Using AI to solve data and IT complexity -- and thereby better enable AI

The next BriefingsDirect data disruption discussion focuses on why the rising tidal wave of data must be better managed, and how new tools are emerging to bring artificial intelligence (AI) to the rescue. 

Stay with us to explore how the latest AI innovations improve both data and services management across a cloud deployment continuum -- and in doing so set up an even more powerful way for businesses to exploit AI.

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

To learn how AI will help conquer complexity to allow for higher abstractions of benefits from across all sorts of analysis, we welcome Rebecca Lewington, Senior Manager of Innovation Marketing at Hewlett Packard Enterprise (HPE). The interview is conducted by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: We have been talking about massive amounts of data for quite some time. What’s new about data buildup that requires us to look to AI for help?

Lewington: Partly it is the sheer amount of data. IDC’s Data Age Study predicts the global data sphere will be 175 zettabytes by 2025, which is a rather large number. That’s what, 1 and 21 zeros? But we have always been in an era of exploding data.

Lewington

Yet, things are different. One, it’s not just the amount of data; it’s the number of sources the data comes from. We are adding in things like mobile devices, and we are connecting factories’ operational technologies to information technology (IT). There are more and more sources.

Also, the time we have to do something with that data is shrinking to the point where we expect everything to be real-time or you are going to make a bad decision. An autonomous car, for example, might do something bad. Or we are going to miss a market or competitive intelligence opportunity.

So it’s not just the amount of data -- but what you need to do with it that is challenging.

Gardner: We are also at a time when Al and machine learning (ML) technologies have matured. We can begin to turn them toward the data issue to better exploit the data. What is new and interesting about AI and ML that make them more applicable for this data complexity issue?

Data gets smarter with AI

Lewington: A lot of the key algorithms for AI were actually invented long ago in the 1950s, but at that time, the computers were hopeless relative to what we have today; so it wasn’t possible to harness them.

For example, you can train a deep-learning neural net to recognize pictures of kittens. To do that, you need to run millions of images to train a working model you can deploy. That’s a huge, computationally intensive task that only became practical a few years ago. But now that we have hit that inflection point, things are just taking off.

Gardner: We can begin to use machines to better manage data that we can then apply to machines. Does that change the definition of AI?

Lewington: The definition of AI is tricky. It’s malleable, depending on who you talk to. For some people, it’s anything that a human can do. To others, it means sophisticated techniques, like reinforcement learning and deep learning.

How to Remove Complexity
From Multicloud and Hybrid IT

One useful definition is that AI is what you use when you know what the answer looks like, but not how to get there.

Traditional analytics effectively does at scale what you could do with pencil and paper. You could write the equations to decide where your data should live, depending on how quickly you need to access it.

But with AI, it’s like the kittens example. You know what the answer looks like, it’s trivial for you to look at the photograph and say, “That is a cat in the picture.” But it’s really, really difficult to write the equations to do it. But now, it’s become relatively easy to train a black box model to do that job for you.

Gardner: Now that we are able to train the black box, how can we apply that in a practical way to the business problem that we discussed at the outset? What is it about AI now that helps better manage data? What's changed that gives us better data because we are using AI?

The heart of what makes AI work is good data; the right data, in the right place, with the right properties you can use to train a model, which you can then feed new data into to get results that you couldn't get otherwise.

Lewington: It’s a circular thing. The heart of what makes AI work is good data; the right data, in the right place, with the right properties you can use to train a model, which you can then feed new data into to get results that you couldn’t get otherwise.

Now, there are many ways you can apply that. You can apply it to the trivial case of the cat we just talked about. You can apply it to helping a surgeon review many more MRIs, for example, by allowing him to focus on the few that are borderline, and to do the mundane stuff for him.

But, one of the other things you can do with it is use it to manipulate the data itself. So we are using AI to make the data better -- to make AI better.

Gardner: Not only is it circular, and potentially highly reinforcing, but when we apply this to operations in IT -- particularly complexity in hybrid cloud, multicloud, and hybrid IT -- we get an additional benefit. You can make the IT systems more powerful when it comes to the application of that circular capability -- of making better AI and better data management.

