Podcast: As enterprises face mounting hybrid IT complexity, new management solutions beckon
[Editor's note: This podcast was recorded on Nov. 6, 2017.]
As cloud adoption accelerates, businesses are struggling to manage all the fast-moving parts: legacy IT, private cloud, public cloud, software as a service, and multicloud. Part of the problem is a lack of multicloud management services to help IT leaders manage the growing complexity of transitioning to a sustainable hybrid IT model.
In this HPE Voice of the Analyst podcast, Dana Gardner of BriefingsDirect and Paul Teich, principal analyst at Tirias Research, explore solutions, including artificial intelligence and machine learning, as well as the vendors taking the lead in this emerging market.
Dana Gardner: Hello, and welcome to the BriefingsDirect Voice of the Analyst podcast series.
I’m Dana Gardner, principal analyst at Interarbor Solutions, your host and moderator. Join us as we hear from leading IT industry analysts and consultants on how to best make the hybrid IT journey to successful digital business transformation.
Our next interview examines how new machine learning and artificial intelligence (AI) capabilities are being applied to hybrid IT complexity challenges. We'll explore how mounting complexity and a lack of multicloud services management maturity must be solved in order for businesses to grow and thrive as digital enterprises.
Here to report on how companies and IT leaders are seeking new means to manage an increasingly complex transition to sustainable hybrid IT is Paul Teich, principal analyst at Tirias Research in Austin, Texas. Welcome, Paul.
Paul Teich, Tirias Research
Paul Teich: Hi, how are you, Dana?
Gardner: I’m great. You and I have appeared on a number of panels and videos over the years, but it’s nice to have you on my BriefingsDirect podcast. I have been looking forward to this.
Teich: Same here, thanks.
Gardner: Paul, there’s a lot of evidence that businesses are adopting cloud models at a rapid pace. There is also lingering concern about the complexity of managing so many fast-moving parts. We have legacy IT, private cloud, public cloud, software as a service (SaaS), and of course, multicloud. So, as someone who tracks technology and its consumption, how much has technology itself been tapped to manage this sprawl, if you will, across hybrid IT?
Teich: So far, not very much, mostly because of the early state of multicloud and the hybrid cloud business model. As you know, it takes a while for management technology to catch up with the actual compute technology and storage. So I think we are seeing that management is the tail of the dog—it’s getting wagged by the rest of it, and it just hasn’t caught up yet.
Gardner: Things have been moving so quickly with cloud computing that few organizations have had an opportunity to step back and examine what’s actually going on around them—never mind properly react to it. We really are playing catch up.
Teich: As we look at the options available, the cloud giants—the public cloud services—don’t have much incentive to work together. So you are looking at a market where there will be third parties stepping in to help manage multicloud environments, and there’s a lag time between having those services available and having the cloud services available and then seeing the third-party management solution step in.
Gardner: It’s natural to see that a specific cloud environment—whether it’s purely public like AWS or a hybrid like Microsoft Azure and Azure Stack—want to help their customers, but they want to help their customers all get to their solutions first and foremost. It’s a natural thing. We have seen this before in technology.
There are not that many organizations willing to step into the neutral position of being ecumenical, of saying they want to help the customer first, manage it all from the first.
As we look to how this might unfold, it seems to me that the previous models of IT management—agent-based, single pane of glass, and unfortunately still in some cases, spreadsheets and Post-It notes—have been brought to bear on this. But we might be in a different ball game, Paul, with hybrid IT, that there’s just too many moving parts, too much complexity, and that we might need to look at data-driven approaches. What is your take on that?
Teich: I think that’s exactly correct. One of the jokes in the industry right now is if you want to find your stranded instances in the cloud, cancel your credit card and AWS or Microsoft will be happy to notify you of all of the instances that you are no longer paying for because your credit card expired. It’s hard to keep track of this, because we don’t have adequate tools yet.
When you are an IT manager and you have a lot of folks on public cloud services, you don't have a full picture.
