Thursday, December 10, 2020

Why customer experience management has never been more important or impactful

 


T
he next
BriefingsDirect digital business innovation discussion explores how companies need to better understand and respond to their markets one subscriber at a time. By better listening inside of their products, businesses can remove the daylight between their digital deliverables and their customers’ impressions.

Stay with us now as we hear from a customer experience (CX) management expert at SAP on the latest ways that discerning customers’ preferences informs digital business imperatives.

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


T
o learn more about the business of best fulfilling customer wants and needs, please welcome Lisa Bianco, Global Vice President, Experience Management and Advocacy at SAP Procurement Solutions. The interview is moderated by
Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: What was the catalyst about five years ago that led you there at SAP Procurement to invest in a team devoted specifically to CX innovation?

Bianco: As a business-to-business (B2B) organization, we recognized that B2B was changing and it was starting to look and feel more like business-to-consumer (B2C). The days of leaders dictating the solutions and products that their end users were going to be leveraging for day-to-day business stuff -- like procurement or finance – we found we were competing with what an end-user’s experience would be with the products or applications they use in their personal life.

Bianco
We all know this; we’ve all been there. We would go to work to use the tools, and there used to be those times we would use the printer for our kids’ flyers for their birthday because it was a much better tool than what we had at home. And that had shifted.

But then business leaders were competing with rogue employees using tools like Amazon.com versus SAP Ariba’s solution for procurement to buy things for their businesses. And so with that maverick spend, companies weren’t having the same insights that they needed to make decisions. So, we knew that we had to ensure that that end-user experience at work replicated what they might feel at home. It reflected that shift in persona from a decision-maker to that of a user.

Gardner: Whether it’s B2B or B2C, there tends to be a group of people out there who are really good at productivity and will find ways to improve things if you only take the chance to listen and follow their lead, right?

Bianco: That’s exactly right.

Gardner: And what was it about B2B in the business environment that was plowing new ground when it came to listening rather than just coming up with a list of requirements, baking it into the software, and throwing it over the wall?

Leaders listen to customer experience

Bianco: The truth is, better listening to B2B resulted in a centralized shift for leaders. All of a sudden, a chief procurement officer (CPO) who made a decision on a procurement solution, or a chief information officer (CIO) who made a decision on an enterprise resource planning (ERP) solution, they were beginning to get flak from cross-functional leaders who were end-users and couldn’t actually do their functions.

In B2B we found that we had to start understanding the feelings of employees and the feelings of our customers. And that’s not really what you do in B2B, right? Marketing and branding at SAP now said that the future of business has feelings. And that’s a shock. I can’t tell you how many times I have talked to leaders who say, “I want to switch the word empathy in our mission statement because that’s not strong leadership in B2B.”

The truth is we had to shift. Society was shifting to that place and understanding that feelings allow us to understand the experiences because experiences were that of people. We can only make so many decisions based on our operational data.

But the truth is we had to shift. Society was shifting to that place and understanding that feelings allow us to understand the experiences because the experiences were that of people. We can only make so many decisions based on our operational data, right? You really have to understand the why.

We did have to carve out a new path, and it’s something we still do to this day. Many B2B companies haven’t evolved to an experience management program, because it’s tough. It’s really hard.

Gardner: If we can’t just follow the clicks, and we can’t discern feelings from the raw data, we need to do something more. What do we do? How do we understand why people feel good or bad about what they are doing?

Bianco: We get over that hurdle by having a corporate strategy that puts the customer at the center of all we do. I like to think of it as having a customer-centric decision-making platform. That’s not to say it’s a product. It’s really a shift in mindset that says, “We believe we will be a successful company if our customers’ feelings are positive, if their experiences are great.”

If you look at the disruptors such as Airbnb or Amazon, they prioritize CX over their own objectives as a business and their own business success, things like net-new software sales or renewal targets. They focus on the experiences that their customers have throughout their lifecycle.

That’s a big shift for corporate America because we are so ingrained in producing for the board and we are so ingrained in producing for the investors that oftentimes putting that customer first is secondary. It’s a systemic shift in culture and thinking that tends to be what we see in the emerging companies today as they grab such huge market share. It’s because they shifted that thinking.

