How to Industrialize Data Science to Attain Mastery of Repeatable Intelligence Delivery

Transcript of a discussion on the latest methods, tools, and thinking around making data science an integral core function of any business.

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

Dana Gardner: Hello, and welcome to the next BriefingsDirect Voice of Analytics Innovation podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on the latest insights into data science advances and strategy.

Gardner

Businesses these days are quick to declare their intention to become data-driven, yet the deployment of analytics and the use of data science remains spotty, isolated, and often uncoordinated. To fully reach their digital business transformation potential, businesses large and small need to make data science more of a repeatable assembly line -- an industrialization, if you will, of end-to-end data exploitation.

Stay with us now as we explore the latest methods, tools, and thinking around making data science an integral core function that both responds to business needs and scales to improve every aspect of productivity.

To learn more about the ways that data and analytics behave more like a factory -- and less like an Ivory Tower -- please join me now in welcoming Doug Cackett, EMEA Field Chief Technology Officer at Hewlett Packard Enterprise. Welcome, Doug.

Doug Cackett: Thank you so much, Dana.

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

Data’s potential at edge

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

Cackett

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

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

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

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

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

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

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

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

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

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

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

Industrialization of data science

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Define and refine

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

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

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

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

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

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

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

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

Two assembly lines to start

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

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

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

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

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

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

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

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

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

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

Operationalize with agility

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

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

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

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

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

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

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

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

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

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

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

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

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

Machine learning leads change

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

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

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

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

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

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

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

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

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

Financial services benefits

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

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

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

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

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

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

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

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

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

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

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

A data-science driven future

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

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

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

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

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

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

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

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

Gardner: I’m afraid we’ll have to leave it there. You’ve been with us as we explored how businesses can make data science more of a repeatable assembly line – an industrialization, if you will -- of end-to-end data exploitation. And we’ve learned how HPE is ushering in the latest methods, tools, and thinking around making data science an integral core function that both responds to business needs and scales to improve nearly every aspect of productivity.

So please join me in thanking our guest, Doug Cackett, EMEA Field Chief Technology Officer at HPE. Thank you so much, Doug. It was a great conversation.

Cackett: Yes, thanks everyone. Thanks, Dana.

Gardner: And a big thank you as well to our audience for joining this sponsored BriefingsDirect Voice of Analytics Innovation discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-supported discussions.

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. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Transcript of a discussion on the latest methods, tools, and thinking around making data science an integral core function of any business. Copyright Interarbor Solutions, LLC, 2005-2020. All rights reserved.

You may also be interested in:

• The journey to modern data management is paved with an inclusive edge-to-cloud Data Fabric

• The IT intelligence foundation for digital business transformation rests on HPE InfoSight AIOps

• Nimble Storage leverages big data and cloud to produce data performance optimization on the fly

• How Digital Transformation Navigates Disruption to Chart a Better Course to the New Normal

• How REI used automation to cloudify infrastructure and rapidly adjust its digital pandemic response

• How the right data and AI deliver insights and reassurance on the path to a new normal

• How IT modern operational services enables self-managing, self-healing, and self-optimizing

• How HPE Pointnext Services ushers businesses to the new normal via an inclusive nine-step plan

• As containers go mainstream, IT culture should pivot to end-to-end DevSecOps

COVID-19 Teaches Higher Education Institutes to Embrace Latest IT to Advance Remote Learning

Transcript of a discussion on how colleges and universities must rapidly redefine and implement a new and dynamic and blended balance between in-person and remote learning interactions.

 

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Citrix.

 

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you’re listening to BriefingsDirect.

Gardner

Like many businesses, innovators in higher education have been transforming themselves for the digital age for years, but the COVID-19 pandemic nearly overnight accelerated the need for flexible new learning models.

 

As a result, colleges and universities must rapidly redefine and implement a new and dynamic balance between in-person and remote interactions. This new normal amounts to more than a repaving of centuries-old, in-class traditions of higher education with a digital wrapper. It requires re-invention -- and perhaps new ways of redefining – of the very act of learning itself.

 

Stay with us now as we explore how such innovation today in remote learning may also hold lessons for how businesses and governments interact with and enlighten their workers, customers, and ultimately citizens.

