Case Study: How HCI-Powered Private Clouds Accelerate Digital Transformation

Transcript of a discussion on how public cloud-like experiences, agility, and cost structures are being delivered via private cloud models.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Welcome to the next edition of the BriefingsDirect Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on digital transformation success. Stay with us now to learn how agile businesses are fending off disruption -- in favor of innovation.

Our next thought leadership interview examines how a world-class private cloud project evolved in the financial sector. We’ll now learn how public cloud-like experiences, agility, and cost structures are being delivered via a strictly private cloud model.

McKittrick

Jim McKittrick is here to help us explore the potential for cloud benefits when retaining control over the data center is a critical requirement. He is Senior Account Manager at Applied Computer Solutions (ACS) in Huntington Beach, California. Welcome, Jim.

Jim McKittrick: Thank you for having me. I’m glad to be here.

Gardner: Many enterprises want a private cloud for security and control reasons. They want an OpEx-like public cloud model, and that total on-premises control. Can you have it both ways?

McKittrick: We are showing that you can. People are learning that the public cloud isn't necessarily all it has been hyped up to be, which is what happens with newer technologies as they come out.

Gardner: What are the drivers for keeping it all private?

McKittrick: Security, of course. But if somebody actually analyzes it, a lot of times it will be about cost and data access, and the ease of data egress because getting your data back can sometimes be a challenge.

Also, there is a realization that even though I may have strict service-level agreements (SLAs), if something goes wrong they are not going to save my business. If that thing tanks, do I want to give that business away? I have some clients who absolutely will not.

Sleep well at night

Gardner: Control, and so being able to sleep well at night.

McKittrick: Absolutely. I have other clients that we can speak about who have HIPAA requirements, and they are privately held and privately owned. And literally the CEO says, “I am not doing it.” And he doesn’t care what it costs.

Gardner: If there were a huge delta between the price of going with a public cloud or staying private, sure. But that deltais closing. So you can have the best of both worlds -- and not pay a very high penalty nowadays.

McKittrick: If done properly, certainly from my experience. We have been able to prove that you can run an agile, cloud-like infrastructure or private cloud as cost-effectively -- or even more cost effectively -- than you can in the public clouds. There are certainly places for both in the market.

Gardner: It's going to vary, of course, from company to company -- and even department to department within a company -- but the fact is that that choice is there.

McKittrick: No doubt about it, it absolutely is.

Gardner: Tell us about ACS, your role there, and how the company is defining what you consider the best of hybrid cloud environments.

McKittrick: We are a relatively large reseller, about $600 million. We have specialized in data center practices for 27 years. So we have been in business quite some time and have had to evolve with the IT industry.

We have a head start on what's really coming down the pipe -- we are one to two years ahead of the general marketplace.

Structurally, we are fairly conventional from the standpoint that we are a typical reseller, but we pride ourselves on our technical acumen. Because we have some very, very large clients and have worked with them to get on their technology boards, we feel like we have a head start on what's really coming down the pipe --  we are maybe one to two years ahead of the general marketplace. We feel that we have a thought leadership edge there, and we use that as well as very senior engineering leadership in our organization to tell us what we are supposed to be doing.

Gardner: I know you probably can't mention the company by name, but tell us about a recent project that seems a harbinger of things to come.

Hyper-convergent control 

McKittrick: It began as a proof of concept (POC), but it’s in production, it’s live globally.

I have been with ACS for 18 years, and I have had this client for 17 of those years. We have been through multiple data center iterations.

When this last one came up, three things happened. Number one, they were under tremendous cost pressure -- but public cloud was not an option for them.

The second thing was that they had grown by acquisition, and so they had dozens of IT fiefdoms. You can imagine culturally and technologically the challenges involved there. Nonetheless, we were told to consolidate and globalize all these operations.

Thirdly, I was brought in by a client who had run the US presence for this company. We had created a single IT infrastructure in the US for them. He said, “Do it again for the whole world, but save us a bunch of money.” The gauntlet was thrown down. The customer was put in the position of having to make some very aggressive choices. And so he effectively asked me bring them “cool stuff.”

You could give control to anybody in the organization across the globe and they would be able to manage it.