AI scales data upward and outward

Lewington: Oh, absolutely. I think the key word here is scale. When you think about data -- and all of the places it can be, all the formats it can be in -- you could do it yourself. If you want to do a particular task, you could do what has traditionally been done. You can say, “Well, I need to import the data from here to here and to spin up these clusters and install these applications.” Those are all things you could do manually, and you can do them for one-off things.

But once you get to a certain scale, you need to do them hundreds of times, thousands of times, even millions of times. And you don’t have the humans to do it. It’s ridiculous. So AI gives you a way to augment the humans you do have, to take the mundane stuff away, so they can get straight to what they want to do, which is coming up with an answer instead of spending weeks and months preparing to start to work out the answer.

Gardner: So AI directed at IT, what some people call AIOps could be an accelerant to this circular advantageous relationship between AI and data? And is that part of what you are doing within the innovation and research work at HPE?

Lewington: That’s true, absolutely. The mission of Hewlett Packard Labs in this space is to assist the rest of the company to create more powerful, more flexible, more secure, and more efficient computing and data architectures. And for us in Labs, this tends to be a fairly specific series of research projects that feed into the bigger picture.

For example, we are now doing the Deep Learning Cookbook, which allows customers to find out ahead of time exactly what kind of hardware and software they are going to need to get to a desired outcome. We are automating the experimenting process, if you will.

And, as we talked about earlier, there is the shift to the edge. As we make more and more decisions -- and gain more insights there, to where the data is created -- there is a growing need to deploy AI at the edge. That means you need a data strategy to get the data in the right place together with the AI algorithm, at the edge. That’s because there often isn’t time to move that data into the cloud before making a decision and waiting for the required action to return.

Once you begin doing that, once you start moving from a few clouds to thousands and millions of endpoints, how do you handle multiple deployments? How do you maintain security and data integrity across all of those devices? As researchers, we aim to answer exactly those questions.

And, further out, we are looking to move the natural learning phase itself to the edge, to do the things we call swarm learning, where devices learn from their environment and each other, using a distributed model that doesn’t use a central cloud at all.

Gardner: Rebecca, given your title is Innovation Marketing Lead, is there something about the very nature of innovation that you have come to learn personally that’s different than what you expected? How has innovation itself changed in the past several years?

Innovation takes time and space 

Lewington: I began my career as a mechanical engineer. For many years, I was offended by the term innovation process, because that’s not how innovation works. You give people the space and you give them the time and ideas appear organically. You can’t have a process to have ideas. You can have a process to put those ideas into reality, to wean out the ones that aren’t going to succeed, and to promote the ones that work.

How to Better Understand
What AI Can do For Your Business

But the term innovation process to me is an oxymoron. And that’s the beautiful thing about Hewlett Packard Labs. It was set up to give people the space where they can work on things that just seem like a good idea when they pop up in their heads. They can work on these and figure out which ones will be of use to the broader organization -- and then it’s full steam ahead.

Gardner: It seems to me that the relationship between infrastructure and AI has changed. It wasn’t that long ago when we thought of business intelligence (BI) as an application -- above the infrastructure. But the way you are describing the requirements of management in an edge environment -- of being able to harness complexity across multiple clouds and the edge -- this is much more of a function of the capability of the infrastructure, too. Is that how you are seeing it, that only a supplier that’s deep in its infrastructure roots can solve these problems? This is not a bolt-on benefit.

Lewington: I wouldn’t say it’s impossible as a bolt-on; it’s impossible to do efficiently and securely as a bolt-on. One of the problems with AI is we are going to use a black box; you don’t know how it works. There were a number of news stories recently about AIs becoming corrupted, biased, and even racist, for example. Those kinds of problems are going to become more common.

And so you need to know that your systems maintain their integrity and are not able to be breached by bad actors. If you are just working on the very top layers of the software, it’s going to be very difficult to attest that what’s underneath has its integrity unviolated.

If you are someone like HPE, which has its fingers in lots of pies, either directly or through our partners, it’s easier to make a more efficient solution.

You need to know that your systems maintain their integrity and are not able to be breached by bad actors. If you are just working on the very top layers of the software, it's going to be very difficult to attest that what's underneath has its integrity unviolated.

Gardner: Is it fair to say that AI should be a new core competency, for not only data scientists and IT operators, but pretty much anybody in business? It seems to me this is an essential core competency across the board.