 

Here to share recent experiences in finding new ways to learn and work during a global pandemic are Chris Foward, Head of Services for IT Services at The University of Northampton in the UK. Welcome, Chris.

 

Chris Foward: Hi, Thanks. Nice to meet you, Dana.

 

Dana Gardner: We are also here with Tim Minahan, Executive Vice President of Business Strategy and Chief Marketing Officer at Citrix. Welcome, Tim.

 

Tim Minahan: Hey, Dana, I’m excited to be here.

 

Gardner: And we’re here with Dr. Scott Ralls, President of Wake Tech Community College in Raleigh, North Carolina. Welcome, Dr. Ralls.

 

Dr. Scott Ralls: Thank you, Dana. I’m glad to be with you.

 

Education sprints online

 

Gardner: Scott, tell us about Wake Tech Community College and why you’ve been able to accelerate your path to broader remote learning?

 

Ralls: Wake Tech is the largest college in North Carolina, one of the largest community colleges in the United States. We have 75,000 total students across all of our different program areas spread over six different campuses.

 

Ralls

In mid-March, we took an early step in moving completely online because of the COVID-19 pandemic. But if we had just started our planning at that point, I think we would have been in trouble; it would have been a big challenge for us, as it has been for much of higher education.

 

The journey really began six years earlier with a plan to move to a more online-supported, virtual-blended world. For us, the last six months have been about sprinting. We are on a journey that hasn’t been so much about changing our direction or changing our efficacy, but really sprinting the last one-fourth of the race. And that’s been difficult and challenging.

 

But it’s not been as challenging as if you were trying to figure out the directions from the very beginning. I’ve been very proud of our team, and I think things are going remarkably well here despite a very challenging situation.

 

Gardner: Chris, please tell us about The University of Northampton and how the pandemic has accelerated change for you.

 

Foward

Foward: The University of Northampton has invested very heavily in its campus. A number of years ago and we built a new one called Waterside campus. The Waterside campus was designed to work with active blended learning (ABL) as an approach to delivering all course works, and -- similar to Wake Tech -- we’ve faced challenges around how we deliver online teaching.

 

We were in a fortunate position because during the building of our new campus we implemented all-new technology from the ground up -- from our plant-based systems right through to our backend infrastructure. We aimed at taking on new technologies that were either cloud-based or that allowed us to deliver teaching in a remote manner. That was done predominantly to support our ABL approach to delivery of education. But certainly the COVID-19 pandemic has sped up the uptake of those services.

 

Gardner: Chris, what was the impetus to the pre-pandemic blended learning? Why were you doing it? How did technology help support it?

 

Foward: The University of Northampton since 2014 has been moving toward its current institutional approach to learning and teaching. We never perceived of this as a large-scale online learning or a distance learning solution. But ABL does rely on fluent and thoughtful use of technologies for learning.

Our teachers found that the work they've done since 2014 really did stand us in good stead as we were able to very quickly change from an on-campus-taught environment to a digital experience for our students.

 

And this has stood the university in good stead in terms of how we actually deliver to our students. What our lecturers and teachers found is that the work they’ve done since 2014 really did stand us in a good stead as we were able to very quickly change from an on-campus-taught environment to a digital experience for our students.

 

Gardner: Scott, has technology enabled you to seek remote learning, or was remote learning the goal and then you had to go find the technology? What’s the relationship between remote learning and technology?

 

Ralls: For us, particularly in community colleges, it was more the second in that remote learning is an important priority for us because a majority of our students work. So the issues of just having the convenience of remote learning started community colleges in the United States down the path of remote learning much more quickly than for other forms of higher education. And so that helped us years ago to start thinking about what technologies are required.

 

Our college has been very thoughtful about the equity issues in remote learning. Some students succeed in more remote learning platforms, while others struggle with what those solutions may be. It was much more about the need for remote learning to allow working students with the capacities and conveniences, and then looking at what the technologies are and the best practices to achieve those goals.

 

Businesses learn from schools’ success

 

Gardner: Tim, when you hear Chris and Scott describing what they are doing in higher education, does it strike you that they are leaders and innovators compared generally to businesses? Should businesses pay attention to what’s going on in higher education these days, particularly around remote, balanced, and blended interactions?