That single pane of glass, looking at a lot of data and information, is soon overloaded. When you are an IT manager, you are at a midsized or a large corporation, you have a lot of folks paying out of pocket right now, slapping a credit card down on public cloud services, so you don’t have a full picture. Where you do have a picture, there are so many moving parts.
I think we have to get past having a screen full of data, a screen full of information, and to a point where we have insight. And that is going to require a new generation of tools, probably borrowing from some of the machine learning evolution that’s happening now in pattern analytics.
Gardner: The timing in some respects couldn’t be better, right? Just as we are facing this massive problem of complexity of volume and velocity in managing IT across a hybrid environment, we have some of the most powerful and cost-effective means to deal with big data problems just like that.
Life in the infrastructure
Paul, before we go further let’s hear about you and your organization, and tell us, if you would, what a typical day is like in the life of Paul Teich?
Teich: At Tirias Research, we are boutique industry analysts. By boutique we mean there are three of us—three principal analysts; we have just added a few senior analysts. We are close to the metal. We live in the infrastructure. We are all former engineers and/or product managers. We are very familiar with deep technology.
My day tends to be first, a lot of reading. We look at a lot of chips, we look at a lot of service-level information, and our job is to, at a very fundamental level, take very complex products and technologies and surface them to business decision-makers, IT decision-makers, folks who are trying to run lines of business (LOB) and make a profit. So we do the heavy lifting on why new technology is important, disruptive, and transformative.
Gardner: Thanks. Let’s go back to this idea of data-driven and analytical values as applied to hybrid IT management and complexity. If we can apply AI and machine learning to solve business problems outside of IT—in such verticals as retail, pharmaceutical, transportation—with the same characteristics of data volume, velocity, and variety, why not apply that to IT? Is this a case of the cobbler’s kids having no shoes? You would think that IT would be among the first to do this.
Dig deep, gain insight
Teich: The cloud giants have already implemented systems like this because of necessity. So they have been at the front end of that big data mantra of volume, velocity, and all of that.
To successfully train for the new pattern recognition analytics, especially the deep learning stuff, you need a lot of data. You can’t actually train a system usefully without presenting it with a lot of use cases.
The public clouds have this data. They are operating social media services, large retail storefronts, and e-tail, for example. As the public clouds became available to enterprises, the IT management problem ballooned into a big data problem. I don’t think it was a big data problem five or 10 years ago, but it is now.
That’s a big transformation. We haven’t actually internalized what that means operationally when your internal IT department no longer runs all of your IT jobs anymore.
We are generating big data, and that means we need big data tools to go analyze it and to get that relevant insight.
That’s the biggest sea change—we are generating big data in the course of managing our IT infrastructure now, and that means we need big data tools to go analyze it and to get that relevant insight. It’s too much data flowing by for humans to comprehend in real time.
Gardner: And, of course, we are also talking about islands of such operational data. You might have a lot of data in your legacy operations. You might have tier-one apps that you are running on older infrastructure, and you are probably happy to do that. It might be very difficult to transition those specific apps into newer operating environments.
You also have multiple SaaS and cloud data repositories and logs. There’s also not only the data within those apps, but there’s the metadata as to how those apps are running in clusters and what they are doing as a whole. It seems to me that not only would you benefit from having a comprehensive data and analytics approach for your IT operations, but you might also have a workflow and process business benefit by being an uber analyst, by being on top of all of these islands of operational data.
To me, moving toward a comprehensive intelligence and data analysis capability for IT is the gift that keeps giving. You would then be able to also provide insight for an uber approach to processes across your entire organization—across the supply chains, across partner networks, and back to your customers. Paul, do you also see that there’s an ancillary business benefit to having that data analysis capability, and not ceding it to your cloud providers?
Manage data, improve workflow
Teich: I do. At one end of the spectrum, it’s simply what do you need to do to keep the lights on, where is your data, all of it, in the various islands and collections and the data you are sharing with your supply chain as well. Where is the processing that you can apply to that data? Increasingly, I think, we are looking at a world in which the location of the stored data is more important than the processing power. The management of all the data you have needs to segue into visible workflows.