They asked, “What's new out there? How can we do this?” Our senior engineering staff brought a couple of ideas to the table, and hyper-converged infrastructure (HCI) was central to that. HCI provided the ability to simplify the organization, as well as the IT management for the organization. You could give control of it to anybody in the organization across the globe and they would be able to manage it, working with partners in other parts of the world.

Gardner: Remote management being very important for this.

Learn How to Transform

To A Hybrid IT

Environment

McKittrick: Absolutely, yes. We also gained failover capabilities, and disaster recovery within these regional data centers. We ended going from -- depending on whom you spoke to -- somewhere between seven to 19 data centers globally, down to three. We were able to consolidate down to three. The data center footprint shrank massively. Just in the US, we went to one data center; we got rid of the other data center completely. We went from 34 racks down to 3.5.

Gardner: Hyper-convergence being a big part of that?

McKittrick: Correct, that was really the key, hyper-convergence and virtualization.

The other key enabling technology was data de-duplication, so the ability to shrink the data and then be able to move it from place to place without crushing bandwidth requirements, because you were only moving the changes, the change blocks.

Gardner: So more of a modern data lifecycle approach?

McKittrick: Absolutely. The backup and recovery approach was built in to the solution itself. So we also deployed a separate data archive, but that's different than backup and recovery. Backup and recovery were essentially handled by VMware and the capability to have the same machine exist in multiple places at the same time.

Gardner: Now, there is more than just the physical approach to IT, as you described it, there is the budgetary financial approach. So how do they maybe get the benefit of the  OpEx approach that people are fond of with public cloud models and apply that in a private cloud setting?

Budget benefits 

McKittrick: They didn't really take that approach. I mean we looked at it. We looked at essentially leasing. We looked at the pay-as-you-go models and it didn't work for them. We ended up doing essentially a purchase of the equipment with a depreciation schedule and traditional support. It was analyzed, and they essentially said, “No, we are just going to buy it.”

Gardner: So total cost of ownership (TCO) is a better metric to look at. Did you have the ability to measure that? What were some of the metrics of success other than this massive consolidation of footprint and better control over management?

McKittrick: We had to justify TCO relative to what a traditional IT refresh would have cost. That's what I was working on for the client until the cost pressure came to bear. We then needed to change our thinking. That's when hyper-convergence came through.

What we would have spent on just hardware and infrastructure costs, not including network and bandwidth -- would have been $55 million over five years, and we ended up doing it for $15 million.

The cost analysis was already done, because I was already costing it with a refresh, including compute and traditional SAN storage. The numbers I had over a five-year period – just what we would have spent on hardware and infrastructure costs, and not including network and bandwidth – would have been $55 million over five years, and we ended up doing it for $15 million.

Gardner: We have mentioned HCI several times, but you were specifically using SimpliVity, which is now part of Hewlett Packard Enterprise (HPE). Tell us about why SimpliVity was a proof-point for you, and why you think that’s going to strengthen HPE's portfolio.

Learn How to Transform

To A Hybrid IT

Environment

McKittrick: This thing is now built and running, and it's been two years since inception. So that's a long time in technology, of course. The major factors involved were the cost savings.

As for HPE going forward, the way the client looked at it -- and he is a very forward-thinking technologist -- he always liked to say, “It’s just VMware.” So the beauty of it from their perspective – was that they could just deploy on VMware virtualization. Everyone in our organization knows how to work with VMware, we just deploy that, and they move things around. Everything is managed in that fashion, as virtual machines, as opposed to traditional storage, and all the other layers of things that have to be involved in traditional data centers.

The HCI-based data centers also included built-in WAN optimization, built-in backup and recovery, and were largely on solid-state disks (SSDs). All of the other pieces of the hardware stack that you would traditionally have -- from the server on down -- folded into a little box, so to speak, a physical box. With HCI, you get all of that functionality in a much simpler and much easier to manage fashion. It just makes everything easier.

Gardner: When you bring all those HCI elements together, it really creates a solution. Are there any other aspects of HPE’s portfolio, in addition now to SimpliVity, that would be of interest for future projects?

McKittrick: HPE is able to take this further. You have to remember, at the time, SimpliVity was a widget, and they would partner with the server vendors. That was really it, and with VMware.

Now with HPE, SimpliVity can really build out their roadmap. There is all kinds of innovation that's going to come.