 

Minahan

Minahan: Yes, I certainly think they are leading, Dana. That leadership comes from having been prepared for this in advance. If there’s any silver lining to this global crisis we are all living through, it’s that it’s caused organizations and participants in all industries to rethink how they work, school, and live.

 

Employers, having seen that work can now actually happen outside of an office, are catching up similarly. They’re rethinking their long-term workforce strategies and work models. They’re embracing more flexible and hybrid work approaches for the long-term.

 

And lower costs and improved productivity and engagement are giving them access to new pools of talent that were previously inaccessible to them in the traditional work-hub model, where you build a big office or call center and then you hire folks to fill them. Now, they can remotely reach talent in any location, including retirees or stay-at-home parents, and caretakers. They can be reactivated into the workforce.

As Kids Do More Remote School,

Managers Have Extra Homework, Too

Similarly to the diversity of the student body you’re seeing at Wake Tech, to do this they need a foundation, a digital workspace platform, that allows them to deliver consistent and secure access to the resources that employees or staff -- or in this case, students -- need to do their very best work across any channel or location. That can be in the classroom, on the road, or as we’ve seen recently in the home.

 

I think going forward, you’re going to see not just higher education, which we are hearing about here, but all industries begin to embrace this blended model for some very real benefits, both to their employees and their constituents, but to their own organizations as well.

 

Gardner: Chris, because Northampton put an emphasis on technology to accomplish blended learning, was the technology typical a few years back – traditional, stack-based enterprise IT -- a hindrance? Did you need to rethink technology as you were trying to accomplish your education goals?

 

Tech learning advances agility

 

Foward: Yes, we did. When we built our new campus, we looked at what new technologies were coming onto the market. We then moved toward a couple of key suppliers to ensure that we received best-in-class services as well as easy-to-use products. We chose partners like Microsoft for our software programs, like Office, and those sorts of productivity products.

 

We chose Cisco for networking and servers, and we also pulled in Citrix for delivery of our virtual applications and desktops from any location, anywhere, anytime. It allows flexibility for our students to access the systems from a smartphone and see a specific CAB-type models if we join those through solutions we have. It allows our factor of business and law to be able to present some of this bespoke software that they use. We can tailor the solutions that they see within these environments to meet the educational needs and courses that they are attending.

 

Gardner: Scott, at Wake Tech, as president of the university, you’re probably not necessarily a technologist. But how do you not be a technologist nowadays when you’re delivering everything as remote learning? How has your relationship with technology evolved? Have you had to learn a lot more tech?

 

Ralls: Oh, absolutely, yes. And even my own use of technology has evolved quite a bit. I was always aware and had broad goals. But, as I mentioned, we started sprinting very quickly, and when you are sprinting you want to know what’s happening.

 

We are very fortunate to have a great IT team that is both thoughtful in its direction and very urgent in their movement. So those two things gave me a lot of confidence. It’s also allowed us to sprint to places that we wouldn’t have been able to had these circumstances not come along.

We are very fortunate to have a great IT team that is both thoughtful in its direction and very urgent in their movement. Those two things gave me a lot of confidence. It also allowed us to sprint to places that we wouldn't have been able to.

 

I will use an example. We have six campuses. I would do face-to-face forums with faculty, staff, and students, so three meetings on every campus but once a semester. Now, I do those kinds of forums most days with students, faculty, or staff using the technology. Many of us have found that with the directions we were going that there are greater efficiencies to be achieved in many ways that we would not have tried had it not been for the [pandemic] circumstances.

 

And I think after we get past the issues we are facing with the pandemic; our world will be completely changed because this has accelerated our movement in this direction and accelerated our utility of the usage as well.

 

Gardner: Tim, we have seen over the years that the intersection between business and technology is not always the easiest relationship. Is what we’re seeing now as a result of the pandemic helping organizations attain the agility that they perhaps struggled to find before? 

 

Minahan: Yes, indeed, Dana. As you just heard, another thing the pandemic has taught us is that agility is key. Fixed infrastructure -- whether it’s real estate, the work-hub-centric models, data centers with loads of servers, and on-premise applications -- has proven to be an anchor during the pandemic. Organizations that rely heavily on such fixed infrastructure have had a much more difficult time shifting to a remote work or remote learning model to keep their employees and students safe and productive.