We have processing power pretty much everywhere now. What’s key is moving data from place to place and setting up the connections to acquire it. It means that the management of all the data you have needs to segue into visible workflows.
Once I know what I have, and I am managing it at a baseline effectively, then I can start to improve my processes. Then I can start to get better workflows, internally as well as across my supply chain. But I think at first it’s simply, “What do I have going on right now?”
As an IT manager, how can I rein in some of these credit card instances, credit card storage on the public clouds, and put that all into the right mix. I have to know what I know first—then I can start to streamline. Then I can start to control my costs. Does that make sense?
Gardner: Yes, absolutely. And how can you know which people you want to give even more credit to on their credit cards—and let them do more of what they are doing? It might be very innovative, and it might be very cost-effective. There might also be those wasting money, spinning their wheels, repaving cow paths, over and over again.
If you don’t have the ability to make those decisions with insight, without the visibility, and then further analyze it as to how best to go about it—it seems to me a no-brainer.
It also comes at an auspicious time as IT is trying to re-factor its value to the organization. If in fact they are no longer running servers and networks and keeping the trains running on time, they have to start being more in the business of defining what trains should be running and then how to make them the best business engines, if you will.
If IT departments needs to rethink their role and step up their game, then they need to use technologies like advanced hybrid IT management from vendors with a neutral perspective. Then they become the overseers of operations at a fundamentally different level.
Data revelation, not revolution
IT needs to keep a handle on costs, so you can understand which jobs are running where and how much more capacity you need.
Teich: I think that’s right. It’s evolutionary stuff. I don’t think it’s revolutionary. I think that in the same way you add servers to a virtual machine farm, as your demand increases, as your baseline demand increases, IT needs to keep a handle on costs so you can understand which jobs are running where and how much more capacity you need.
One of the things they are missing with random access to the cloud is bulk purchasing. And so at a very fundamental level, helping your organization manage which clouds you are spending on by aggregating the purchase of storage, aggregating the purchase of compute instances to get just better buying power, doing price arbitrage when you can—to me, those are fundamental qualities of IT going forward in a multicloud environment.
They are extensions of where we are today; it just doesn’t seem like it yet. They have always added new servers to increasing internal capacity, and this is just the next evolutionary step.
Gardner: It certainly makes sense that you would move as maturity occurs in any business function toward that orchestration, automation and optimization, rather than simply getting the parts in place. What you are describing is that IT is becoming more like a procurement function and less like a building, architecture, or construction function, which is just as powerful.
Not many people can make those hybrid IT procurement decisions without knowing a lot about the technology. Someone with just business acumen can’t walk in and make these decisions. I think this is an opportunity for IT to elevate itself and become even more essential to the businesses.
Teich: The opportunity is a lot like the Sabre airline scheduling system that nearly every airline uses now. That’s a fundamental capability for doing business, and it’s separate from the technology of Sabre. It’s the ability to schedule—people and airplanes—and it’s a lot like scheduling storage and jobs on compute instances. So I think there will be this step.
But to go back to the technology versus procurement, I think some element of that has always existed in IT in terms of dealing with vendors and doing the volume purchases on one side, but also having some architect know how to compose the hardware and the software infrastructure to serve those applications.
Connect the clouds
We’re simply translating that now into a multicloud architecture. How do I connect those pieces? What network capacity do I need to buy? What kind of storage architectures do I need? I don’t think that all goes away. It becomes far more important as you look at—for example, AWS as a very large bag of services. It’s very powerful. You can assemble it in any way you want, but in some respect, that’s like programming in C. You have all the power of assembly language and all the danger of assembly language, because you can walk up in the memory and delete stuff, and so, you have to have architects who know how to build a service that’s robust, that won’t go down, that serves your application most efficiently, and all of those things are still hard to do.
So, architecture and purchasing are both still necessary. They don’t go away. I think the important part is that the orchestration part now becomes as important as deploying a service on the side of infrastructure because you’ve got multiple sets of infrastructure.