Now with HPE, SimpliVity has behind them one of the largest technology companies in the world. They can really build out their roadmap. There is all kinds of innovation that’s going to come. When you then pair that with things like Microsoft Azure Stack and HPE Synergy and its composable architecture -- yes, all of that is going to be folded right in there.

I give HPE credit for having seen what HCI technology can bring to them and can help them springboard forward, and then also apply it back into things that they are already developing. Am I going to have more opportunity with this infrastructure now because of the SimpliVity acquisition? Yes.

Gardner:  For those organizations that want to take advantage of public cloud options, also having HCI-powered hybrid clouds, and composable and automated-bursting and scale-out -- and soon combining that multi-cloud options via HPE New Stack – this gives them the best of all worlds.

Learn How to Transform

To A Hybrid IT

Environment

McKittrick: Exactly. There you are. You have your hybrid cloud right there. And certainly one could do that with traditional IT, and still have that capability that HPE has been working on. But now, [with SimpliVity HCI] you have just consolidated all of that down to a relatively simple hardware approach. You can now quickly deploy and gain all those hybrid capabilities along with it. And you have the mobility of your applications and workloads, and all of that goodness, so that you can decide where you want to put this stuff.

Gardner: Before we sign off, let's revisit this notion of those organizations that have to have a private cloud. What words of advice might you give them as they pursue such dramatic re-architecting of their entire IT systems?

A people-first process 

McKittrick: Great question. The technology was the easy part. This was my first global HCI roll out, and I have been in the business well over 20 years. The differences come when you are messing with people -- moving their cheese, and messing with their rice bowl. It’s profound. It always comes back to people.

The people and process were the hardest things to deal with, and quite frankly, still are. Make sure that everybody is on-board. They must understand what's happening, why it's happening, and then you try to get all those people pulling in the same direction. Otherwise, you end up in a massive morass and things don't get done, or they become almost unmanageable.

Gardner: Unfortunately, there are plenty of examples of that out there.

McKittrick: Certainly. Recently, I have been saying it more, “It always comes back to the people, that’s always the case.”

Gardner: I’m afraid we’ll have to leave it there. We have been exploring how a world-class private cloud project evolved in the financial sector. And we have learned how a private cloud model using HCI can deliver a public cloud-like experience -- with agility and cost structures that mimic public cloud attributes. This is especially important for those organizations that need to retain control over their data centers.

So please join me in thanking our guest, Jim McKittrick, Senior Account Manager at Applied Computer Solutions in Huntington Beach, California. Thanks so much, Jim.

McKittrick: Thank you for having me. I appreciate it.

Gardner: And a big thank you to our audience as well for joining this BriefingsDirect Voice of the Customer digital transformation success story. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-sponsored interviews.

Thanks again for listening. Please pass this along as you can in your IT community, and do come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Transcript of a discussion on how public cloud-like experiences, agility, and cost structures are being delivered via private cloud models. Copyright Interarbor Solutions, LLC, 2005-2017. All rights reserved.

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• Inside story on HPC’s AI role in Bridges 'strategic reasoning' research at CMU
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Inside Story on HPC’s AI Role in Bridges Strategic Reasoning Research Project at CMU

Transcript of a discussion on how Carnegie Mellon University researchers are advancing strategic reasoning and machine learning capabilities using the latest in high performance computing.  

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on digital transformation success stories. Stay with us now to learn how agile businesses are fending off disruption -- in favor of innovation.

Our next high performance computing (HPC) success interview examines how strategic reasoning is becoming more common and capable -- even using imperfect information. We’ll now learn how Carnegie Mellon University and a team of researchers there are producing amazing results with strategic reasoning thanks in part to powerful new memory-intense systems architectures.

Sandholm

To learn more about strategic reasoning advances, please join me in welcoming Tuomas Sandholm, Professor and Director of the Electronic Marketplaces Lab at Carnegie Mellon University in Pittsburgh.

Tuomas Sandholm: Thank you very much.

Gardner: Tell us about strategic reasoning and why imperfect information is often the reality that these systems face?

Sandholm: In strategic reasoning we take the word “strategic” very seriously. It means game theoretic, so in multi-agent settings where you have more than one player, you can't just optimize as if you were the only actor -- because the other players are going to act strategically. What you do affects how they should play, and what they do affects how you should play.