 

In fact, by an anecdote, we had one financial services customer, a CIO, recently say, “Hey, we can’t add servers and capacity fast enough.” And so, similar to Scott and Chris, we’re seeing an increasing number of our customers moving to adopt more variable operating models in everything they do. They are rethinking the real estate, staffing, and their IT infrastructure. As a result, we’re seeing customers take their measured plans for a one- to three-year transition to the cloud and accelerated that to three months, or even a few weeks.

 

They’re also increasing adoption of digital workspaces so that they can provide a consistent and secure work or learning experience for employees or students across any channel or location. It really boils down to organizations building agility into their operations so they can scale up quickly in the face of the next inevitable, unplanned crisis -- or opportunity.

 

Gardner: We’ve been talking about this through the lens of the higher education institute and the technology provider. But what’s been the experience over the past several months for the user? How are your students at Northampton adjusting to this, Chris? Is this rapid shift a burden or is there a silver lining to more blended and remote learning?

 

Easy-to-use options for student adoption

 

Foward: I’ll be honest, I think our students have yet to adopt it fully.

 

There are always challenges with new technology when it comes in. The uptake will be mainly driven in October when we see our mainstream student cohorts come onboard. I do think the types of technologies we have chosen are key, because making technology simple to use and easy to access will drive further adoption of those products.

 

What we have seen is that our staff’s uptake on our Citrix environment was phenomenal. And if there’s one positive to take from the COVID-19 situation it is the adoption of technology. Our staff has taken to it like ducks to water. Our IT team has delivered something exceptional, and I think our students will also see a massive benefit from these products, and especially the ease of use of these products.

 

So, yes, the key thing is making the products easily accessible and easy to use. If we overcomplicate it, you won’t get adoption and you won’t get an experience that customers need when they come to our education institutions.

 

Gardner: Dr. Ralls, have the students adjusted to these changes in a way that gives them agility as they absorb education?

 

Ralls: They have. All of us -- whether we work, teach, or are students at Wake Tech – have gained more confidence in these environments than we had before. I have regular conversations with these students. There was a lot of uncertainty, just like for many of us working remotely. How would that all work?

 

And we’ve now seen that we can do it. Things will still change around the notions of making the adjustments we need to. And for many of our students, it isn’t just how things will it change in the class, but in all of the things that they need around that class. For example, we have tutoring centers in our libraries. How do we make those work remotely and by appointment? We all wondered how that would work. And now we’ve seen that it can work, and it does work; and there’s an ease of doing that.

Reimagining Education

In a Remote World

Because we are a community college, we’re an open-admissions college. Many of our students haven’t had the level of academic preparation or opportunity that others have had. And so for some of our students who have a sense of uncertainty or anxiety, we have found that there is a challenge for them to move to remote learning and to have confidence initially.

 

Sometimes we can see that in withdrawals, but we’ve also found that we can rally around our students using different tools. We have found the value of different types of remote learning that are effective. For example, we’re doing a lot of the HyFlex model now, which is a combination of hybrid and remote, online-based education.

 

Over time we have seen in many of our classes that where classes started as hybrid, students then shifted to more fully remote and online. So you see the confidence grow over time.

 

Gardner: Scott, another benefit of doing more online is that you gain a data trail. When it comes to retention, and seeing how your programs are working, you have a better sense of participation -- and many other metrics. Does the data that comes along with remote learning help you identify students at risk, and are there other benefits?

 

Remote learning delivers data

 

Ralls: We’re a very data-focused college. For instance, even before we moved to more remote learning, every one of our courses had an online shell. We had already moved to where every course was available online. So we knew when our students were interacting.

 

One of the shifts we’ve seen at Wake Tech with more remote services is the expansion of those hours, as well as the ability to access counseling -- and all of our services remotely -- and through answer centers and other things.