Lewington: I think that's true. Think of AI as another layer of tools that, as we go forward, becomes increasingly sophisticated. We will add more and more tools to our AI toolbox. And this is one set of tools that you just cannot afford not to have.

Gardner: Rebecca, it seems to me that there is virtually nothing within an enterprise that won't be impacted in one way or another by AI.

Lewington: I think that’s true. Anywhere in our lives where there is an equation, there could be AI. There is so much data coming from so many sources. Many things are now overwhelmed by the amount of data, even if it’s just as mundane as deciding what to read in the morning or what route to take to work, let alone how to manage my enterprise IT infrastructure. All things that are rule-based can be made more powerful, more flexible, and more responsive using AI.

Gardner: Returning to the circular nature of using AI to make more data available for AI -- and recognizing that the IT infrastructure is a big part of that -- what are doing in your research and development to make data services available and secure? Is there a relationship between things like HPE OneView and HPE OneSphere and AI when it comes to efficiency and security at scale?

Let the system deal with IT 

Lewington: Those tools historically have been rules-based. We know that if a storage disk gets to a certain percentage full, we need to spin up another disk -- those kinds of things. But to scale flexibly, at some point that rules-based approach becomes unworkable. You want to have the system look after itself, to identify its own problems and deal with them.

Including AI techniques in things like HPE InfoSight, HPE Clearpath, and network user identity behavior software on the HPE Aruba side allows the AI algorithms to make those tools more powerful and more efficient.

You can think of AI here as another class of analytics tools. It’s not magic, it’s just a different and better way of doing IT analytics. The AI lets you harness more difficult datasets, more complicated datasets, and more distributed datasets.

Gardner: If I’m an IT operator in a global 2000 enterprise, and I’m using analytics to help run my IT systems, what should I be thinking about differently to begin using AI -- rather than just analytics alone -- to do my job better?

Lewington: If you are that person, you don’t really want to think about the AI. You don’t want the AI to intrude upon your consciousness. You just want the tools to do your job.

For example, I may have 1,000 people starting a factory in Azerbaijan, or somewhere, and I need to provision for all of that. I want to be able to put on my headset and say, “Hey, computer, set up all the stuff I need in Azerbaijan.” You don’t want to think about what’s under the hood. Our job is to make those tools invisible and powerful.

Composable, invisible, and insightful 

Gardner: That sounds a lot like composability. Is that another tangent that HPE is working on that aligns well with AI?

Lewington: It would be difficult to have AI be part of the fabric of an enterprise without composability, and without extending composability into more dimensions. It’s not just about being able to define the amount of storage and computer networking with a line of code, it’s about being able to define the amount of memory, where the data is, where the data should be, and what format the data should be in. All of those things – from the edge to cloud – need to be dimensions in composability.

How to Achieve Composability
Across Your Datacenter

You want everything to work behind the scenes for you in the best way with the quickest results, with the least energy, and in the most cost-effective way possible. That’s what we want to achieve -- invisible infrastructure.

Gardner: We have been speaking at a fairly abstract level, but let’s look to some examples to illustrate what we’re getting at when we think about such composability sophistication.

Do you have any concrete examples or use cases within HPE that illustrate the business practicality of what we’ve been talking about?

Lewington: Yes, we have helped a tremendous number of customers either get started with AI in their operations or move from pilot to volume use. A couple of them stand out. One particular manufacturing company makes electronic components. They needed to improve the yields in their production lines, and they didn’t know how to attack the problem. We were able to partner with them to use such things as vision systems and photographs from their production tools to identify defects that only could be picked up by a human if they had a whole lot of humans watching everything all of the time.

This gets back to the notion of augmenting human capabilities. Their machines produce terabytes of data every day, and it just gets turned away. They don’t know what to do with it.

We began running some research projects with them to use some very sophisticated techniques, visual autoencoders, that allow you, without having a training set, to characterize a production line that is performing well versus one that is on the verge of moving away from the sweet spot. Those techniques can fingerprint a good line and also identify when the lines go just slightly bad. In that case, a human looking at line would think it was working just perfectly.

This takes the idea of predictive maintenance further into what we call prescriptive maintenance, where we have a much more sophisticated view into what represents a good line and what represents a bad line. Those are couple of examples for manufacturing that I think are relevant.

