because of the requirements for latency. 

When you think about the autonomous car; none of us know what’s going to be required there for that high-performance processing, but I would expect there is going to be a need for that down at the edge. 

Gardner: Peter, looking at the power side of things when we look at the batteries that help UPS and systems remain mission-critical regardless of external factors, what’s going on with battery technology? How will we be using batteries differently in the modern data center? 

Battery-powered savings 

Panfil: That’s a great question. Battery technology has been evolving at an incredibly fast rate. It’s being driven by the electric vehicles. That growth is bringing to the market batteries that have a size and weight advantage. You can’t put a big, heavy pack of batteries in a car and hope to have it perform well. 

It also gives a long-life expectation. So data centers used to have to decide between long-life, high-maintenance, wet cells and the shorter-life, high-maintenance, valve-regulated lead-acid (VRLA) batteries. In step with the lithium-ion batteries (LIBs) and thin plate pure lead (TPPL) batteries, what’s happened is the total cost of ownership (TCO) has started to become very advantageous for these batteries. 

Our sales leadership lead sent me the most recent TCO between either TPPL or LIBs versus traditional VRLA batteries, and the TCO is a winner for the LIBs and the TPPL batteries. In some cases, over a 10-year period, the TCO is a factor of two lower for LIB and TPPL.

Where in the cloud generation of data centers was all about lowest first cost, in this edge-to-core mentality of data centers, it’s about TCO. There are other levers that they can start to play with, too. 

So, for example, they have life cycle and operating temperature variables. That used to be a real limitation. Nobody in the data center wanted their systems to go on batteries. They tried everything they could to not have their systems go on the battery because of the potential for shortening the life of their batteries or causing an outage. 

Today we are developing IT systems infrastructure that takes advantage of not only LIBs, but also pure lead batteries that can increase the number of [discharge/recharge] cycles. Once you increase the number of cycles, you can think about deploying smart power configurations. That means using batteries not only in the critical infrastructure for a very short period of time when the power grid utility fails, but to use that in critical infrastructure to help offset cost.

If I can reduce utility use at peak demand periods, for example, or I can reduce stress on the grid at specified times, then batteries are not only a reliability play – they are also a revenue-offset play. And so, we’re seeing more folks talking to us about how they can apply these new energy storage technologies to change the way they think about using their critical space.

Also, folks used to think that the longer the battery time, the better off they were because it gave more time to react to issues. Now, folks know what they are doing, they are going with runtimes that are tuned to their operations team’s capabilities. So, if my operations team can do a hot swap over an IT application — either to a backup critical space application or to a redundant data center — then all of a sudden, I don’t need 5 to 12 minutes of runtime, I just need the bridge time. I might only need 60 to 120 seconds.

Now, if I can have these battery times tuned to the operations’ capabilities — and I can use the batteries more often or in higher temperature applications — then I can really start to impact my TCO and make it very, very cost-effective.

Gardner: It’s interesting; there is almost a power analog to hybrid computing. We can either go to the cloud or the grid, or we can go to on-premises or the battery. Then we can start to mix and match intelligently. That’s really exciting. How does lessening dependence on the grid impact issues such as sustainability and conserving energy?

Sustainability surges forward 

Panfil: We are having such conversations with our key accounts virtually every day. What they are saying is, “I am eventually not going to make smoke and steam. I want to limit the number of times my system goes on a generator. So, I might put in more batteries, more LIBs or TPPL batteries, in certain applications because if my TCO is half the amount of the old way, I could potentially put in twice as much, and have the same cost basis and get that economic benefit.”

And so from a sustainability perspective, they are saying, “Okay, I might need at some point in the useful life of that critical space to not draw what I think I need to draw from my utility. I can limit the amount of power I draw from that utility.”

I love all of you out there in data center design, but most of them are designed for peak useage. These changes allow them to design more for the norm of the requirements. That means they can put in less infrastructure, less battery, to right-size their generators; same thing on cooling. 

This is not a criticism, I love all of you out there in data center design, but most of them are designed for peak usage. So what these changes allow them to do is to design more for the norm of the requirements. That means they can put in less infrastructure, the potential to put in less battery. They have the potential to right-size their generators; same thing on the cooling side, to right-size the cooling to what they need and not for the extremes of what that data center is going to see.

From a sustainability perspective, we used to talk about the glass as half-full or half-empty. Now, we say there is too much of a glass. Let’s right-size the glass itself, and then all of the other things you have to do in support of that infrastructure are reduced.

Madara: As we look at the edge applications, many will not have backup generators. We will have alternate energy sources, and we will probably be taking more hits to the batteries. Is the LIB the better solution for that?

Panfil: Yes, Steve, it sure is. We will see customers with an expectation of sustainability, a path to an energy source that is not fossil fuel-based. That could be a renewable energy source. We might not be able to deploy that today, but they can now deploy what I call foundational technologies that allow them to take advantage of it. If I can have a LIB, for example, that stores excess energy and allows me to absorb energy when I’m creating more than I need — then I can consume that energy on the other side. It’s better for everybody.

Gardner: We are entering an era where we have the agility to optimize utilization and reduce our total costs. The thing is that it varies from region to region. There are some areas where compliance is a top requirement. There are others where energy issues are a top requirement because of cost.

What’s going on in terms of global cross-pollination? Are we seeing different markets react to their power and thermal needs in different ways? How can we learn from that?

Global differences, normalized 

Madara: If you look at the size of data centers around the world, the data centers in the U.S. are generally much larger than in Europe. And what’s in Europe is much larger than what we have in other developed countries. So, there are a couple of things, as you mentioned, energy availability, cost of energy, the size of the market and the users that it serves. We may be looking at more edge data centers in very underserved markets that have been in underdeveloped countries.

So, you are going to see the size of the data center and the technology used potentially different to better fit needs of the specific markets and applications. Across the globe, certain regions will have different requirements with regard to security and sustainability.

Even though we have these potential differences, we can meet the end-user needs to right-size the IT resources in that region. We are all more common than we are different in many respects. We all have needs for security, we all have needs for efficiency, it may just be to different degrees.

Panfil: There are different regional agency requirements, different governmental regulations that companies have to comply with. And so what we find, Dana, is that what our customers are trying to do is normalize their designs. I won’t say they are standardizing their design because standardization says I am going to deploy exactly the same way everywhere in the world. I am a fan of Kit Kats and Kit Kats are not the same globally, they vary by region, the same is true for data centers.

So, when you look at how the customers are trying to deal with the regional and agency differences that they have to live with, what they find themselves doing is trying to normalize their designs as much as they possibly can globally, realizing that they might not to be able to use exactly the same power configuration or exactly the same thermal configuration. But we also see pockets where different technologies are moving to the forefront. For example, China has data centers that are running at high voltage DC, 240 volts DC, we have always had 48-volt DC IT applications in the Americas and in Europe. Customers are looking at three things — speed, speed, and speed.

And so when we look at the application, for example, of DC, there used to be a debate, is it AC or DC? Well, it’s not an “or” it’s an “and.” Most of the customers we talk to, f
or example, in Asia are deploying high-voltage DC and have some form of hybrid AC plus DC deployment. They are doing it so that they can speed their applications deployments.

In the Americas, the Open Compute Project (OCP) deploys either 12 or 48 volts to the rack. I look at it very simply. We have been seeing a move from 2N architecture to N plus 1 architecture in the power world for a decade, this is nothing more than adopting the N plus 1 architecture at the rack level versus the 2N architecture at the rack level.