 

But that means we had to change our way of thinking. Before, we knew when students took our courses, because they took them when you scheduled the courses. Now, as they are working remotely, we can also tell when they are working. And we know from many of our students that they are more likely to be online and engaged in our coursework between the hours of 5 pm and 10 pm, as opposed to 8 am and noon. Most of when we had been operating, from just having physical sites, was 8 am to 5 pm. Consequently, we have had to move the hours, and I think that’s something that will always be different about us and so that does give us that indication.

We had to change our way of thinking. Before, we knew when students took our courses because they took them when you scheduled the courses. Now, remotely we can also tell when they are working. We have had to move the hours to when they are actually operating.

 

One other thing about us that has been unique is because of who we are, because we do so much technical education -- that’s why we are called Wake Tech – and much of that is hands-on. You can’t do it fully remotely. But every one of our programs has found out the value of remote-based access through the support.

 

For example, we have a remarkable baking and pastry program. They have figured out how help the students get all of their hands-on resources at home in their own kitchens. They no longer have to come into the labs for what they do. Every program has found that value, the best aspects of their program being remote, even if their full program cannot be remote because of the hands-on matrix.

 

Gardner: Chris, is the capability to use the data that you get along the way at Northampton a benefit to you, and how?

 

Foward: Data is key for us in IT Services. We like to try and understand how people are using our systems and which applications they are using. It allows us to then fix the delivery of our applications more effectively. Our courses are also very data-driven. In our games art courses, for example, data allows us to design the materials more effectively for our students.

 

Gardner: Tim, when you are providing more value back through your technology, the data seems to be key as well. It’s about optimization and even reducing costs with better business and education outcomes. How does the data equation benefit Citrix’s customers, and how do you expect to improve on that?

 

Data enhances experiences

 

Minahan: Dana, data plays a major role in every aspect of what we do. When you think about the need to deliver digital workspaces by providing consistent and secure access to the resources -- whether it’s employees or students – they need to be able to perform at their best wherever that work needs to get done. The data that we are gathering is applied in a number of different ways.

 

Number one is around the security model. I use the analogy of not just having security access in -- the bouncer at the front door to make sure you have authenticated and are on the list to be access the resources you need -- but also having the bodyguard that follows you around the club, if you will, to constantly monitor your behavior and apply additional security policies.

 

The data is valuable for that because we understand the behavior of the individual user, whether they are typically accessing from a particular device or location or via the types of information or applications they access.

 

The second area is around performance. If we move to a much more distributed model, or a flexible or a blended model, vital to that is ensuring that those employees or students have reliable access to the applications and information they need to perform at their best. Being able to constantly monitor that environment allows for increasing bandwidth, or moving to a different channel as needed, so they get the best experience.

 

And then the last one gets very exciting. It is literally about productivity. Being able to push the right information or the right tasks, or even automate a particular task or remove it from their work stream in real time is vital to ensuring that we are not drowning in this cacophony of different apps and alerts -- and all the noise that gets in the way of us actually doing our best work or learning. And so data is actually vital to our overall digital workspace strategy at Citrix.

 

Gardner: Chris, to attain an improved posture around ABL, that can mean helping students pick up wherever they left off -- whether in a classroom, their workplace, at a bakery or in a kitchen at home. It requires a seamless transition regardless of their network and end device. How important is it to allow students to not have to start from scratch or find themselves lost in this collaboration environment? How is Citrix an important part of that?

 

Foward: With our ABL approach, we have small collaborative groups that work together to deliver or gain their learning.

 

We also ensure that the students have face-to-face contact with tutors, other distance learning, or while on campus. And with the technology, we store all of the academic materials in one location, called our mail site, which allows students to be able to access and learn as and when they need to. 

 

Citrix plays a key part in that because we can deliver applications into that state quickly and seamlessly. It allows students to always be able to understand and see the applications they need for their specific courses. It allows them to experiment, discuss ideas, and get more feedback from our lecturers because they understand what materials are being stored and how to access them.

 

Gardner: Dr. Ralls, how do you at Wake Tech prevent learning gaps from occurring? How does the technology help students move seamlessly throughout their education process, regardless of the location or device?

 

Seamless tracking lets students thrive

 

Ralls: There are different types of gaps. In terms of courses, one of the things we found recently is our students are looking for different types of access. Many of our students are looking for additional types of access -- perhaps replicating our seated courses to gain the value of synchronous experiences. We have had to make sure that all of our courses have that capacity, and that it works well. 