Gardner: If I am an IT strategist, a Chief Information Officer (CIO) or a Chief Technology Officer (CTO), for example, and I’m looking at what HPE is doing -- perhaps at the HPE Discover conference -- where should I focus my attention if I want to become better at using AI, even if it’s invisible? How can I become more capable as an organization to enable AI to become a bigger part of what we do as a company?

The new company man is AI

Lewington: For CIOs, their most important customers these days may be developers and increasingly data scientists, who are basically developers working with training models as opposed to programs and code. They don’t want to have to think about where that data is coming from and what it’s running on. They just want to be able to experiment, to put together frameworks that turn data into insights.

It’s very much like the programming world, where we’ve gradually abstracted things from bare-metal, to virtual machines, to containers, and now to the emerging paradigm of serverless in some of the walled-garden public clouds. Now, you want to do the same thing for that data scientist, in an analogous way.

Today, it’s a lot of heavy lifting, getting these things ready. It’s very difficult for a data scientist to experiment. They know what they want. They ask for it, but it takes weeks and months to set up a system so they can do that one experiment. Then they find it doesn’t work and move on to do something different. And that requires a complete re-spin of what’s under the hood.

Now, using things like software from the recent HPE BlueData acquisition, we can make all of that go away. And so the CIO’s job becomes much simpler because they can provide their customers the tools they need to get their work done without them calling up every 10 seconds and saying, “I need a cluster, I need a cluster, I need a cluster.”

That’s what a CIO should be looking for, a partner that can help them abstract complexity away, get it done at scale, and in a way that they can both afford and that takes the risk out. This is complicated, it’s daunting, and the field is changing so fast.

Gardner: So, in a nutshell, they need to look to the innovation that organizations like HPE are doing in order to then promulgate more innovation themselves within their own organization. It’s an interesting time.

Containers contend for the future 

Lewington: Yes, that’s very well put. Because it’s changing so fast they don’t just want a partner who has the stuff they need today, even if they don’t necessarily know what they need today. They want to know that the partner they are working with is working on what they are going to need five to 10 years down the line -- and thinking even further out. So I think that’s one of the things that we bring to the table that others can’t.

Gardner: Can give us a hint as to what some of those innovations four or five years out might be? How should we not limit ourselves in our thinking when it comes to that relationship, that circular relationship between AI, data, and innovation?

Lewington: It was worth coming to HPE Discover in June, because we talked about some exciting new things around many different options. The discussion about increasing automation abstractions is just going to accelerate.

We are going to get to the point where using containers seems as complicated as bare-metal today and that's really going to help simplify the whole data pipelines thing.

For example, the use of containers, which have a fairly small penetration rate across enterprises, is at about 10 percent adoption today because they are not the simplest thing in the world. But we are going to get to the point where using containers seems as complicated as bare-metal today and that’s really going to help simplify the whole data pipelines thing.

Beyond that, the elephant in the room for AI is that model complexity is growing incredibly fast. The compute requirements are going up, something like 10 times faster than Moore’s Law, even as Moore’s Law is slowing down.

We are already seeing an AI compute gap between what we can achieve and what we need to achieve -- and it’s not just compute, it’s also energy. The world’s energy supply is going up, can only go up slowly, but if we have exponentially more data, exponentially more compute, exponentially more energy, and that’s just not going to be sustainable.

So we are also working on something called Emergent Computing, a super-energy-efficient architecture that moves data around wherever it needs to be -- or not move data around but instead bring the compute to the data. That will help us close that gap.

How to Transform
The Traditional Datacenter

And that includes some very exciting new accelerator technologies: special-purpose compute engines designed specifically for certain AI algorithms. Not only are we using regular transistor-logic, we are using analog computing, and even optical computing to do some of these tasks, yet hundreds of times more efficiently and using hundreds of times less energy. This is all very exciting stuff, for a little further out in the future.

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

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How IT can fix the broken employee experience

The next BriefingsDirect intelligent workspaces discussion explores how businesses are looking to the latest digital technologies to transform how employees work.

There is a tremendous amount of noise, clutter, and distraction in the scattershot, multi-cloud workplace of today -- and it’s creating confusion and frustration that often pollute processes and hinder innovative and impactful work.

We’ll now examine how IT can elevate the game of sorting through apps, services, data, and delivery of simpler, more intelligent experiences that enable people -- in any context -- to work on relevancy and consistently operate at their informed best. 