Gardner: For hybrid IT, it really has to be an enlightened procurement, not just blind procurement. And the people in the trenches that are just buying these services—whether the developers or operations folks—they don’t have that oversight, that view of the big picture to make those larger decisions about optimization of purchasing and business processes.
That gets us back to some of our earlier points of, what are the tools, what are the management insights that these individuals need in order to make those decisions? Like with Sabre, where they are optimizing to fill every hotel room or every airplane seat, we’re going to want in hybrid IT to fill every socket, right? We’re going to want all that bare metal and all those virtualization instances to be fully optimized, whether it’s your cloud or somebody else’s.
It seems to me that there is an algorithmic approach eventually, right? Somebody is going to need to be the keeper of that algorithm as to how this all operates. But you can’t program that algorithm if you don’t have the uber insights into what’s going on, and what works and what doesn’t.
What’s the next step, Paul, in terms of the technology catching up to the management requirements in this new hybrid IT complex environment?
Teich: People can develop some of that experience on a small scale, but there are so many dimensions to managing a multicloud, hybrid IT infrastructure business model. It’s throwing off all of this metadata for performance and efficiency. It’s ripe for machine learning.
We're moving so fast right now that if you are an organization of any size, machine learning has to come into play to help you get better economies of scale.
In a strong sense, we’re moving so fast right now that if you are an organization of any size, machine learning has to come into play to help you get better economies of scale. It’s just going to be looking at a bigger picture; it’s going to be managing more variables and learning across a lot more data points than a human can possibly comprehend.
We are at this really interesting point in the industry where we are getting deep-learning approaches that are coming online cost effectively; they can help us do that. They have a little while to go before they are fully mature. But IT organizations that learn to take advantage of these systems now are going to have a head start, and they are going to be more efficient than their competitors.
Gardner: At the end of the day, if you’re all using similar cloud services, then that differentiation between your company and your competitor is in how well you utilize and optimize those services. If the baseline technologies are becoming commoditized, then optimization—that algorithm-like approach to smartly moving workloads and data, and providing consumption models that are efficiency-driven—that’s going to be the difference between a 1 percent margin and a 5 percent margin over time.
The deep-learning difference
Teich: The important part to remember is that these machine-training algorithms are somewhat new, so there are several challenges with deploying them. First is the transparency issue. We don’t quite yet know how a deep-learning model makes specific decisions. We can’t point to one aspect and say that aspect is managing the quality of our AWS services, for example. The model obscures data.
We can’t yet verify the results of these models. We know they are being efficient and fast, but we can’t verify that the model is as efficient as it could possibly be. There is room for improvement over the next few years. As the models get better, they’ll leave less money on the table.
We’re also validating that when you build a machine-learning model that it’s covering all the situations you want it to cover. You need an audit trail for specific sets of decisions, especially with data that is subject to regulatory constraints. You need to know why you made decisions.
So the net is, once you are training a machine-learning model, you have to keep retraining it over time. Your model is not going to do the same thing as your competitor's model.
There is a lot of room for differentiation, a lot of room for learning. You just have to go into it with your eyes open that, yeah, occasionally things will go sideways. Your model might do something unexpected, and you just have to be prepared for that. We’re still in the early days of machine learning.
Gardner: You raise an interesting point, Paul, because even as the baseline technology services in the multicloud era become commoditized, you’re going to have specific, unique, and custom approaches to your own business’ management.
Your hybrid IT optimization is not going to be like that of any other company. I think getting that machine learning capability attuned to your specific hybrid IT panoply of resources and assets is going to be a gift that keeps giving. Not only will you run your IT better, you will run your business better. You’ll be fleet and agile.
If some risk arises—whether it’s a cybersecurity risk, a natural disaster risk, a business risk of unintended or unexpected changes in your supply chain or in your business environment—you’re going to be in a better position to react. You’re going to have your eyes to the ground, you’re going to be well tuned to your specific global infrastructure, and you’ll be able to make good choices. So I am with you. I think machine learning is essential, and the sooner you get involved with it, the better.
Before we sign off, who are the vendors and some of the technologies that we will look to in order to fill this apparent vacuum on advanced hybrid IT management? It seems to me that traditional IT management vendors would be a likely place to start.