And so what we see is when folks are trying to, number one, increase the speed; number two, increase their utilization; number three, lower their total cost, they are going to deploy infrastructures that are most advantageous for either the IT appliances that they are deploying or for the IT applications that they are running, and it’s not the same for everybody, right Steve? 

You and I have been around the planet way too many times, you are a million miler, so am I. It’s amazing how a city might be completely different in a different time zone, but once you walk into that data center, you see how very consistent they have gotten, even though they have done it completely independently from anybody else.

Madara: Correct!

Consistency lowers costs and risks 

Gardner: A lot of what we have talked about boils down to a need to preserve speed-to-value while managing total cost of utilization. What is there about these multiple trends that people can consider when it comes to getting the right balance, the right equilibrium, between TCO and that all important speed-to-value?

Madara: Everybody strives to drive cost down. The more you can drive the cost down of the infrastructure, the more you can do to develop more edge applications.

I think we are seeing a very large rate of change of driving cost down. Yet we still have a lot of stranded capacity out there in the marketplace. And people are making decisions to take that down without impacting risk, but I think they can do it faster.

Standardization helps drive speed, whether it’s normalization or similarity. What allows people to move fast is to repeat what they are doing instead of snowflake data centers, where every one is different. 

Peter mentioned standardization. Standardization helps drive speed, whether it’s normalization or similarity. What allows people to move fast is to repeat what they are doing instead of snowflake data centers, where every new one is different.

Repeating allows you to build a supply base ecosystem where everybody has the same goal, knows what to do, and can be partners in driving out cost and in driving speed. Those are some of the key elements as we go forward.

Gardner: Peter when we look to that standardization, you also allow for more seamless communication from core to cloud to edge. Why is that important, and how can we better add intelligence and seamless communication among and between all these different distributed data centers?

Panfil: When we normalize designs globally, we take a look at the regional differences, sort out what the regional differences have to be, and then put a proof of concept deployment. And out of that comes a consistent method of procedure.

When we talk about managing the data center effectively and efficiently, first of all, you have to know what you have. And second, you have to know what it’s doing. And so, we are seeing more folks normalizing their designs and getting consistency. They can then start looking at how much of their available capacity from a design perspective they are actually using both on a normal basis and on a peak basis and then they can determine how much of that they are willing to use.

We have some customers who are very risk-averse. They stay in the 2N world, which is a 50 percent maximum utilization. We applaud them for it because they are not going to miss a transaction.

There are others who will say, “I can live with the availability that an N+1 architecture gives me. I know I am going to have to be prepared for more failures. I am going to have to figure out how to mitigate those failures.”

So they are working constantly at figuring out how to monitor what they have and figure out what the equipment is doing, and how they can best optimize the performance. We talked earlier about battery runtimes, for example. Sometimes they might get short or sometimes they might be long.

As these companies get into this step and repeat function, they are going to get consistency of their methods of procedure. They’re going to get consistency of how their operations teams run their physical infrastructure. They are going to think about running their equipment in ways that is nontraditional today but will become the norm in the next generation of data centers. And then they are going to look at us and say, “Okay, now that I have normalized my design, can I use rapid deployment configuration? Can I put it on a skid, in a container? Can I drop it in place as the complete data center?”

Well, we build it one piece of equipment at a time and stitch it all together. The question that you asked about monitoring, it’s interesting because we talked to a major company just last month. Steve and I were visiting them at their site. And they said, “You know what? We spend an awful lot of time figuring out how our building management system and our data exchange happens at the site. Could Vertiv do some of that in the factory? Could you configure our data acquisition systems? Could you test them there in the factory? Could we know that when the stuff shows up on site that it’s doing the things that it’s supposed to be doing instead of us playing hunt and peck to figure out what the issues are?”

We said, “Of course.” So we are adding that capability now into our factory testing environment. What we see is a move up the evolutionary scale. Instead of buying separate boxes, we are seeing them buying solutions — and those solutions include both monitoring and controls.

Steve didn’t even get a chance to mention the industry-leading Vertiv Liebert® iCOM control for thermal. These controls and monitoring systems allow them to increase their utilization rates because they know what they have and what it’s doing.

Gardner: It certainly seems to me, with all that we have said today, that the data center status quo just can’t stand. Change and improvement is inevitable. Let’s close out with your thoughts on why people shouldn’t be standing still; why it’s just not acceptable.

Innovation is inevitable 

Madara: At the end of the day, the IT world is changing rapidly every day. Whether in the cloud or down at the edge, the IT world needs to adjust to those needs. They need to be able to be cut out enough of the cost structure. There is always a demand to drive cost down.

If we don’t change with the world around us, if we don’t meet the requirements of our customers, things aren’t going to work out – and somebody else is going to take it and go for it.

Panfil: Remember, it’s not the big that eats the small, it’s the fast that eats the slow.

Madara: Yes, right.

Panfil: And so, what I have been telling folks is, you got to go. The technology is there. The technology is there for you to cut your cost, improve your speed, and increase utilization. Let’s do it. Otherwise, somebody else is going to do it for you.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Vertiv.

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The next edition of the BriefingsDirect Voice of the Innovator podcast series explores the latest developments in hybrid IT management.

IT operators have for decades been playing catch-up to managing their systems amid successive waves of heterogeneity, complexity, and changing deployment models. IT management technologies and methods have evolved right along with the challenge, culminating in the capability to optimize and automate workloads to exacting performance and cost requirements.

But now automation is about to give an AIOps boost from new machine learning (ML) and artificial intelligence (AI) capabilities — just as multicloud and edge computing deployments become more common — and demanding.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

Stay with us as we explore the past, present, and future of IT management innovation with a 30-year veteran of IT management, Doug de Werd, Senior Product Manager for Infrastructure Management at Hewlett Packard Enterprise (HPE). The interview is conducted byDana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Management in enterprise IT has for me been about taking heterogeneity and taming it, bringing varied and dynamic systems to a place where people can operate over more, using less. And that’s been a 30-year journey.

Yet heterogeneity these days, Doug, includes so much more than it used to. We’re not just talking about platforms and frameworks – we’re talking about hybrid cloud, multicloud, and many Software as a service (SaaS) applications. It includes working securely across organizational boundaries with partners and integrating business processes in ways that never have happened before. 

With all of that new complexity, with an emphasis on intelligent automation, where do you see IT management going next?

Managing management 

de Werd: Heterogeneity is known by another term, and that’s chaos. In trying to move from the traditional silos and tools to more agile, flexible things, IT management is all about your applications — human resources and finance, for example – that run the core of your business. There’s also software development and other internal things. The models for those can be very different and trying to do that in a single manner is difficult because you have widely varying endpoints.

Gardner: Sounds like we are now about managing the management.

de Werd: Exactly. Trying to figure out how to do that in an efficient and economically feasible way is a big challenge.

Gardner: I have been watching the IT management space for 20-plus years and every time you think you get to the point where you have managed everything that needs to be managed — something new comes along. It’s a continuous journey and process.

But now we are bringing intelligence and automation to the problem. Will we ever get to the point where management becomes subsumed or invisible?

de Werd: You can automate tasks, but you can’t automate people. And you can’t automate internal politics and budgets and things like that. What you do is automate to provide flexibility.