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

To illustrate new paths to the next generation of higher productivity work, please welcome Marco Stalder, Team Leader of Citrix Workspace Services at Bechtle AG, one of Europe's leading IT providers, and Tim Minahan, Executive Vice President of Strategy and Chief Marketing Officer at Citrix. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tim, improving the employee experience has become a hot topic, with billions of productivity dollars at stake. Why has how workers do or don't do their jobs well become such a prominent issue?

Minahan: The simple answer is the talent crunch. Just about everywhere you look, workforce, management, and talent acquisition have become a C-suite level, if not board level, priority.

Minahan

And this really boils down to three things. Number one, demographically there is not enough talent. You have heard the numbers from McKinsey that within the next year there will be a shortage of 95 million medium- to high-skilled workers around the globe. And that’s being exacerbated by the fact that our traditional work models -- where we build a big office building or a call center and try to hire people around it -- is fundamentally broken.

The second key reason is a skills gap. Many companies are reengineering their business to drive digital transformation and new digital business or engagement models with their customers. But oftentimes their employee base doesn’t have the right skills and they need to work on developing them.

The third issue exacerbating the talent crunch is the fact that if you are fortunate enough to have the talent, it’s highly likely they are disengaged at work. Gallup just did its global Future of Work Study and found that 85 percent of employees are either disengaged or highly disengaged at work. A chief reason is they don’t feel they have access to the information and the tools they need to get their jobs done effectively.

Gardner: We have dissatisfaction, we have a hard time finding people, and we have a hard time keeping the right people. What can we bring to the table to help solve that? Is there some combination of what human resources (HR) used to do and IT maybe didn’t think about doing but has to do?

Enhance the employee experience 

Minahan: The concept of employee experience is working its way into the corporate parlance. The chief reason is that you want to be able to ensure the employees have the right combination of physical space and an environment conducive with interacting and partnering with their project teams -- and for getting work done.

Digital spaces, right? That is not just access to technology, but a digital space that is simplified and curated to ensure workers get the right information and insights to do their jobs. And then, obviously, cultural considerations, such as, “Who is my manager, what’s my development career, am I continuing to move forward?”

Those three things are combining when we talk about employee experience.

Gardner: And you talked about the where, the physical environment. A lot of companies have experimented with at-home workers, remote workers, and branch offices. But many have not gotten the formula right. At the same time, we are seeing cities become very congested and very expensive.

The traditional work models of old just aren't working, especially in light of the talent crunch and skills gap we're seeing. Traditional work models are fundamentally broken.

Do we need to give people even more choice? And if we do, how can we securely support that?

Minahan: The traditional work models of old just aren’t working, especially in light of the talent crunch and skills gap we are seeing. The high-profile example is Amazon, right? So over the past year in the US there was a big deal over Amazon selecting their second and third headquarters. Years ago Amazon realized they couldn’t hire all the talent they needed in Seattle or Silicon Valley or Austin. Now they have 17-odd tech centers around the US, with anywhere from 400 to several thousand people at each one. So you need to go where the talent is.

When we think about traditional work models -- where we would build a call center and hire a lot of people around that call center – it’s fundamentally broken. As evidence of this, we did a study recently where we surveyed 5,000 professional knowledge workers in the US. These were folks who moved to cities because they had opportunities and they got paid more. Yet 70 percent of them said that they would move out of the city if they could have more flexible work schedules and reliable connectivity.

Gardner: It’s pretty attractive when you can get twice the house for half the money, still make city wages, and have higher productivity. It’s a tough equation to beat.

Minahan: Yes, there is that higher productivity thing, this whole concept of mindfulness that’s working its way into the lingo. People should be hired to do a core job, not spending their days doing things like expense report approvals, performance reviews, or purchase requisitions. Yet those are a big part of everyone's job, when they are in an office.

You compound that with two-hour commutes, and that there are a lot of distractions in the office. We often need to navigate multiple different applications just to get a bit of the information that we need. We often need to navigate multiple different applications to get a single business process done and that’s just not dealing with all the different interfaces, it’s dealing with all the different authentications, and so on. All of that noise in your day really frustrates workers. They feel they were hired to do a job based on core skills they are really passionate about – but they spend all their time doing task work.