Gardner: Right. And when you shift the thinking in the age of social media -- and people can share what their impressions are -- that becomes a channel and a marketing opportunity in itself. People aren’t in a bubble. They are able to say and even demonstrate in real time what their likes are, what their dislikes are, and that’s obvious to many other people around them.

Customer feedback ecosystem

Bianco: Dana, you are pointing out risk. And it’s so true. And this year, the disrupter that COVID-19 has created is a tectonic shift in our digitalization of customer feedback. And now, via social media and Twitter, if you are not at the forefront of understanding what your customers’ feelings are -- and what they may or may not say -- and you are not doing that in a proactive way, you run the risk of it playing out socially in a public forum. And the longer that goes unattended to, you start to lose trust.

When you start to lose trust, it is so much harder to fix than understanding in the lifecycle of a customer the problems that they face, fixing those and making that a priority.

Gardner: Why is this specifically important in procurement? Is there something about procurement, supply chain, and buying that this experience focus is important? Or does it cut across all functions in business?

Bianco: It’s across all functions in business. However, if you look at procurement in the world today, it incorporates a vast ecosystem. It’s one of those functions in business that includes buyers and suppliers. It includes logistics, and it’s complex. It is one of the core areas of a business. When that is disrupted it can have drastic effects on your business.


We saw that in spades this year. It affects your supply chain, where you can have alternative opportunities to regain your momentum after a disruption. It affects your workforce and all of the tools and materials necessary for your company to function when it shifts and moves home. And so with that, we look from SAP’s perspective at these personas that navigate through a multitude of products in your organization. And in procurement, because that ecosystem is there for our customers, understanding the experience of all of those parties allows for customers to make better decisions.

A really good example is one of the world’s largest consulting firms. They took 500,000 employees in offices around the world and found that they had to immediately put them in their homes. They had to make sure they had the products they needed, like computers, green screens, or leisure wear.

They learned what looks good enough on a virtual Zoom meeting. Procurement had to understand what their employees needed within a week’s time so that they didn’t lose revenue deploying the services that their customers had purchased and rely on them for.

Understanding that lifecycle really helps companies, especially now. Seeing the recent disruption made them able to understand exactly what they need to do and quickly make decisions to make experiences better to get their business back on track.

Gardner: Well, this is also the year or era of moving toward automation and using data and analytics more, even employing bots and robotic process automation (RPA). Is there something about that tack in our industry now that can be brought to CX management? Is there a synergy between not just doing this manually, but looking to automation and finding new insights using new tools?

Automate customer journeys

Bianco: It’s a really great insight into the future of understanding the experiences of a customer. A couple of things come to mind. As you look at operational data, we have all recognized the importance of having operational data; so usage data, seeing where the clicks are throughout your product. Really documenting customer journey maps.

If you automate the way you get feedback you don't just have operational data; you need to get that feelings to come through with experience data ... to help drive to where automation needs to happen.

But if you automate the way you get feedback you don’t just have operational data; you need to get the feelings to come through with experience data. And that experience data can help drive where automation needs to happen. You can then embed that kind of feedback-loop-process in typical survey-type tools or embed them right into your systems.

And so that helps you understand some areas where we can remove steps from in the process, especially as many companies look to procurement to create automation. And so the more we can understand where we have those repetitive flows and we can automate, the better.

Gardner: Is that what you mean by listening inside of the product or does that include other things, too?

Bianco: It includes other things. As you may know, SAP purchased a company called Qualtrics. They are experts in experience management, and we have been able to move from and evolve from traditional net promoter score (NPS) surveys into looking at micro moments to get customer feedback as they are doing a function. We have embedded certain moments inside of our product that allow us to capture feedback in real time.

Gardner: Lisa, a little earlier you alluded that there are elements of what happens in the B2C world as individual consumers and what we can then learn and take into the B2B world. Is there anything top of mind for you that you have experienced as a consumer that you said, “Aha, I want to be able to do that or bring that type of experience and insight to my B2B world?”

Customer service is king in B2B

Bianco: Yes, you know what happened to me just this week as a matter of fact? There is a show on TV right now about chess. With all of us being at home, many of us are consuming copious amounts of content. And I went and ordered a chess set, it came, it was beautiful, it was from Wayfair, and one of the pieces was broken.

I snapped a little picture of the piece that had broken and they had an amazing app that allowed me to say, “Look, I don’t need you to replace the whole thing, it’s just this one little piece, and if you can just send me that, that would be great.”