That's what game theory is about. In artificial intelligence (AI), there has been a long history of strategic reasoning. Most AI reasoning -- not all of it, but most of it until about 12 years ago -- was really about perfect information games like Othello, Checkers, Chess and Go.

And there has been tremendous progress. But these complete information, or perfect information, games don't really model real business situations very well. Most business situations are of imperfect information.

Know what you don’t know

So you don't know the other guy's resources, their goals and so on. You then need totally different algorithms for solving these games, or game-theoretic solutions that define what rational play is, or opponent exploitation techniques where you try to find out the opponent's mistakes and learn to exploit them.

So totally different techniques are needed, and this has way more applications in reality than perfect information games have.

Gardner: In business, you don't always know the rules. All the variables are dynamic, and we don't know the rationale or the reasoning behind competitors’ actions. People sometimes are playing offense, defense, or a little of both.

Before we dig in to how is this being applied in business circumstances, explain your proof of concept involving poker. Is it Five-Card Draw?

Heads-Up No-Limit Texas Hold'em has become the leading benchmark in the AI community.

Sandholm: No, we’re working on a much harder poker game called Heads-Up No-Limit Texas Hold'em as the benchmark. This has become the leading benchmark in the AI community for testing these application-independent algorithms for reasoning under imperfect information.

The algorithms have really nothing to do with poker, but we needed a common benchmark, much like the IC chip makers have their benchmarks. We compare progress year-to-year and compare progress across the different research groups around the world. Heads-Up No-limit Texas Hold'em turned out to be great benchmark because it is a huge game of imperfect information.

It has 10 to the 161 different situations that a player can face. That is one followed by 161 zeros. And if you think about that, it’s not only more than the number of atoms in the universe, but even if, for every atom in the universe, you have a whole other universe and count all those atoms in those universes -- it will still be more than that.

Gardner: This is as close to infinity as you can probably get, right?

Sandholm: Ha-ha, basically yes.

Gardner: Okay, so you have this massively complex potential data set. How do you winnow that down, and how rapidly does the algorithmic process and platform learn? I imagine that being reactive, creating a pattern that creates better learning is an important part of it. So tell me about the learning part.

Three part harmony

Sandholm: The learning part always interests people, but it's not really the only part here -- or not even the main part. We basically have three main modules in our architecture. One computes approximations of Nash equilibrium strategies using only the rules of the game as input. In other words, game-theoretic strategies.

That doesn’t take any data as input, just the rules of the game. The second part is during play, refining that strategy. We call that subgame solving.

Then the third part is the learning part, or the self-improvement part. And there, traditionally people have done what’s called opponent modeling and opponent exploitation, where you try to model the opponent or opponents and adjust your strategies so as to take advantage of their weaknesses.

However, when we go against these absolute best human strategies, the best human players in the world, I felt that they don't have that many holes to exploit and they are experts at counter-exploiting. When you start to exploit opponents, you typically open yourself up for exploitation, and we didn't want to take that risk. In the learning part, the third part, we took a totally different approach than traditionally is taken in AI.

We are letting the opponents tell us where the holes are in our strategy. Then, in the background, using supercomputing, we are fixing those holes.

We said, “Okay, we are going to play according to our approximate game-theoretic strategies. However, if we see that the opponents have been able to find some mistakes in our strategy, then we will actually fill those mistakes and compute an even closer approximation to game-theoretic play in those spots.”

One way to think about that is that we are letting the opponents tell us where the holes are in our strategy. Then, in the background, using supercomputing, we are fixing those holes.

All three of these modules run on the Bridges supercomputer at the Pittsburgh Supercomputing Center (PSC), for which the hardware was built by Hewlett Packard Enterprise (HPE).

HPC from HPE

Overcomes Barriers

To Supercomputing and Deep Learning

Gardner: Is this being used in any business settings? It certainly seems like there's potential there for a lot of use cases. Business competition and circumstances seem to have an affinity for what you're describing in the poker use case. Where are you taking this next?

Sandholm: So far this, to my knowledge, has not been used in business. One of the reasons is that we have just reached the superhuman level in January 2017. And, of course, if you think about your strategic reasoning problems, many of them are very important, and you don't want to delegate them to AI just to save time or something like that.