Gardner: I feel like I spend way too much time in email. I think everybody knows and feels that problem. Now, how do we start to solve this? What can the technology side bring to the table and how can that start to move into the culture, the methods, and the rethinking of how work gets done?

De-clutter intelligently

Minahan: The simple answer is you need to clear way the clutter. And you need to bring intelligence to bear. We believe that artificial intelligence (AI) and machine learning (ML) play a key role. And so Citrix has delivered a digital workspace that has three primary attributes.

First, it’s unified. Users and employees gain everything they need to be productive in one unified experience. Via single sign-on they gain access to all of their Software as a service (SaaS) apps, web apps, mobile apps, virtualized apps, and all of their content in one place. That all travels consistently with them wherever they are -- across their laptop, to a tablet, to a smartphone, or even if they need to log on from a distinct terminal.

The second component, in addition to being unified, is being secure. When things are within the workspace, we can apply contextual security policies based on who you are. We know, for example, that Dana logs in every day from a specific network, using his device. If you were to act abnormally or outside of that pattern, we could apply an additional level of authentication, or some other rules like shutting off certain functionalities such as downloading. So your applications and content are far more secure inside of the workspace than outside.

When things are within the workspace, we can apply contextual security policies based on who you are. Your applications and content are far more secure inside of the workspace than outside.

The third component, intelligence, gets to the frustration part for the employees. Infusing ML and simplified workflows -- what we call micro apps -- within the workspace brings in a lot of those consumer-like experiences, such as curating your information and news streams, like Facebook. Or, like Netflix, it provides recommendations on the content you would like to see.

We can bring that into the workspace so that when you show up you get presented in a very personalized way the insights and tasks that you need, when you need them, and remove that noise from your day so you can focus on your core job.

Gardner: Getting that triage based on context and that has a relevancy to other team processes sounds super important.

When it comes to IT, they may have been part of the problem. They have just layered on more apps. But IT is clearly going to be part of the solution, too. Who else needs to play a role here? How else can we re-architect work other than just using more technology?

To get the job done, ask employees how 

Minahan: If you are going to deliver improved employee experiences, one of the big mistakes a lot of companies make is they leave out the employee. They go off and craft the great employee experience and then present it to them. So definitely bring employees in.

When we do research and engage with customers who prioritize on the employee experience, it’s usually a union between IT and human resources to best understand what the work is that an employee needs to get done. What’s the preferred environment? How do they want to work? With that understanding, you can ensure you are adapting the digital workspaces -- and the physical workplaces -- to support that.

Gardner: It certainly makes sense in theory. Let’s learn how this works in practice.

Marco, tell us about Bechtle, what you have been doing, and why you made solving employee productivity issues a priority.

Stalder

Stalder: Bechtle AG is one of Europe’s leading IT providers. We currently have about 70 systems integrators (SIs) across Germany, Switzerland, and Austria, as well as e-commerce businesses in 14 different European countries.

We were founded in 1983 and our company headquarters is in Neckarsulm, a small town in the southern part of Germany. We currently have 10,300 employees spread across all of Europe.

As an IT company, one of our key priorities is to make IT as easy as possible for the end users. In the past, that wasn't always the case because the priorities had been set in the wrong place.

Gardner: And when you say the priorities were set in the wrong place, when you tried to create the right requirements and the right priorities, how did you go about that, what were the top issues you wanted to solve?

Stalder: The hard part is gaining the balance between security and user experience. In the past, priorities were more focused on the security part. We have tried to shift this through our Corporate Workspace Project to give the user the right kind of experience back again, and letting it show in the work and focus on what the user has to do.

Gardner: And just to be clear, are we talking about the users that are just within your corporation or did this extend also to some of your clients and how you interact with them?

Stalder: The primary focus was our internal user base, but of course we also have contractors that externally have to access our data and our applications.

Gardner: Tim, this is yet another issue companies are dealing with: contingent workforces, contractors that come and go, and creative people that are often on another continent. We have to think about supporting that mix of workers, too.

Synchronizing the talent pool 

Minahan: Absolutely. We are seeing a major shift in how companies think of the workforce, between full-time and part-time contractors, and the like. Leading companies are looking around for pools of talent. They are asking, “How do I organize the right skills and resources I need? How do I bring them together in an environment, whether it’s physical or digital, to collaborate around a project and then dissolve them when that project is complete?”