And they are like, “You know what? Don’t worry about sending it back. We are just going to send you a whole new set.” It was like a $100 set. So I now have two sets because they were gracious enough to see that I didn’t have a great experience. They didn’t want me to deal with sending it back. They immediately sent me the product that I wanted.

I am, like, where is that in B2B? Where is that in the complex area of procurement that I find myself? How can we get that same experience for our customers when something goes wrong?


When I began this program, we would try to figure out what is that chess set. Other organizations use garlic knots, like at pizza restaurants. While you and your kids wait 25 minutes for the pizza to be made, a lot of pizza shops offer garlic knots to make you happy so the wait doesn’t seem so long. What is that equivalent for B2B?

It’s hard. What we learned early on, and I am so grateful for, is that in B2B many end users and customers know how difficult it is to make some of their experiences better, because it’s complex. They have a lot of empathy for companies trying to go down such a path, in this case, for procurement. 

But with that, what their garlic knot is, what their free product or chess set is, is when we tell them that their voice matters. It’s when we receive their feedback, understand their experience against our operational data, and let them know that we have the resources and budget to take action on their feedback and to make it better.


Either we show them that we have made it better or we tell them, “We hear what you are saying, but that doesn’t fit into our future.” You have to be able to have that complete feedback loop, otherwise you alienate your customer. They don’t want to feel like you are asking for their feedback but not doing anything with it.

And so that’s one of the most important things we learned here. That’s the thing that I witnessed from a B2C perspective and tried to replicate in B2B.

Gardner: Lisa, I’m sensing that there is an opportunity for the CX management function to become very important for overall digital business transformation. The way that Wayfair was able to help you with the chess set required integration, cooperation, and coordination between what were probably previously siloed parts of their organization.

That means the helpdesk, the ordering and delivering, exception management capabilities, and getting sign-off on doing this sort of thing. It had to mean breaking down those silos -- both in process, data, and function. And that integration is often part of an all-important digital transformation journey. 

So are you finding that people like yourself, who are spearheading the experience management for your customers, are in a catbird seat of identifying where silos, breakdowns, and gaps exist in the B2B supplier organizations?

Feedback fuels cross-training

Bianco: Absolutely. Here is what I have learned: I am going to focus on cloud, especially in companies that are either cloud companies or had been an on-premises company and are migrating to being a cloud company. SAP Ariba did this over the last 20 years. It has migrated from on-premises to cloud, so we have a great DNA understanding of that. SAP is out doing the same thing; many companies are.

And what’s important to realize, at least from my perspective -- it was an “Aha” moment -- is that there is a tendency in the B2C world leadership to say, “Look, I am looking at all this data and feedback around customers. Can’t we just go fix this particular customer issue, and they are going to be happy?”

Most of the issues our customers were facing were systemic. There was consistent feedback about something that wasn't working. We had to recognize that these systemic issues needed to be solved by a cross-functional group of people.

What we found in the B2B data was that most of the issues our customers were facing were systemic. It was broad strokes of consistent feedback about something that wasn’t working. We had to recognize that these systemic issues needed to be solved by a cross-functional group of people.

That’s really hard because so many folks have their own budgets, and they lead only a particular function. To think about how they might fix something more broadly took our organization quite a bit of time to wrap our heads around. Because now you need a center of excellence, a governance model that says that CX is at the forefront, and that you are going to have accountability in the business to act on that feedback and those actions. And you are going to compose a cross-functional, multilevel team to get it done.

It was funny early on, in our receiving feedback that customer support is a problem. Support was the problem. The support function was awful. I remember the head of support was like, “Oh, my gosh. I am going to get fired. I just hate my job. I don’t know what to do.”

When you look at the root cause you find that quality is a root-cause issue, but quality wasn’t just in one or another product -- it was across many products. That broader quality issue led to how we enabled our support teams to understand how to better support those products. That quality issue also impacted how we went to market and we showed the features and functions of the product.

We developed a team called the Top X Organization that aggregated cross-functional folks, held them accountable to a standard of a better outcome experience for our customers, and then led a program to hit certain milestones to transform that experience. But all that is a heavy lift for many companies.