Now that the AI is better at strategic reasoning than humans, that completely shifts things. I believe that in the next few years it will be a necessity to have what I call strategic augmentation. So you can't have just people doing business strategy, negotiation, strategic pricing, and product portfolio optimization.

You are going to have to have better strategic reasoning to support you, and so it becomes a kind of competition. So if your competitors have it, or even if they don't, you better have it because it’s a competitive advantage.

Gardner: So a lot of what we're seeing in AI and machine learning is to find the things that the machines do better and allow the humans to do what they can do even better than machines. Now that you have this new capability with strategic reasoning, where does that demarcation come in a business setting? Where do you think that humans will be still paramount, and where will the machines be a very powerful tool for them?

Human modeling, AI solving

Sandholm: At least in the foreseeable future, I see the demarcation as being modeling versus solving. I think that humans will continue to play a very important role in modeling their strategic situations, just to know everything that is pertinent and deciding what’s not pertinent in the model, and so forth. Then the AI is best at solving the model.

That's the demarcation, at least for the foreseeable future. In the very long run, maybe the AI itself actually can start to do the modeling part as well as it builds a better understanding of the world -- but that is far in the future.

Gardner: Looking back as to what is enabling this, clearly the software and the algorithms and finding the right benchmark, in this case the poker game are essential. But with that large of a data set potential -- probabilities set like you mentioned -- the underlying computersystems must need to keep up. Where are you in terms of the threshold that holds you back? Is this a price issue that holds you back? Is it a performance limit, the amount of time required? What are the limits, the governors to continuing?

Sandholm: It's all of the above, and we are very fortunate that we had access to Bridges; otherwise this wouldn’t have been possible at all.  We spent more than a year and needed about 25 million core hours of computing and 2.6 petabytes of data storage.

This amount is necessary to conduct serious absolute superhuman research in this field -- but it is something very hard for a professor to obtain. We were very fortunate to have that computing at our disposal.

Gardner: Let's examine the commercialization potential of this. You're not only a professor at Carnegie Mellon, you’re a founder and CEO of a few companies. Tell us about your companies and how the research is leading to business benefits.

Superhuman business strategies

Sandholm: Let’s start with Strategic Machine, a brand-new start-up company, all of two months old. It’s already profitable, and we are applying the strategic reasoning technology, which again is application independent, along with the Libratus technology, the Lengpudashi technology, and a host of other technologies that we have exclusively licensed to Strategic Machine. We are doing research and development at Strategic Machine as well, and we are taking these to any application that wants us.

                                                                  HPC from HPE

Overcomes Barriers 
To Supercomputing and Deep Learning

Such applications include business strategy optimization, automated negotiation, and strategic pricing. Typically when people do pricing optimization algorithmically, they assume that either their company is a monopolist or the competitors’ prices are fixed, but obviously neither is typically true.

We are looking at how do you price strategically where you are taking into account the opponent’s strategic response in advance. So you price into the future, instead of just pricing reactively. The same can be done for product portfolio optimization along with pricing.

Let's say you're a car manufacturer and you decide what product portfolio you will offer and at what prices. Well, what you should do depends on what your competitors do and vice versa, but you don’t know that in advance. So again, it’s an imperfect-information game.

Gardner: And these are some of the most difficult problems that businesses face. They have huge billion-dollar investments that they need to line up behind for these types of decisions. Because of that pipeline, by the time they get to a dynamic environment where they can assess -- it's often too late. So having the best strategic reasoning as far in advance as possible is a huge benefit.

If you think about machine learning traditionally, it's about learning from the past. But strategic reasoning is all about figuring out what's going to happen in the future.

Sandholm: Exactly! If you think about machine learning traditionally, it's about learning from the past. But strategic reasoning is all about figuring out what's going to happen in the future. And you can marry these up, of course, where the machine learning gives the strategic reasoning technology prior beliefs, and other information to put into the model.

There are also other applications. For example, cyber security has several applications, such as zero-day vulnerabilities. You can run your custom algorithms and standard algorithms to find them, and what algorithms you should run depends on what the other opposing governments run -- so it is a game.

Similarly, once you find them, how do you play them? Do you report your vulnerabilities to Microsoft? Do you attack with them, or do you stockpile them? Again, your best strategy depends on what all the opponents do, and that's also a very strategic application.