Teich: They are a likely place to start. All of them are starting to say something about being in a multicloud environment, about being in a multicloud-vendor environment. They are already finding themselves there with virtualization, and the key is they have recognized that they are in a multi-vendor world.
There are some start-ups, and I can’t name them specifically right now. But a lot of folks are working on this problem of how do I manage hybrid IT: in-house IT and multicloud orchestration—a lot of work going on there. We haven’t seen a lot of it publicly yet, but there is a lot of venture capital being placed.
I think this is the next step, just like PCs came in the office, smartphones came in the office as we move from server farms to the clouds, going from cloud to multicloud—it’s attracting a lot of attention. The hard part right now is nailing whom to place your faith in. The name brands that people are buying their internal IT from right now are probably good near-term bets. As the industry gets more mature, we’ll have to see what happens.
Gardner: We did hear a vision described on this from Hewlett Packard Enterprise back in June at their Discover event in Las Vegas. I’m expecting to hear quite a bit more on something they’ve been calling New Hybrid IT Stack that seems to possess some of the characteristics we’ve been describing, such as broad visibility and management.
So at least one of the long-term IT management vendors is looking in this direction. That’s a place I’m going to be focusing on, wondering what the competitive landscape is going to be, and if HPE is going to be in the leadership position on hybrid IT management.
Teich: Actually, I think HPE is the only company I’ve heard from so far talking at that level. Everybody is voicing some opinion about it, but from what I’ve heard, it does sound like a very interesting approach to the problem.
Microsoft actually constrained their view on Azure Stack to a very small set of problems and is actively saying, “No, I don’t.” If you’re looking at doing virtual machine migration and taking advantage of multicloud for general-purpose solutions, it’s probably not something that you want to do yet. It was very interesting for me then to hear about the HPE project New Hybrid IT Stack and what HPE is planning to do there.
Gardner: For Microsoft, the more automated and constrained they can make it, the more likely you’d be susceptible or tempted to want to just stay within an Azure and/or Azure Stack environment. So I can appreciate why they would do that.
Before we sign off, one other area I’m going to be keeping my eyes on is around orchestration of containers, Kubernetes, in particular. If you follow orchestration of containers and container usage in multicloud environments, that’s going to be a harbinger of how the larger hybrid IT management demands are going to go as well. So a canary in the coal mine, if you will, as to where things could get very interesting very quickly.
The place to be
Teich: Absolutely. And I point out that the Linux Foundation’s CloudNativeCon in early December 2017 looks like the place to be—with nearly everyone in the server infrastructure community and cloud infrastructure communities signing on. Part of the interest is in basically interchangeable container services. We’ll see that become much more important. So that sleepy little technical show is going to be invaded by “suits,” this year, and we’re paying a lot of attention to it.
Gardner: Yes, I agree. I’m afraid we’ll have to leave it there. We’ve been exploring how mounting complexity and a lack of multicloud services management maturity must be solved in order for businesses to grow and thrive as digital enterprises. And we’ve learned how companies and IT leaders are seeking new means to manage an increasingly complex transition to sustainable hybrid IT.
We’ve also talked about how artificial intelligence—and specifically, machine learning—will be an important element to solve some of these issues. And we’ve talked about some of the early days of the larger vendors coming to the market with solutions.
Please join me in thanking our guest, Paul Teich, principal analyst at Tirias Research in Austin, Texas. Thank you so much, Paul.
Teich: Thanks, Dana. I very much appreciate it.
Gardner: Paul, how can our listeners and readers best follow you to gain more of your excellent insights?
Gardner: A big thank you to our audience as well for joining this BriefingsDirect Voice of the Analyst discussion on how to best manage the hybrid IT journey to digital business transformation.
I’m Dana Gardner, principal analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-sponsored interviews. Follow me on Twitter at @Dana_Gardner, and find more hybrid IT-focused podcasts at briefingsdirect.com. Thanks again for joining. Please pass this on to your IT community if you found it valuable, and do come back next time.
This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.