How to Support DevOps, Automation,

And IT Management Initiatives 

But it’s not just the technology, it’s the economics and it’s the people. By putting that all together, it becomes a balancing act to make sure you have the right people in the right places in the right organizations. You can automate, but it’s still within the context of that broader picture. 

Gardner: When it comes to IT management, you need a common framework. For HPE, HPE OneView has been core. Where does HPE OneView go from here? How should people think about the technology of management that also helps with those political and economic issues?

de Werd: HPE OneView is just an outstanding core infrastructure management solution, but it’s kind of like a car. You can have a great engine, but you still have to have all the other pieces. 

And so part of what we are trying to do with HPE OneView, and we have been very successful, is extending that capability out into other tools that people use. This can be into more traditional tools like with our Microsoft or VMware partnerships and exposingand bringing HPE OneView functionality into traditional things. 

The integration allows the confidence of using HPE OneView as a core engine. All those other pieces can still be customized to do what you need to do — yet you still have that underlying core foundation of HPE OneView.

But it also has a lot to do with DevOps and the continuous integration development types of things with Docker, Chef, and Puppet — the whole slew of at least 30 partners we have. 

That integration allows the confidence of using HPE OneView as a core engine. All those other pieces can still be customized to do what you need to do — yet you still have that underlying core foundation of HPE OneView.

Gardner: And now with HPE increasingly going to an as-a-service orientation across many products, how does management-as-a-servicework?

Creativity in the cloud 

de Werd: It’s an interesting question, because part of management in the traditional sense — where you have a data center full of servers with fault management or break/fix such as a hard-drive failure detection – is you want to be close, you want to have that notification immediately. 

As you start going up in the cloud with deployments, you have connectivity issues, you have latency issues, so it becomes a little bit trickier. When you have more up levels, up the stack, where you have software that can be more flexible — you can do more coordination. Then the cloud makes a lot of sense.  

Management in the cloud can mean a lot of things. If it’s the infrastructure, you tend to want to be closer to the infrastructure, but not exclusively. So, there’s a lot of room for creativity.

Gardner: Speaking of creativity, how do you see people innovating both within HPE and within your installed base of users? How do people innovate with management now that it’s both on- and off-premises? It seems to me that there is an awful lot you could do with management beyond red-light, green-light, and seek out those optimization and efficiency goals. Where is the innovation happening now with IT management?

de Werd: The foundation of it begins with automation, because if you can automate you become repeatable, consistent, and reliable, and those are all good in your data center.

Transform Compute, Storage, and Networking

Into Software-Defied Infrastructure 

You can free up your IT staff to do other things. The truth is if you can do that reliably, you can spend more time innovating and looking at your problems from a different angle. You gain the confidence that the automation is giving you. 

Automation drives creativity in a lot of d
ifferent ways. You can be faster to market, have quicker releases, those types of things. I think automation is the key.

Gardner: Any examples? I know sometimes you can’t name customers, but can you think of instances where people are innovating with management in ways that would illustrate its potential?

Automation innovation 

de Werd: There’s a large biotech genome sequencing company, an IT group that is very innovative. They can change their configuration on the fly based on what they want to do. They can flex their capacity up and down based on a task — how much compute and storage they need. They have a very flexible way of doing that. They have it all automated, all scripted. They can turn on a dime, even as a very large IT organization. 

And they have had some pretty impressive ways of repurposing their IT. Today we are doing X and tonight we are doing Y. They can repurpose that literally in minutes — versus days for traditional tasks.

Gardner: Are your customers also innovating in ways that allow them to get a common view across the entire lifecycle of IT? I’m thinking from requirements, through development, deployment, test, and continuous redeployment. 

de Werd: Yes, they can string all of these processes together using different partner tools, yet at the core they use HPE OneView and HPE Synergy underneath the covers to provide that real, raw engine. 

By using the HPE partner ecosystem integrated with HPE OneView, they have visibility. Then they can get into things like Docker Swarm. It may not be HPE OneView providing that total visibility. At the hardware level it is, but because we feed into upper-level apps they can adjust to meet the needs across the entire business process.

By using the HPE partner ecosystem integrated with HPE OneView, they have that visibility. Then they can get into things like Docker Swarm. It may not be HPE OneView providing that total visibility. At the hardware and infrastructure level it is, but because we are feeding into upper-level and broader applications, they can see what’s going on and determine how to adjust to meet the needs across the entire business process.

Gardner: In terms of HPE Synergy and composability, what’s the relationship between composability and IT management? Are people making the whole greater than the sum of the parts with those?

de Werd: They are trying to. I think there is still a learning curve. Traditional IT has been around a long time. It just takes a while to change the mentality, skills sets, and internal politics. It takes a while to get to that point of saying, “Yeah, this is a good way to go.”

But once they dip their toes into the water and see the benefits — the power, flexibility, and ease of it — they are like, “Wow, this is really good.” One step leads to the next and pretty soon they are well on their way on their composable journey.

Gardner: We now see more intelligence brought to management products. I am thinking about how HPE InfoSight is being extended across more storage and server products.

How to Eliminate Complex Manual Processes 

And Increase Speed of IT Delivery 

We used to access log feeds from different IT products and servers. Then we had agents and agent-less analysis for IT management. But now we have intelligence as a service, if you will, and new levels of insight. How will HPE OneView evolve with this new level of increasingly pervasive intelligence?

de Werd: HPE InfoSight is a great example. You see it being used in multiple ways, things like taking the human element out, things like customer advisories coming out and saying, “Such-and-such product has a problem,” and how that affects other products.

If you are sitting there looking at 1,000 or 5,000 servers in your data center, you’re wondering how I am affected by this? There are still a lot of manual spreadsheets out there, and you may find yourself pouring through a list.

Today, you have the capability of getting an [intelligent alert] that says, “These are the ones that are affected. Here is what you should do. Do you want us to go fix it right now?” That’s just an example of what you can do. 

It makes you more efficient. You begin to understand howyou are using your resources, where your utilization is, and how you can then optimize that. Depending on how flexible you want to be, you can design your systems to respond to those inputs and automatically flex [deployments] to the places that you want to be. 

This leads to autonomous computing. We are not quite there yet, but we are certainly going in that direction. You will be able to respond to different compute, storage, and network requirements and adjust on the fly. There will also be self-healing and self-morphing into a continuous optimization model.

Gardner: And, of course, that is a big challenge these days … hybrid cloud, hybrid IT, and deploying across on-premises cloud, public cloud, and multicloud models. People know where they want to go with that, but they don’t know how to get there.

How does modern IT management help them achieve what you’ve described across an increasingly hybrid environment?

Manage from the cloud down 

de Werd: They need to understand what their goals are first. Just running virtual machines (VMs) in the cloud isn’t really where they want to be. That was the initial thing. There are economic considerations involved in the cloud, CAPEX and OPEX arguments.

Simply moving your infrastructure from on-premises up into the cloud isn’t going to get you where you really need to be. You need to look at it from a cloud-native-application perspective, where you are using micro services, containers, and cloud-enabled programming languages — your Javas and .NETs and all the other stateless types of things – all of which give you new flexibility to flex performance-wise.

From the management side, you have to look at different ways to do your development and different ways to do delivery. That’s where the management comes in. To do DevOps and exploit the DevOps tools, you have to flip the way you are thinking — to go from the cloud down. 