And in upstairs blocks trading, in finance, it’s the same thing: A few players, very big, very strategic.

Gaming your own immune system

The most radical application is something that we are working on currently in the lab where we are doing medical treatment planning using these types of sequential planning techniques. We're actually testing how well one can steer a patient's T-cell population to fight cancers, autoimmune diseases, and infections better by not just using one short treatment plan -- but through sophisticated conditional treatment plans where the adversary is actually your own immune system.

Gardner: Or cancer is your opponent, and you need to beat it?

Sandholm: Yes, that’s right. There are actually two different ways to think about that, and they lead to different algorithms. We have looked at it where the actual disease is the opponent -- but here we are actually looking at how do you steer your own T-cell population.

Gardner: Going back to the technology, we've heard quite a bit from HPE about more memory-driven and edge-driven computing, where the analysis can happen closer to where the data is gathered. Are these advances of any use to you in better strategic reasoning algorithmic processing?

Algorithms at the edge

Sandholm: Yes, absolutely! We actually started running at the PSC on an earlier supercomputer, maybe 10 years ago, which was a shared-memory architecture. And then with Bridges, which is mostly a distributed system, we used distributed algorithms. As we go into the future with shared memory, we could get a lot of speedups.

We have both types of algorithms, so we know that we can run on both architectures. But obviously, the shared-memory, if it can fit our models and the dynamic state of the algorithms, is much faster.

Gardner: So the HPE Machine must be of interest to you: HPE’s advanced concept demonstration model, with a memory-driven architecture, photonics for internal communications, and so forth. Is that a technology you're keeping a keen eye on?

HPC from HPE

Overcomes Barriers 
To Supercomputing and Deep Learning

Sandholm: Yes. That would definitely be a desirable thing for us, but what we really focus on is the algorithms and the AI research. We have been very fortunate in that the PSC and HPE have been able to take care of the hardware side.

We really don’t get involved in the hardware side that much, and I'm looking at it from the outside. I'm trusting that they will continue to build the best hardware and maintain it in the best way -- so that we can focus on the AI research.

Gardner: Of course, you could help supplement the cost of the hardware by playing superhuman poker in places like Las Vegas, and perhaps doing quite well. 

It's unethical to pretend to be a human when you are not. The monetary opportunities in the business applications, are much bigger than what you could hope to make in poker anyway.

Sandholm: Actually here in the live game in Las Vegas they don't allow that type of computational support. On the Internet, AI has become a big problem on gaming sites, and it will become an increasing problem. We don't put our AI in there; it’s against their site rules. Also, I think it's unethical to pretend to be a human when you are not. The business opportunities, the monetary opportunities in the business applications, are much bigger than what you could hope to make in poker anyway.

Gardner: I’m afraid we’ll have to leave it there. We have been learning how Carnegie Mellon University researchers are using strategic reasoning advances and pertaining that to poker as a benchmark -- but clearly with a lot more runway in terms of other business and strategic reasoning benefits.

So a big thank you to our guest, Tuomas Sandholm, Professor at Carnegie Mellon University as well as Director of the Electronic Marketplace Lab there.

Sandholm: Thank you, my pleasure.

Gardner: And a big thank you to our audience as well for joining this BriefingsDirect Voice of the Customer digital transformation success story discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-sponsored interviews.

Thanks again for listening. Please pass this along to your IT community, and do come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Transcript of a discussion on how Carnegie Mellon University researchers are advancing strategic reasoning and machine learning capabilities using high performance computing. Copyright Interarbor Solutions, LLC, 2005-2017. All rights reserved.

You may also be interested in:

• Philips teams with HPE on ecosystem approach to improve healthcare informatics-driven outcome 
• Inside story: How Ormuco abstracts the concepts of private and public cloud across the globe
• How Nokia refactors the video delivery business with new time-managed IT financing models
• IoT capabilities open new doors for Miami telecoms platform provider Identidad IoT
• Inside story on developing the ultimate SDN-enabled hybrid cloud object storage environment
• How IoT and OT collaborate to usher in the data-driven factory of the future 
• DreamWorksAnimation crafts its next era of dynamic IT infrastructure

Philips Teams with HPE on Ecosystem Approach to Improve Healthcare Informatics Outcomes

Transcript of a discussion on how an ecosystem approach brings improved healthcare informatics outcomes thanks to using advanced big data and analytics.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Welcome to the next edition of the BriefingsDirect Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on digital transformation success stories. Please stay with us as we learn how agile businesses are fending off disruption -- in favor of innovation.