 

Then, because many of our students are also in a work environment, they want an asynchronous capability. And so we are now working on making sure students know the difference and how to match those expectations.

 

Also, because we are an open access college -- and as I like to say, we take the top 100 percent of our applicant students -- for many of our students, gaps come not just within a course, but between courses or toward their goals. For many of our students who are first-generation students, higher education is new. They may have also been away from education for a period of time.

We have to be much more intrusive and to help students and monitor to make sure our students are making it from one place to the next. We need to make sure that learning makes sense to them.

 

So we have to be much more intrusive and to help students and monitor to make sure our students are making it from one place to the next. We need to make sure that learning makes sense to them and that they are making it to whatever their ultimate goals are.

 

We use technology to track that and to know when our students are getting close to leaving. We call that being like rumble strips on the side of the road. There are gaps that we are looking at, not just within courses, but between courses, on the way to our students’ academic goals.

 

Gardner: Tim, when I hear Chris and Scott describe these challenges in education, I think how impactful this can be for other businesses in general as they increasingly have blended workforces. They are going to face similar gaps too. What, from Citrix’s point of view, should businesses be learning from the experiences at University of Northampton and Wake Tech?

 

Minahan: I think Winston Churchill summed it up best: “Never let a good crisis go to waste.” Smart organizations are using the current crisis -- not just to survive, but to thrive. They are using the opportunity to accelerate their digital transformation and rethink long-held work and operating models in ways they probably hadn’t before.

 

So as demonstrated both at Wake Tech and Northampton, and as Scott and Chris both said, for both school and work the future is definitely going to be blended.

 

We have, for example, another higher education customer, the University of Sydney that was able to get 20,000 students and faculty transition to an online learning environment last March, literally within a week. But that’s not the real story, it’s where they are going next with this.

 

As they entered the new school year in Sydney, they now have 100 core and software as a service (SaaS) applications that students can access through the digital workspace regardless of the type of device or their location. And they can ensure they have that consistent and secure and reliable experience with those apps. They say the student experience is as good, and sometimes even better, than what a student would have when using a locally installed app on a physical computer.

 

And now the university, most importantly, has used this remote learning model as an opportunity to reach new students -- and even new faculty -- in locations that they couldn’t have supported before due to geographic limitations of largely classroom-based models.

 

These are the types of things that businesses also have to think through. And as we hear from Wake Tech and Northampton, businesses can take a page from the courseware from many forward-thinking higher education organizations that are already in a blended learning model and see how that applies to their own business.

 

Gardner: Dr. Ralls, when you look to the future, what comes next? What would you like to see happen around remote learning, and what can the technologists like Citrix do to help?

 

Blended learning without walls

 

Ralls: Right now, there is so much greater efficiency than we had before. I think there is a way to bring that greater efficiency even more into our classrooms. For years we have talked about a flipped classroom, which really means those things that are better accomplished outside in a lab or in a shop, to do those outside of the classroom.

 

We have to all get to a place where the learning process just doesn’t happen within the walls of the classrooms. So the ability for students to go back and review work, to pick up on work, to use multiple different tools to add and supplement what they are getting through a classroom-based experience, a shop-based experience -- I think that’s what we are moving to.

See How Leading Institutes Use

Technology to Transform Education Delivery

For Wake Tech, this really hit us about March 15, 2020 when we went fully remote. We don’t want to go back to the way we were in April. We don’t want to be a fully remote, online college. But we also don’t want to be where we were in February.

 

This pandemic crisis has presented to us a greater acceleration of where we want to be, of where we can be. It’s what we aspire to be in terms of better education -- not just more convenient access of education -- but better educational opportunities through the multiple different opportunities that are brought to us by technology to supplement the core work that we have always done through our seat-based environment.

 

Gardner: Chris, at Northampton, what’s the next step for the technology enabling these higher goals that Dr. Ralls just described? Where would you like to see the technology take Northampton students next?

 

Foward: The technology is definitely key to what we are trying to do as education providers, to provide the right skill sets wherein students move from higher education into business. Certainly, with the likes of Citrix, with what was originally a commercial-focused application, and bringing it into our institution, we have allowed our students to gain access and understand how the system works -- and understand how to use it.