And these new work models excite me when we talk about the workspace opportunity that technology can enable. A great example is a customer of ours, eBay, which people are familiar with. A long time ago, eBay recognized that they could not get ahead of the older call center model. They kept training people, but the turnover was too fast. So they began using the Citrix Workspace together with some of our networking technologies to go to where the employees are.

Now they can go to the stay-at-home parent in Montana, the retiree in Florida, or the gig worker in New York. In this way, they can Uberfy the call center model by giving them, regardless of location, the applications, knowledge base, and reliable connectivity they need. So when you or I call in, it sounds like we are calling into a call center, and we get the answers we need to solve our problems.

Gardner: Marco, your largely distributed IT organization has permeable boundaries. There isn’t a hard wall between you and where your customers start and end. The Citrix Workspace helped you solve that. What were some of the other problems, and what was the outcome?

Stalder: One of the main criteria for Bechtle is agility. We have been growing constantly for the last 36 years. Bechtle started as a small company with only 100 employees, but organic and inorganic growth continues, and we are still growing quite rapidly. We just acquired another four companies at the end of last year, for example, with 400 to 500 employees. We need to on-board them quickly.

One of the main criteria for Bechtle is agility. We have been growing constantly for the last 36 years. And our teams are spread around different office locations. We also have to adapt to new technologies rapidly because we want to be ahead of the technology.

And our teams are spread around different office locations; even my team, for example. I am based in Switzerland with four people. Another part of our group is in Germany, and I have one colleague in Budapest. Giving all of these people the correct and secure access to all of their applications and data is definitely key.

As an IT company, we also have to adapt to new technologies rapidly and quickly, probably faster than other companies because we want to be ahead of the technology for our employees. We are selling these same solutions to our customers, along with the same experience -- and a good experience.

Gardner: We often call that drinking your own champagne. Tell us about the process through which you evaluated the Citrix Workspace solution and why that’s proven so powerful.

One platform to rule them all 

Stalder: In early 2016, we began with a high-level design for a basic corporate workspace. We began with an on-premises design, like a lot of companies. Then we were introduced to something called Citrix Cloud services by our partner manager in Germany.

In January 2017, we started to think about the Citrix Cloud solution as an interesting addition to what we were already planning. And we quickly realized that the team I am leading -- we are six to eight people with limited resources – could only deliver all those services out to our end users with help. The Citrix Cloud services were a perfect fit for the project we wanted to do.

There are different reasons. One is standardization, to build and use one platform to access all of our applications, data, and services. Another is flexibility. While most of our workloads are currently in our own data centers in Germany, we are also thinking about bringing workloads and data out to the cloud. It doesn’t matter if it’s Microsoft Azure, Amazon Web Services (AWS), or you name it.

Another benefit, of course, is scalability. As I said, we have been growing a lot and we are going to grow a lot more in the future. We really need to be able to scale out, and it doesn’t matter where the workload is going to be or where the data is going to be at the end.

And, as an IT company, we are facing another issue. We are selling different kinds of IT products to our customers, and people tend to like to use the product they are selling to their customers. So we have to explore and use different kinds of applications for different tasks.

For example, we use Microsoft Teams, Cisco WebEx Teams, as well as Skype for Business. We are using many other kinds of applications, too. That perfectly fits into what we have seen [from Citrix at their recent Synergy conference keynote]. It brings it all together in the Citrix Workspace using micro apps and micro services.

Another important attribute is efficiency. As I said before, with seven or eight IT support people, we cannot build very complex and large things. You have to focus on doing things very efficiently.

Another really important thing for us as we set up the workspaces project is engaging with the executive board of Bechtle. If we find that those people are not standing behind the idea and understanding what we are trying to do, then the project is definitely going to fail.

It was not that easy, just telling those board people what we would like to do. We had to build a proof of concept system to let them see, touch, and feel it themselves. Only in this way can one really understand it.

Gardner: Of course, such solutions are a team sport. You don’t just buy this out of the box. Digital transformation doesn’t come with a pretty ribbon on it. How did you go about creating this workspace?

There is IT in team 

Stalder: It was via teamwork spread between different kinds of groups. We have been working very closely with Citrix Consulting Services in Germany, for example. We have been working together with the engineers within our business units who are selling and implementing those solutions within our customers.