Our next business transformation use-case discussion focuses on how an ecosystem approach brings about improved healthcare informatics outcomes. We will now learn how a Philips Healthcare Informatics and Hewlett Packard Enterprise (HPE) partnership creates new solutions for the global healthcare market and provides better health outcomes for patients.

Heemskerk

Here to explain how companies tackle the complexity of solutions delivery in healthcare by using advanced big data and analytics is Martijn Heemskerk, Healthcare Informatics Ecosystem Director for Philips, based in Eindhoven, the Netherlands. Welcome, Martijn.

Martijn Heemskerk: Thank you for having me.

Gardner: Why are partnerships so important in healthcare informatics? Is it because there are clinical considerations combined with big data technology? Why are these types of solutions particularly dependent upon an ecosystem approach?

Partner up

Heemskerk: It’s exactly as you say, Dana. At Philips we are very strong at developing clinical solutions for our customers. But nowadays those solutions also require an IT infrastructure layer underneath to solve the total equation. As such, we are looking for partners in the ecosystem because we at Philips recognize that we cannot do everything alone. We need partners in the ecosystem that can help address the total solution -- or the total value proposition -- for our customers.

Gardner: I'm sure it varies from region to region, but is there a cultural barrier in some regard to bringing cutting-edge IT in particular into healthcare organizations? Or have things progressed to where technology and healthcare converge?

The level of healthcare and the type of solutions that you offer to different countries may vary. But many of the challenges that hospitals everywhere are going through are similar.

Heemskerk: Of course, there are some countries that are more mature than others. Therefore the level of healthcare and the type of solutions that you offer to different countries may vary. But in principle, many of the challenges that hospitals everywhere are going through are similar.

Some of the not-so-mature markets are also trying to leapfrog so that they can deliver different solutions that are up to par with the mature markets.

Gardner: Because we are hearing a lot about big data and edge computing these days, we are seeing the need for analytics at a distributed architecture scale. Please explain how big data changes healthcare.

Big data value add

Heemskerk: What is very interesting for big data is what happens if you combine it with value-based care. It's a very interesting topic. For example, nowadays, a hospital is not reimbursed for every procedure that it does in the hospital – the value is based more on the total outcome of how a patient recovers.

This means that more analytics need to be gathered across different elements of the process chain before reimbursement will take place. In that sense, analytics become very important for hospitals on how to measure on how things are being done efficiently, and determining if the costs are okay.

Gardner: The same data that can used to be more efficient can also be used for better healthcare outcomes and understanding the path of the disease, or for the efficacy of procedures, and so on. A great deal can be gained when data is gathered and used properly.

Heemskerk: That is correct. And you see, indeed, that there is much more data nowadays, and you can utilize it for all kind of different things.

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Gardner: Please help us understand the relationship between your organization and HPE. Where does your part of the value begin and end, and how does HPE fill their role on the technology side?

Healthy hardware relationships 

Heemskerk: HPE has been a highly valued supplier of Philips for quite a long time. We use their technologies for all kinds of different clinical solutions. For example, all of the hardware that we use for our back-end solutions or for advanced visualization is sourced by HPE. I am focusing very much on the commercial side of the game, so to speak, where we are really looking at how can we jointly go to market.

As I said, customers are really looking for one-stop shopping, a complete value proposition, for the challenges that they are facing. That’s why we partner with HPE on a holistic level.

Gardner: Does that involve bringing HPE into certain accounts and vice versa, and then going in to provide larger solutions together?

Heemskerk: Yes, that is exactly the case, indeed. We recognized that we are not so much focusing on problems related to just the clinical implications, and we are not just focusing on the problems that HPE is facing -- the IT infrastructure and the connectivity side of the value chain. Instead, we are really looking at the problems that the C-suite-level healthcare executives are facing.

How do you align all of your processes so that there is a more optimized process flow within the hospitals?