 

And that’s similar with most of our technologies that we have brought in. It gives students more of a commercial feel for how operations should be running, how systems should be accessed, and the ways to use those systems.

 

Gardner: Tim, graduates from Wake Tech and from University of Northampton a year or two from now, they are going to be well-versed in these technologies, and this level of collaboration and seamless transitions between blended approaches. How are the companies they go to going to anticipate these new mindsets? What should businesses be doing to take full advantage of what these students have already been doing in these universities?

 

Students become empowered employees

 

Minahan: That’s a great point, and it is certainly something that business is grappling with now as we move beyond hiring Millennials to the next generation of highly educated, grown-up-on-the-Internet students with high expectations who are coming out of universities today.

 

For the next few years, it all boils down to the need to deliver a superior employee experience, to empower employees to perform at their best, and to do the jobs they were hired to do. We should not burden them, as we have in a lot of corporate America, with a host of different distractions, apps, and rules and regulations that keep them away from doing their core jobs.

We need to deliver a superior employee experience. We should not burden them with a host of different distractions, apps, and rules that keep them from doing their core jobs.

 

And key to that, not surprisingly, is going to require a digital workspace environment that empowers and provides unified access to all of the resources and information that the employee needs to perform at their best across any work channel or location. They need a behind-the-scenes security model that ensures the security of the corporate assets, applications, and information -- as well as the privacy of individuals -- without getting in the way of work. 

 

And then, at a higher level, as we talked about earlier, we need an intelligence model with more analytics built into that environment. It will then not just offer up a launch pad to access the resources you need, but will actually guide you through your day, presenting the right tasks and insights as you need them, and allowing you to get the noise out of your day so you can really create, innovate, and do your best work. And that will be whether work is in an office, on the road, or work as we have seen recently, in the home.

 

Gardner: I wouldn’t be surprised if the students coming out of these innovative institutes of higher learning are going to be the instigators of change and innovation in their employment environments. So a point on the arrow from education into the business realm.

 

I’m afraid we’ll have to leave it there. We have been listening to a sponsored BriefingsDirect discussion on how innovators in higher education have been transforming themselves to meet the needs for flexible, new ways of learning.

 

And we have heard how innovation today in remote learning may hold valuable lessons for how businesses and governments will newly interact and enlighten their workers, customers, and even citizens.

 

So a big thank you to our guests, Chris Foward, Head of Services for IT Services at the University of Northampton in the UK. Thank you so much, Chris.

 

Foward: Thank you.

 

Gardner: We have also been here with Tim Minahan, Executive Vice President of Business Strategy and Chief Marketing Officer at Citrix. Thank you, Tim.

 

Minahan: Thanks, Dana. I appreciate the opportunity.

 

Gardner: And we have been joined by Dr. Scott Ralls, President of Wake Tech Community College in Raleigh, North Carolina. Thank you so much, Dr. Ralls.

 

Ralls: Thank you, Dana. I have enjoyed being with you.

 

Gardner: And a big thank you as well to our audience for joining this BriefingsDirect remote work and learning innovation discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host throughout this series of Citrix-sponsored BriefingsDirect discussions.

 

Thanks again for listening, please pass this along to your business associates, and do come back next time.

 

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Citrix.

 

Transcript of a discussion on how colleges and universities must rapidly redefine and implement a new and dynamic and blended balance between in-person and remote learning interactions. Copyright Interarbor Solutions, LLC, 2005-2020. All rights reserved.

 

You may also be interested in:

• Work in a COVID-19 world: Back to the office won’t mean back to normal
• Why flexible work and the right technology may Just close the talent gap
• Business readiness provides an agile key to surviving and thriving in these uncertain times
• Busy work is a dumpster fire and it’s time for something completely different
• Three generations of Citrix CEOs on enabling a better way to work
• IT and HR: Not such an odd couple
• How IT can fix the broken employee experience
• Happy employees equal happy customers -- and fans. Can IT deliver for them all?
• CEO Henshall on Citrix’s 30-year journey to make workers productive, IT stronger, and partners more capable