And another very important part, in my opinion, was not just engaging the Citrix people, but also engaging with the application owners. It doesn’t really help if I give them a very nice virtual desktop and they are able to log-on fast but they don’t have any applications on it. Or the application doesn’t work very well. Or if they have to log-on again, for example, or configure it before using it. We tried to provide an end-to-end solution by engaging with all of the different people -- from the front-end client, to the networking, and through to the applications’ back end.

And we have been quite successful. For example, for our main business applications, SAP or Microsoft, we have been telling the people what we want to do to get those application guys on board. They understand what it means for them. In the past we had been rolling out version updates for 70 different locations.

They were sending out emails saying, “Can you please go to the next version? Can you please update to this or that?” That, of course, requires a lot of time and is very hard to troubleshoot and configure.

But now, by standardizing those things together [as a workspace], we can deploy it once, configure it once, and it doesn’t matter who is going to use it. It has made those rollouts much easier. For example, for our virtual apps and desktops, we just reached about 30 percent of our employees. It's being done in a highly standardized project basis across every business unit.

We also realized the importance of informing and guiding the people as to how they have to use the new solutions, because it’s changing and some people, they react a bit slow to change. At first they say, “I don’t want to try it. I don’t need it.” It was a learning process to see what kind of documentation and guidance the people needed.

The changes are simple things [that deliver big paybacks]. Because if the people can take a PC back home and use a VPN to connect to their company resources, they may no longer need that PC. They can simply use any device to access their work from home or from on the road. Those are very simple things, but people have to understand that they can do that now.

Gardner: As I like to say, we used to force people to conform to the apps and now we can get the apps and services to conform to what the people want and need.

But we have talked about this in terms of the productivity of the employee. How about your IT department? How have your IT people reacted to this?

Stalder: I also needed a lot of time to convince the IT people, especially some security guys. They said, “You are going to go to Citrix Cloud? What does it mean for security?”

We have been working very closely with Citrix to explain to the security officer what kind of data goes to the cloud, how it’s stored, and how it’s processed. And that took quite a while to get approval, but at the end it went through, definitely.

The IT guys have to understand and use the solution. They sometimes think that it’s just for the end users. But IT is also an end user. They have to get on board and use the solutions. Only in this way everyone knows what the other one is talking about.

Gardner: Now that you have been through this process and the workspace is in place, what have you found? What are the metrics of success? When you do it well, what do you get back?

Positive feedback 

Stalder: Unfortunately, measuring productivity is very hard to do. I don’t have any numbers on that yet. I just get feedback from employees who are talking about different things as they try the system.

And I have quite an interesting story. For example, one guy in our application consulting group was a bit skeptical. One day his notebook PC was broken so he had to use the new Citrix Workspace. He had no choice but to try it.

He wrote back some very interesting facts and figures, saying it was faster. It was faster to log on and the applications started faster. And it was easy to use. Because he does a lot of presentations and training, he realized he could start the work on one device and then switch back to another device, maybe in the meeting room or go to the training room, and just continue the work.

We also get feedback saying they can work from everywhere, can access everything they need, especially if they go out to the customer, and that they only have to remember one place to log on to. They just log-on once and they have all the data and all the applications they are going to need.

Gardner: Tim, when you hear about such feedback from Marco, what jumps out at you?

Minahan: What stands out is the universal challenge we are all experiencing now. The employee experience is less than adequate in most organizations. It is impacting not only the ability to develop and retain great talent, but it’s also impacting your overall business.

What also stands out is that when technology is harnessed in a way that puts the employee first -- and drives superior experience to allow them to have access to the information and the tools they need to get their jobs done -- not only does employee retention go up, but you also drive better customer experiences, and better business end results.

The third thing that stands out is the recognition that traditionally we in the IT sector focused on putting security in the way of the experience. Now, if you put the employee at the center, we are beginning to attain a better balance between experience and security. It’s not an either-or equation anymore. This story at Bechtle is a great example of that in reality.

Gardner: What was interesting for me, too, was that employees get used to the way things are. You hit inertia. But when a necessity crops up, and somebody was forced to try something new, they found that there are better ways to do things.

Minahan: Right, it’s the old saw … If you only asked folks what they wanted, they would want a faster horse -- and we never would have had the car.

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

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