You can think about healthcare industry consolidation, for example, as a big topic. Many hospitals are now moving into a cluster or into a network and that creates all kinds of challenges, both on the clinical application layer, but also on the IT infrastructure. How do you harmonize all of this? How do you standardize all of your different applications? How do you make sure that hospitals are going to be connected? How do you align all of your processes so that there is a more optimized process flow within the hospitals?

By addressing these kinds of questions and jointly going to our customers with HPE, we can improve user experiences for the customers, we can create better services, we have optimized these solutions, and then we can deliver a lot of time savings for the hospitals as well.

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Gardner: We have certainly seen in other industries that if you try IT modernization without including the larger organization -- the people, the process, and the culture -- the results just aren’t as good. It is important to go at modernization and transformation, consolidation of data centers, for example, with that full range of inputs and getting full buy-in.

Who else makes up the ecosystem? It takes more than two players to make an ecosystem.

Heemskerk: Yes, that's very true, indeed. In this, system integrators also have a very important role. They can have an independent view on what would be the best solution to fit a specific hospital.

Of course, we think that the Philips healthcare solutions are quite often the best, jointly focused with the solutions from HPE, but from time to time you can be partnering with different vendors.

Besides that, we don't have all of the clinical applications. By partnering with other vendors in the ecosystem, sometimes you can enhance the solutions that we have to think about; such as 3D solutions and 3D printing solutions.

Gardner: When you do this all correctly, when you leverage and exploit an ecosystem approach, when you cover the bases of technology, finance, culture, and clinical considerations, how much of an impressive improvement can we typically see?

Saving time, money, and people

Heemskerk: We try to look at it customer by customer, but generically what we see is that there are really a lot of savings.

First of all, addressing standardization across the clinical application layer means that a customer doesn't have to spend a lot of money on training all of its hospital employees on different kinds of solutions. So that's already a big savings.

Secondly, by harmonizing and making better effective use of the clinical applications, you can drive the total cost of ownership down.

Thirdly, it means that on the clinical applications layer, there are a lot of efficiency benefits possible. For example, advanced analytics make it possible to reduce the time that clinicians or radiologists are spending on analyzing different kinds of elements, which also creates time savings.

Gardner: Looking more to the future, as technologies improve, as costs go down, as they typically do, as hybrid IT models are utilized and understood better -- where do you see things going next for the healthcare sector when it comes to utilizing technology, utilizing informatics, and improving their overall process and outcomes?

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Heemskerk: What for me would be very interesting is to see is if we can create some kind of a patient-centric data file for each patient. You see that consumers are increasingly engaged in their own health, with all the different devices like Fitbit, Jawbone, Apple Watch, etc. coming up. This is creating a massive amount of data. But there is much more data that you can put into such a patient-centric file, with the chronic diseases information now that people are being monitored much more, and much more often.

If you can have a chronological view of all of the different touch points that the patient has in the hospital, combined with the drugs the patient is using etc., and it’s all in a patient-centric file – it will be very interesting.

If you can have a chronological view of all of the different touch points that the patient has in the hospital, combined with the drugs that the patient is using etc., and you have that all in this patient-centric file -- it will be very interesting. And everything, of course, needs to be interconnected. Therefore, Internet of Things (IoT) technologies will become more important. And as the data is growing, you will have smarter algorithms that can also interpret that data – and so artificial intelligence (AI) will become much more important.

Gardner: I’m afraid we’ll have to leave it there. We have been exploring how an ecosystem approach brings improved healthcare information benefits. And we have also learned how a Philips Healthcare Informatics and Hewlett Packard Enterprise partnership combines forces to take create new solutions in the global healthcare field.

Please join me in thanking our guest, Martijn Heemskerk, Healthcare Informatics Ecosystem Director for Philips, based in Eindhoven, the Netherlands. Thank you, sir.

Heemskerk: Thank you, very much, Dana, for having me.

Gardner: And a big thank you as well to our audience for joining this latest BriefingsDirect Voice of the Customer digital transformation success story. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-sponsored interviews. Thanks again for listening, and do please come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Transcript of a discussion on how an ecosystem approach brings improved healthcare informatics outcomes thanks to using advanced big data and analytics. Copyright Interarbor Solutions, LLC, 2005-2017. All rights reserved.

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