Cloud application development on-premises, that’s one of the great things about containers and cloud-native, stateless types of applications. There are no hardware dependencies, so you can develop the apps and services on-premises, and then run them in the cloud, run them on-premises, and/or use your hybrid cloud vendor’s capabilities to burst up into a cloud if you need it. That’s the joy of having those types of applications. They can run anywhere. They are not dependent on anything — on any particular underlying operating system.

But you have to shift and get into that development mode. And the automation helps you get there, and then helps you respond quickly once you do. 

Gardner: Now that hybrid deployment continuum extends to the edge. There will be increasing data analytics, measurement, and making deployment changes dynamically from that analysis at the edge.

It seems to me that the way you have designed and architected HPE IT management is ready-made for such extensibility out to the edge. You could have systems run there that can integrate as needed, when appropriate, with a core cloud. Tell me how management as you have architected it over the years helps manage the edge, too.

Businesses need to move their processing further out to the edge and gain the instant response, instant gratification. You can’t wait to have an input analyzed by going all the way back to the cloud. You want the processing toward the edge to get that instantaneous response.

de Werd: Businesses need to move their processing further out to the edge, and gain the instant response, instant gratification. You can’t wait to have an input analyzed on the edge, to have it go all the way back to a data source or all the way up to a cloud. You want to have the processing further and further toward the edge so you can get that instantaneous response that customers are coming to expect. 

But again, being able to automate how to do that, and having the flexibility to respond to differing workloads and moving those toward the edge, I think, is key to getting there.

Gardner: And Doug, for you, personally, do you have some takeaways from your years of experience about innovation and how to make innovation a part of your daily routine?

de Werd: One of the big impacts on the team that I work with is in our quality assurance (QA) testing. It’s a very complex thing to test various configurations; that’s a lot of work. In the old days, we had to manually reconfigure things. Now, as we use an Agile development process, testing is a continuous part of it. 

We can now respond very quickly and keep up with the Agile process. It used to be that testing was always the tail-end and the longest thing. Development testing took forever. Now because we can automate that, it just makes that part of the process easier, and it has taken a lot of stress off of the teams. We are now much quicker and nimbler in responses, and it keeps people happy, too.

How to Get Simle, Automated Management 

Of Your Hybrid Infrastructure 

Gardner: As we close out, looking to the future, where do you see management going, particularly how to innovate using management techniques, tools, and processes? Where is the next big green light coming from?

Set higher goals 

de Werd: First, get your house in order in terms of taking advantage of the automation available today. Really think about how not to just use the technology as the end-state. It’s more of a means to get to where you want to be.

Define where your organization wants to be. Where you want to be can have a lot of different aspects; it could be about how the culture evolves, or what you want your customers’ experience to be. Look beyond just, “I want this or that feature.” 

Then, design your full IT and development processes. Get to that goal, rather than just saying, “Oh, I have 100 VMs running on a server, isn’t that great?” Well, if it’s not achieving the ultimate goal of what you want, it’s just a technology feat. Don’t use technology just for technology’s sake. Use it to get to the larger goals, and define those goals, and how you are going to get there. 

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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The next BriefingsDirect Voice of the Customer edge computing trends discussion explores the rapidly evolving confluence of operational technology (OT) and Internet of Things (IoT). 

New advances in data processing, real-time analytics, and platform efficiency have prompted innovative and impactful OT approaches at the edge. We’ll now explore how such data analysis platforms bring manufacturers data-center caliber benefits for real-time insights where they are needed most. 

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. 

To hear more about the latest capabilities in gaining unprecedented operational insights, we sat down with Riaan Lourens, Vice President of Technology in the Office of the Chief Technology Officer at PTC, and Tripp Partain, Chief Technology Officer of IoT Solutions at Hewlett Packard Enterprise (HPE). The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Riaan, what kinds of new insights are manufacturers seeking into how their operations perform?

Lourens: We are in the midst of a Fourth Industrial Revolution, which is really an extension of the third, where we used electronics and IT to automate manufacturing. Now, the fourth is the digital revolution, a fusion of technology and capabilities that blur the lines between the physical and digital worlds. 

With the influx of these technologies, both hardware and software, our customers — and manufacturing as a whole, as well as the discrete process industries — are finding opportunities to either save or make more money. The trend is focused on looking at technology as a business strategy, as opposed to just pure IT operations.

There are a number of examples of how our customers have leveraged technology to drive their business strategy.

Gardner: Are we entering a golden age by combining what OT and IT have matured into over the past couple of decades? If we call this Industrial Revolution 4.0 (I4.0) there must be some kind of major opportunities right now.

Lourens: There are a lot of initiatives out there, whether it’s I4.0, Made in China 2025, or the Smart Factory Initiative in the US. By democratizing the process of providing value — be it with cloud capabilities, edge computing, or anything in between – we are inherently providing options for manufacturers to solve problems that they were not able to solve before.

The opportunity for manufacturers today allows them to solve problems that they face almost immediately. There is quick time-to-value by leveraging technology that is consumable. 

If you look at it from a broader technology standpoint, in the past we had very large, monolith-like deployments of technology. If you look at it from the ISA-95 model, like Level 3 or Level 4, your MES deployments or large-scale enterprise resource planning (ERP), those were very large deployments that took many years. And the return on investment (ROI) the manufacturers saw would potentially pay off over many years.

The opportunity that exists for manufacturers today, however, allows them to solve problems that they face almost immediately. There is quick time-to-value by leveraging technology that is consumable. Then they can lift and drop and so scale [those new solutions] across the enterprise. That does make this an era the likes of which nobody has seen before.

Gardner: Tripp, do you agree that we are in a golden age here? It seems to me that we are able to both accommodate a great deal of diversity and heterogeneity of the edge, across all sorts of endpoints and sensors, but also bring that into a common-platform approach. We get the best of efficiency and automation.

Partain: There is a combination of two things. One, due to the smartphone evolution over the last 10 years, the types of sensors and chips that have been created to drive that at the consumer level are now at such reasonable price points you are able to apply these to industrial areas. 

To Riaan’s point, the price points of these technologies have gotten really low — but the capabilities are really high. A lot of existing equipment in a manufacturing environment that might have 20 or 30 years of life left can be retrofitted with these sensors and capabilities to give insights and compute capabilities at the edge. The capability to interact in real-time with those sensors provides platforms that didn’t exist even five years ago. That combines with the right software capabilities so that manufacturers and industrials get insights that they never had before into their processes.

Gardner: How is the partnership between PTC and HPE taking advantage of this new opportunity? It seems you are coming from different vantage points but reinforcing one another. How is the whole greater than the sum of the parts when it comes to the partnership?

Partnership for progress, flexibility

Lourens: For some context, PTC is a software vendor. Over the last 30 years we targeted our efforts at helping manufacturers either engineer software with computer-aided design (CAD) or product lifecycle management (PLM). We have evolved to our growth areas today of IoT solution platforms and augmented reality (AR) capabilities. 

The challenge that manufacturers face today is not just a software problem. It requires a robust ecosystem of hardware vendors, software vendors, and solutions partners, such as regional or global systems integrators. 

The reason we work very closely with HPE as an alliance partner is because HPE is a leader in the space. HPE has a strong offering of compute capabilities — from very small gateway-level compute all the way through to hybrid technologies and converged infrastructure technologies.

Ultimately our customers need flexible options to deploy software at the right place, at the right time, and throughout any part of their network. We find that HPE is a strong partner on this front.

Gardner: Tripp, not only do we have lower cost and higher capability at the edge, we also have a continuum of hybrid IT. We can use on-premises micro-datacenters, converged infrastructure, private cloud, and public cloud options to choose from. Why is that also accelerating the benefits for manufacturers? Why is a continuum of hybrid IT – edge to cloud — an important factor?

Partain: That flexibility is required if you look at the industrial environments where these problems are occurring for our joint customers. If you look at any given product line where manufacturing takes place — no two regions are the same and no two factories are the same. Even within a factory, a lot of times, no two production lines are the same. 

There is a wide diversity in how manufacturing takes place. You need to be able to meet those challenge with the customers to give them the deployment options that meet each of those environments.

It’s interesting. Factories don’t do enterprise IT-like deployments, where every factory takes on new capabilities at the same time. It’s much more balanced in the way that products are made. You have to be able to have that same level of flexibility in how you deploy the solutions, to allow it to be absorbed the same way the factories do all of their other types of processes. 

We have seen the need for different levels of IT to match up to the way they are implemented in different types of factories. That flexibility meets them where they are and allows them to get to the value much quicker — and not wait for some huge enterprise rollout, like what Riaan described earlier with ERP systems that take multiple years.

By leveraging new, hybrid, converged, and flexible environments, we allow a single plant to deploy multiple solutions and get results much quicker. We can also still work that into an enterprise-wide deployment — and get a better balance between time and return.

Gardner: Riaan, you earlier mentioned democratization. That jumped out at me. How are we able to take these advances in systems, software, and access and availability of deployments and make that consumable by people who are not data scientists? How are we able to take the results of what the technology does and make it actionable, even using things like AR?

Lourens: As Tripp described, every manufacturing facility is different. There are typically different line configurations, different programmable logic controller (PLC) configurations, different heterogeneous systems — be it legacy IT systems or homegrown systems — so the ability to leverage what is there is inherently important. 

From a strategic perspective, PTC has two core platforms; one being our ThingWorx Platform that allows you to source data and information from existing systems that are there, as well as from assets directly via the PLC or by embedding software into machines.

We also have the ability to simplify and contextualize all of that information and make sense of it. We can then drive analytical insights out of the data that we now have access to. Ultimately we can orchestrate with end users in their different personas – be that the maintenance operator, supervisor, or plant manager — enabling and engaging with these different users through AR.

Four capabilities for value 

There are four capabilities that allow you to derive value. Ultimately our strategy is to bring that up a level and to provide capabilities solutions to our end customers across four different areas. 

One, we look at it from an enterprise operational intelligence perspective; the second is intelligent asset optimization; the third, digital workforce productivity, and fourth, scalable production management.

So across those four solution areas we can apply our technology together with that of our sourced partners. We allow our customers to find use-cases within those four solution areas that provides them a return on investment.

One example of that would be leveraging augmented work instructions. So instead of an operator going through a maintenance procedure by opening a folder of hundreds of pages of instructions, they can leverage new technology such as AR to guide the operator in process, and in situ, in terms of how to do something.

There are many use cases across those four solution areas that leverage the core capabilities across the IoT platform, ThingWorx, as well as the AR platform, Vuforia.

Gardner: Tripp, it sounds like we are taking the best of what people can do and the best of what systems and analytics can do. We also move from batch processing to real time. We have location-based services so we can tell where things and people are in new ways. And then we empower people in ways that we hadn’t done before, such as AR.

Are we at the point where we’re combining the best of cognitive human capabilities and machine capabilities?

Partain: I don’t know if we have gotten to the best yet, but probably the best of what we’ve had so far. As we continue to evolve these technologies and find new ways to look at problems with different technology — it will continue to evolve.

We are getting to the new sweet spot, if you will, of putting the two together and being able to drive advancements forward. One of the things that’s critical has to do with where our current workforce is. 

A number of manufacturers I talk to — and I’ve heard similar from PTC’s customers and our joint customers — is you are at a tipping point in terms of the current talent pool, with those currently employed and those getting close to retirement age.

The next generation that’s coming in is not going to have the same longevity and the same skill sets. Having these newer technologies and bringing these pieces together, it’s not only a new matchup based on the new technology – it’s also better suited for the type of workers carrying these activities forward. Manufacturing is not going away, but it’s going to be a very different generation of factory workers and types of technologies.

The solutions are now available to really enhance those jobs. We are starting to see all of the pieces come together. That’s where both IoT solutions — but even especially AR solutions like PTC Vuforia — really come into play.

Gardner: Riaan, in a large manufacturing environment, only small iterative improvements can make a big impact on the economics, the bottom line. What sort of future categorical improvements value are we looking at? To what degree do we have an opportunity to make manufacturing more efficient, more productive, more economically powerful?

Tech bridges skills gap, talent shortage

Lourens: If you look at it from the angle that Tripp just referred to, there are a number of increasing pressures across the board in the industrial markets via the workers’ skills gap. Products are also becoming more complex. Workspaces are becoming more complex. There are also increasing customer demands and expectations. Markets are just becoming more fiercely competitive.

But if you leverage capabilities such as AR — which provides augmented 3-D work instructions, expert guidance, and remote assistance, training, and demonstrations — that’s one area. If you combine that, to Tripp’s point, with the new IoT capabilities, then I think you can look at improvements such as reducing waste in processes and materials.

We have seen customers reducing by 30 percent unplanned downtime, which is a very common use case that we see manufacturers target. We also see reducing energy consumption by 3 to 7 percent. And we’re looking at improving productivity by 20 to 30 percent.

We have seen customers reducing by 30 percent unplanned downtime, which is a very common use case that we see manufacturers target. We also see reducing energy consumption by 3 to 7 percent at a very large ship manufacturer, a customer of PTC’s. And we’re generally looking at improving productivity by 20 to 30 percent.

By leveraging this technology in a meaningful way to get iterative improvements, you can then scale it across the enterprise very rapidly, and multiple use cases can become part of the solution. In these areas of opportunity, very rapidly you get that ROI.

Gardner: Do we have concrete examples to help illustrate how those general productivity benefits come about?

Joint solutions reduce manufacturing pains 

Lourens: A joint-customer between HPE and PTC focuses on manufacturing and distributing reusable and recyclable food packaging containers. The company, CuBE Packaging Solutions, targeted protective maintenance in manufacturing. Their goal is to have the equipment notify them when attention is needed. That allows them to service what they need when they need to and focus on reducing unplanned downtime.

In this particular example, there are a number of technologies that play across both of our two companies. The HPE Nimble Storage capability and HPE Synergy technology were leveraged, as well as a whole variety of HPE Aruba swit
ches and wireless access points, along with PTC’s ThingWorx solution platform.

The CuBE Packaging solution ultimately was pulled together through an ecosystem partner, Callisto Integration, which we both worked with very closely. In this use case, we not only targeted the plastic molding assets that they were monitoring, but the peripheral equipment, such as cooling and air systems, that may impact their operations. The goal is to avoid anything that could pause their injection molding equipment and plants.

Gardner: Tripp, any examples of use-cases that come to your mind that illustrate the impact?

Partain: Another joint-customer that comes to mind is Texmark Chemicals in Galena Park, Texas. They are using number of HPE solutions, including HPE Edgeline, our micro-datacenter. They are also using PTC ThingWorx and a number of other solutions.

They have very large pumps critical to the operation as they move chemicals and fluids in various stages around their plant in the refining process. Being able to monitor those in real time, predict potential failures before they happen, and use a combination of live data and algorithms to predict wear and tear, allows them to determine the optimal time to make replacements and minimize downtime.

Such uses cases are one of the advantages when customers come and visit our IoT Lab in Houston. From an HPE standpoint, not only do they see our joint solutions in the lab, but we can actually take them out to the Texmark location and Texmark will host and allow you them see these technologies in real-time working at their facility.

Similar as Riaan mentioned, we started at Texmark with condition monitoring and now the solutions have moved into additional use cases — whether it’s mechanical integrity, video as a sensor, and employee-safety-related use cases.

We started with condition monitoring, proved that out, got the technology working, then took that framework — including best-in-class hardware and software — and continued to build and evolve on top of that to solve expanded problems. Texmark has been a great joint customer for us.

Gardner: Riaan, when organizations hear about these technologies and the opportunity for some very significant productivity benefits, when they understand that more-and-more of their organization is going to be data-driven and real-time analysis benefits could be delivered to people in their actionable context, perhaps using such things as AR, what should they be doing now to get ready?

Start small

Lourens: Over the last eight years of working with ThingWorx, I have noticed the initial trend of looking at the technology versus looking at specific use-cases that provide real business value, and of working backward from the business value.

My recommendation is to target use cases that provide quick time-to-value. Apply the technology in a way that allows you to start small, and then iterate from there, versus trying to prove your ROI based on the core technology capabilities.

Ultimately understand the business challenges and how you can grow your top line or your bottom line. Then work backward from there, starting small by looking at a plant or operations within a plant, and then apply the technology across more people. That helps create a smart connected people strategy. Apply technology in terms of the process and then relative to actual machines within that process in a way that’s relevant to use cases — that’s going to drive some ROI.

Gardner: Tripp, what should the IT organization be newly thinking? Now, they are tasked with maintaining systems across a continuum of cloud-to-edge. They are seeing micro-datacenters at the edge; they’re doing combinations of data-driven analytics and software that leads to new interfaces such as AR.

How should the IT organization prepare itself to take on what goes into any nook and cranny in almost any manufacturing environment?

IT has to extend its reach 

Partain: It’s about doing all of that IT in places where typically IT has had a little or no involvement. In many industrial and manufacturer organizations, as we go in and start having conversations, IT really has usually stopped at the datacenter back-end. Now there’s lots of technology in the manufacturing side, too, but it has not typically involved the IT department.

One of the first steps is to get educated on the new edge technologies and how they fit into the overall architecture. They need to have the existing support frameworks and models in place that are instantly usable, but also work with the business side and frame-up the problems they are trying to solve.

As Riaan mentioned, being able to say, “Hey, here are the types of technologies we in IT can apply to this that you [OT] guys haven’t necessarily looked at before. Here’s the standardization we can help bring so we don’t end up with something completely different in every factory, which runs up your overall cost to support and run.”

It’s a new world. And IT is going to have to spend much more time with the part of the business they have probably spent the least amount of time with. IT needs to get involved as early as possible in understanding what the business challenges are and getting educated on these newer IoT, AR, virtual reality (VR), and edge-based solutions. These are becoming the extension points of traditional technology and are the new ways of solving problems.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

You may also be interested in:

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• The budding storage relationship between HPE and Cohesity brings the best of startup innovation to global enterprise reach

• Industrial-strength wearables combine with collaboration cloud to bring anywhere expertise to intelligent-edge work

• HPE’s Erik Vogel on what’s driving success in hybrid cloud adoption and optimization

• IT kit sustainability: A business advantage and balm for the planet

• How total deployment intelligence overcomes the growing complexity of multicloud management

• Manufacturer gains advantage by expanding IoT footprint from many machines to many insights

• How Texmark Chemicals pursues analysis-rich, IoT-pervasive path to the ‘refinery of the future’

• Using AI to Solve Data and IT Complexity–And Better Enable AI

The next BriefingsDirect Voice of the Innovator podcast discussion explores the latest insights into hybrid cloud and hyperconverged infrastructure (HCI) strategies.

Speed to business value and simplicity in deployments have been top drivers of the steady growth around HCI solutions. IT operators are now looking to increased automation, built-in intelligence, and robust security as they seek such turnkey appliance approaches for both cloud and traditional workloads.

Stay with us now as we examine the rapidly evolving HCI innovation landscape, which is being shaped just as much by composability, partnerships, and economics, as it is new technology.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

Here to help us learn more about the next chapter of automated and integrated IT infrastructure solutions is Thomas Goepel, Chief Technologist for Hyperconverged Infrastructure at Hewlett Packard Enterprise (HPE). The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Thomas, what are the top drivers now for HCI as a business tool? What’s driving the market now, and how has that changed from a few years ago?

Goepel: HCI has gone through a really big transformation in the last few years. When I look at how it originally started, it was literally people looking for a better way of building virtual desktop infrastructure (VDI) solutions. They wanted to combine servers and storage in a single device and make it easier to operate.

What I am seeing now is HCI spreading throughout datacenters and becoming one of the core elements of a lot of the datacenters around the world. The use cases have significantly been expanded. It started out with VDI, but now people are running all kinds of business applications on HCI — all the way to critical databases  like SAP HANA.

Gardner: People are using HCI in new ways. They are innovating in the market, and that often means they do things with HCI that were not necessarily anticipated. Do you see that happening with HCI?

Ease of use encourages HCI expansion 

Goepel: Yes, it’s happened with HCI quite a bit. The original use cases were very much focused on VDI and end-user computing. It was just a convenient way of having a platform for all of your virtual desktops and an easy way of managing them.

But people saw that ease of management can actually be expanded into other use cases. They then began to bring in some core business applications, such as Microsoft Exchange or SharePoint, logged onto the platform and saw there are more and more things they can put on there, and gain the entire simplicity that hyperconverged brings to operating in this environment.

How Hyperconverged Infrastructure Delivers

Unexpected Results for VDI Users

You no longer had to build a separate server farm, separate storage farm, or even manage your network independently. You could now do all of that from a single interface, a single-entry point, and gain a single point of management. Then people said, “Well, this ease makes it so beneficial for me, why don’t we bring the other things in here?” And then we saw it spread out in the data centers.

What we now have is people saying, “Hey, let me take this a step further. If I have remote offices, branch offices, or edge use-cases where I also need compute resources, why not try to take HCI there? Because typically on the edge I don’t even have system administrators, so I can take this entire simplicity down to this point, too.”

And the nice thing with hyperconvergence is that — at least in the HPE version of hyperconvergence, which is HPE SimpliVity — it’s not only simple to manage, it has also built in all of the enterprise features such as high availability and data efficiency, so it makes it really a robust solution. It has come a very long way on this journey.

Gardner: Thomas, you mentioned the role of HCI at the edge gaining traction and innovation. What’s a typical use case for this sort of micro datacenter at the edge? How does that work?

Losing weight with HCI wins the race

Goepel: Let me give you a really good example of a super-fast-paced industry: Formula One car racing. It really illustrates how edge is having an impact — and also how this has a business impact.

One of our customers, Aston Martin Red Bull Racing, has been very successful in Formula One racing. The rules of the International Automobile Federation (FIA), the governing board of Formula One racing, say that each race team can only bring a certain amount of weight to a racetrack during the races.

This is obviously a high-tech race. They are adjusting the car during the race, lap by lap, making adjustments based on the real-time performance of the car to get the last inch possible out of the car to win that race. All of these cars are very close to each other from a performance perspective.

Traditionally, they shipped racks and racks of IT gear to the racetrack to calculate the performance of the car and make adjustments during the race. They have now replaced all of these racks with HPE SimpliVity HCI gear and significantly reduced the amount of gear. It means having significantly less weight to bring to the racetrack.

How Hyperconvergence Plays

A Pivotal Role at Red Bull

There are two benefits. First, reducing the weight of the IT gear allows them to bring additional things to the racetrack because what counts is the total weight – and that includes the car, spare parts, people, equipment — everything. There is a certain mandated limit.

By taking that weight out, having less IT equipment on the racetrack, the HCI allows them to bring extra personnel and spare parts. They can perform better in the races.

The other benefit is that HCI performs significantly better than traditional IT infrastructure. They can now make adjustments within one lap of the race versus before, when it took them three laps before they could make adjustments to the car.

This is a huge competitive advantage. When you look at the results, they are doing great when it comes to Formula One racing, especially for being a smaller team compared to the big teams out there.

From that perspective, at the edge, HCI is making some big improvements, not only in a high-end industry like Formula One racing, but in all kinds of other industries, including manufacturing and retail. They are seeing similar benefits.

Gardner: I wrote a research paper about four years ago, Thomas, that laid out the case that HCI will become a popular on-ramp to private clouds and ultimately hybrid cloud. Was I ahead of my time?

HCI on-ramp to the clouds

Goepel: Yes, I think you were a little bit ahead of your time. But you were also a visionary to lay out that groundwork. When you look at the industry, hyperconvergence is a fast-growing industry segment. When it comes to server and data center infrastructure, HCI has the highest growth rate across the entire IT industry.

I don’t see an end anytime soon. HCI continues to grow as people discover new use cases. The edge is one new element, but we are just scratching the surface.

What you were foreseeing four years ago is exactly what we now have, and I don’t see an end anytime soon. HCI continues to grow as people discover new use cases. The edge is one new element, but we are just scratching the surface.

Edge use cases are a fascinating new world in general — from such distributed environments as smart cities and smart manufacturing. We are just starting to get into this world. There’s a huge opportunity for innovation and this will become an attractive area for hyperconvergence. 

Gardner: How does HCI innovation align with other innovations at HPE around automation, composability, and intelligence derived to make IT behave as total solutions? Is there a sense that the whole is greater than the sum of the parts?

HCI innovations prevent problems

Goepel: Absolutely there is. We have leveraged a lot of innovation in the broader HPE ecosystem, including the latest generation of the ProLiant DL380 Server, the most secure server in the industry. All of these elements flew into the HPE SimpliVity HCI platform, too.

But we are not stopping there. A lot of other innovations in the HPE ecosystem are being brought into hyperconvergence. A perfect example is HPE InfoSight, a management platform that allows you to operate your infrastructure better by understanding what’s going on in a very efficient way. It uses artificial intelligence (AI) to detect when something is going wrong in your IT environment so you can proactively take action and don’t end up with a disaster.

How to Tell if Your Network

Is Really Aware of Your Infrastructure

HPE InfoSight originally started out in storage, but we are now taking it into the full HPE SimpliVity HCI ecosystem. It’s not just a support portal, it gives you intelligence to understand what’s going on before you run into problems. Those problems can be solved so your environment keeps running at top performance. You’ll have what you need to run any mission-critical business on HCI. 

More and more of these innovations in our ecosystem will be brought into the hyperconverged world. Another example is around composability. We have been developing a lot of platform capabilities around composability and we are now bringing HPE SimpliVity and composability together. This allows customers to actually change the infrastructure’s personality depending on the workload, including bringing on HPE SimpliVity. You can get the best of these two worlds.

This leads to building a private cloud environment that can be easily connected to a public cloud or clouds. You will ultimately build out a hybrid IT environment in such a way that your private cloud environment, or your on-premise environment, runs in the most optimized way for your business and for your specific needs as a company.

Gardner: You are also opening up that HCI ecosystem with new partners. Tell us how innovation around hyperconverged is broadening and making it more ecumenical for the IT operations consumer.

Welcome to the hybrid world

Goepel: HPE has always been an open player. We never believed in locking down an environment or making it proprietary and basically locking out everyone else. We have always been a company that listens to what our customers want, what our customers need, and then give them the best solution.

Now, customers are looking to run their HCI environment on HPE equipment and infrastructure because they know that this is reliable infrastructure. It is working, and they feel comfortable with it, and they trust it. But we also have customers who say, “Hey, you know, I want to run this piece of software or that solution on this HPE environment. Can you make sure this runs and works perfectly?”

We are in a hybrid world. And in a hybrid world there is not a single vendor that can cover the entire hybrid market. We need to innovate in such a way that we allow an ecosystem of partners to all come together and work collaboratively and jointly to provide new solutions.

We have recently announced new partnerships with other software vendors, and that includes HPE GreenLake Flex Capacity. With that, instead of doing big, upfront investments on equipment, you can do it in a more innovative way financially. It brings about the solution that solves the customers’ real problems, rather than locking the customer into some certain infrastructure.

Flexibility improves performance 

Gardner: You are broadening the idea of making something consumable when you innovate, not only around the technology and the partnerships, but also the economic model, the consumption model. Tell us more about how HPE GreenLake Flex Capacity and acquiring a turnkey HPE SimpliVity HCI solution can accelerate value when you consume it, not as a capital expense, but as an operating cost affair.

Goepel: No industry is 100 percent predictable, at least I haven’t seen it, and I haven’t found it. Not even the most conservative government institution that has a five-year plan is predictable. There are always factors that will disrupt that predictability plan, and you have to react to that.

How Hyperconverged Infrastructure

 Solves Unique Challenges

For Datacenters at the Edge

Traditionally, what we have done in the industry is oversized our environments to calculate for anticipated growth over five years — and then add another 25 percent on top of it, and then another 10 percent cover on top of that. Hopefully we did not undersize the environment once we get to the end of the life of the equipment. 

That is a lot of capital you are investing into something that just sits there and has no value, no use, and just basically stands around, and you take off of your books in the financial perspective. 

Now, HPE GreenLake gives you a flexible-capacity model. You only pay literally for what you consume. If you grow faster than you anticipated, you just use more. If you grow slower, you use less. If you have an extremely successful business — but then something in the economic model changes and your business doesn’t perform as you have anticipated — then you can reduce your spending. That flexibility better supports your business.

IT shouldn’t be a burden that slows you down, it should be an accelerator. By having a flexible financial model, you get exactly that.You can scale up and down based on your business needs. 

We are ultimately doing IT to help our businesses to perform better. IT shouldn’t be a burden that slows you down, it should be an accelerator. By having a flexible financial model, you get exactly that. HPE GreenLake allows you to scale up and scale down your environment based on your business needs with the right financial benefits behind it.

Gardner: There is such a thing as too much of a good thing. And I suppose that also applies to innovation. If you are doing so many new and interesting things — allowing for hybrid models to accelerate and employing new economic models — sometimes things can spin out of control.

But you can also innovate around management to prevent that from happening. How does management innovation fit into these other aspects of a solution, to keep it from getting out of control?

Checks and balances extend manageability

Goepel: You bring up a really good point. One of the things we have learned as an industry is that things can spin out of control very quickly. And for me, the best example is when I go back two years when people said, “I need to go to the cloud because that is going to save my world. It’s going to reduce my costs, and it’s going to be the perfect solution for me.”

What happened is people went all-in for the cloud and every developer and IT person heard, “Hey, if you need a virtual machine just get it on whatever your favorite cloud provider is. Go for it.” People very quickly learned that this means exploding their costs. There was no control, no checks and balances.

On both the HCI and general IT side, we have learned from that initial mistake in the public cloud and have put the right checks and balances in place. HPE OneView is our infrastructure management platform that allows the system administrator to operate the infrastructure from a single-entry point or single point of view.

How Hyperconverged Infrastructure

 Helps Trim IT Complexity

Without Sacrificing Quality

That gives you a very simple way of managing and plays along with the way HCI is operated — from a single point of view. You don’t have five consoles or five screens, you literally have one screen you operate from. 

You need to have a common way of managing checks and balances in any environment. You don’t want the end user or every developer to go in there and just randomly create virtual machines, because then your HCI environment quickly runs out of resources, too. You need to have the right access controls so that only people that have the right justification can do that, but it still needs to happen quickly. We are in a world where a developer doesn’t want to wait three days to get a virtual machine. If he is working on something, he needs the virtual machine now — not in a week or in two days.

Similarly, when it comes to a hybrid environment — when we bring together the private cloud and the public cloud — we want a consistent view across both worlds. So this is where HPE OneSphere comes in. HPE OneSphere is a cloud management platform that
manages hybrid clouds, so private and public clouds. 

It allows you to gain a holistic view of what resources you are consuming, what’s the cost of these resources, and how you can best distribute workloads between the public and private clouds in the most efficient way. It is about managing performance, availability, and cost. You can put in place the right control mechanisms to curb rogue spending, and control how much is being consumed and where.

Gardner: From all of these advancements, Thomas, have you made any personal observations about the nature of innovation? What is it about innovation that works? What do you need to put in place to prevent it from becoming a negative? What is it about innovation that is a force-multiplier from your vantage point?

Faster is better 

Goepel: The biggest observation I have is that innovation is happening faster and faster. In the past, it took quite a while to get innovation out there. Now it is happening so fast that one innovation comes, then the next one just basically runs over it, and we are taking advantage of it, too. This is just the nature of the world we are living in; everything is moving much faster. 

There are obviously some really great benefits from the innovation we are seeing. We have talked about a few of them, like AI and how HCI is being used in edge use-cases. In manufacturing, hospitals, and these kinds of environments, you can now do things in better and more efficient ways. That’s also helping on the business side.

How One Business

Took Control of their Hybrid Cloud 

But there’s also the human factor, because innovation makes things easier for us or makes it better for us to operate. A perfect example is in hospitals, where we can provide the right compute power and intelligence to make sure patients get the right medication. It is controlled in a good way, rather than just somebody writing on a piece of paper and hoping the next person can read it. You can now do all of these things electronically, with the right digital intelligence to ensure that you are actually curing the patient.

I think we will see more and more of these types of examples happening and bringing compute power to the edge. That is a huge opportunity, and there is a lot of innovation in the next two to three years, specifically in this segment, and that will impact everyone’s life in a positive way. 

Gardner: Speaking of impacting people’s lives, I have observed that the IT operator is being greatly impacted by innovation. The very nature of their job is changing. For example, I recently spoke with Gary Thome, CTO for Composable Cloud at HPE, and he said that composability allows for the actual consumers of applications to compose their own supporting infrastructure.

Because of ease, automation, and intelligence, we don’t necessarily need to go to IT to say, “Set up XYZ infrastructure with these requirements.” Using composablity, we can move innovation to the very people who are in the most advantageous position to define what it is they need.

Thomas, how do you see innovation impacting the very definition of what IT people do?

No more mundane tasks 

Goepel: This is a very positive impact, and I will give you a really good example. I spend a lot of time talking to customers and to a lot of IT people out there. And I have never encountered a single systems administrator in this industry who comes to work in the morning and says, “You know, I am so happy that I am here this morning so I can do a backup of my environment. It’s going to take me four hours, and I am going to be the happiest person in the world if the backup goes through.” Nobody wants to do this. 

Nobody goes to work in the morning and says, “You know, I really hope I get a hard problem to solve, like my network crashes and I am going to be the hero in solving the problem, or by making a configuration change in my virtual environment.”

These are boring tasks that nobody is looking for, but we have to do it because we don’t have the right automation in our environments. We don’t have the right management tools in our environment. We put a lot of boring tasks to our administrators and let them do them. They are mundane and they don’t really look forward to them.

How Hyperconverged Infrastructure

Gives You 54 Minutes Back Every Hour

Innovation takes these burdens away from the systems administrator and frees up their time to do things that are not only more interesting, but also add to the bottom line of the company. They can better help drive the businesses and spend IT resources on something that makes the difference for the company’s bottom line.

Ultimately, you don’t want to be the one watching backups going through or restoring files. You want this to be automatic, with a couple of clicks, and then you spend your time on something more interesting.

Every systems administrator I talk to really likes the new ways. I haven’t seen anyone coming back to me and saying, “Hey, can you take this automation away and all this hyperconvergence away? I want to go back to the old way and do things manually so I know how to spend my eight hours of the day.” People have much more to do with the hours they have. This is just freeing them up to focus on the things that add value.

HCI to make IT life easier and easier 

Gardner: Before we close out, Thomas, how about some forward-looking thoughts about what innovation is going to bring next to HCI? We talked about the edge and intelligence, but is there more? What are we going to be talking about when it comes to innovation in two years in the HCI space?

Goepel: I touched on the edge. I think there will be a lot of things happening across the entire edge space, where HCI will clearly be able to make a difference. We will take advantage of the capabilities that HCI brings in all these segments — and it will actually drive innovation outside of the hyperconverged world, but by being enabled by HCI.

But there are a couple of other th
ings to look at. Self-healing using AI in IT troubleshooting, I think, will become a big innovation point in the HCI industry. What we are doing with HPE InfoSight is a start, but there is much more to come. This will continue to make the life of the systems administrator easier.

We want HCI as a platform to be almost invisible to the end user because they shouldn’t care about the infrastructure. It will behave like a cloud, but just be on-premises and private, and in a better, more controlled way. 

Ideally, we want HCI as a platform to be almost invisible to the end user because they shouldn’t care about the infrastructure. It will behave like a cloud, but just be on-premises and private, and in a better, more controlled way.

The next element of innovation you will see is HCI acting very similar to a cloud environment. And some of the first steps with that are what we are doing around composability. This will drive forward to where you change the personality of the infrastructure depending on the workload needed. It becomes a huge pool of resources. And if you need to look like a bare-metal server, or a virtual server — a big one or a small one — you can just change it and this will be all software controlled. I think that innovation element will then enable a lot of other innovations on top of it.

How to Achieve Composability

Across Your Datacenter

If you take these three elements — AI, composability of the infrastructure, and driving that into the edge use cases — that will enable a lot of business innovation. It’s like the three legs of a stool. And that will help us drive even further innovation.

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