Gardner: That’s fascinating. So, your CX advocates -- by having that cross-functional perspective by nature -- became advocates for better processes and higher quality at the organization level. They are not just advocating for the customer; they are actually advocating for the betterment of the business. Are you finding that and where do you find the people that can best do that?

Responsibility of active listening

Bianco: It’s not an easy task, it’s for few and far between. Again, it takes a corporate strategy. Dana, when you asked me the question earlier on, “What was the catalyst that brought you here?” I oftentimes chuckle. There isn’t a leader on the planet who isn’t going to have someone come to them, like I did at the time, and say, “Hey, I think we should listen to our customers.” Who wouldn’t want to do that? Everyone wants to do that. It sounds like a really good idea.

But, Dana, it’s about active listening. If you watch movies, there is often a scene where there is a husband and wife getting therapy. And the therapist says, “Hey, did you hear what she said?” or, “Did you hear what he said?” And the therapist has them repeat it back. Your marriage or a struggle you have with relationships is never going to get better just by going and sitting on the couch and talking to the therapist. It requires each of you to decide internally that you want this to be better, and that you are going to make the changes necessary to move that relationship forward.

It’s not dissimilar to the desire to have a CX organization, right? Everyone thinks it’s a great idea to show in their org chart that they have a leader of CX. But the truth is you have to really understand the responsibility of listening. And that responsibility sometimes devolves into just taking a survey. I’m all for sending a survey out to our customers, let’s do it. But that is the smallest part of a CX organization.


It’s really wrapped up in what the corporate strategy is going to be: A customer-centric, decision-making model. If we do that, are we prepared to have a governance structure that says we are going to fund and resource making experiences better? Are we going to acknowledge the feedback and act on it and make that a priority in business or not?

Oftentimes leaders get caught up in, “I just want to show I have a CX team and I am going to run a survey.” But they don’t realize the responsibility that gives them when now they have on paper all the things that they know they have an opportunity to make better for their customers.

Gardner: You have now had five years to make these changes. In theory this sounds very advantageous on a lot of levels and solves some larger strategic problems that you would have a hard time addressing otherwise.

So where’s the proof? Do you have qualitative, quantitative indicators? Maybe it’s one of those things that’s really hard to prove. But how do you rate customer advocacy and CX role? What does it get you when you do it well?

Feelings matter at all levels

Bianco: Really good point. We just came off of our five-year anniversary this week. We just had an NPS survey and we got some amazing trends. In five years, we have seen an even greater improvement in the last 18 months -- an 11-point increase in our customer feedback. And that not only translates into the survey, as I mentioned, but it also translates with influencers and analysts.

Gartner has noted the increase in our ability to address CX issues and make them better. We can see that in terms of the 11-point increase. We can see that in terms of our reputation within our analyst community.

And we also see it in the data. Customers are saying, “Look, you are much more responsive to me.” We see a 35-percent decrease in customers complaining in their open text fields about support. We see customers mentioning less the challenges they have seen in the area of integration, which is so incredibly important.

We see a 35-percent decrease in customers complaining in their open text fields about support. We see customers less challenged by integration, which is so incredibly important.

And we also hear less from our own SAP leaders who felt like NPS just exposed the fact that they might not be doing their job well, which was initially the experience we got from leaders who were like, “Oh my gosh. I don’t want you to talk about anything that makes it look like I am not doing my job.” We created a culture where we have been more open to feedback. We now relish in that insight, versus feeling defensive.

And that’s a culture shift that took us five years to get to. Now you have leaders chomping at the bit to get those insights, get that data, and make the changes because we have proof. And that proof did start with an organizational change right in the beginning. It started with new leadership in certain areas like support. Those things translated into the success we have today. But now we have to evolve beyond that. What’s the next step for us?

Gardner: Before we talk about your next steps, for those organizations that are intrigued by this -- that want to be more customer-centric and to understand why it’s important -- what lessons have you learned? What advice do you have for organizations that are maybe just beginning on the CX path?

Bianco: How long is this show?

Gardner: Ten more minutes, tops.

Bianco: Just kidding. I mean gosh, I have learned a lot. If I look back -- and I know some of my colleagues at IBM had a similar experience – the feedback is this. We started by deploying NPS. We just went out there and said we are going to do these NPS surveys and that’s going to shake the business into understanding how our customers are feeling.

We grew to understand that our customers came to SAP because of our products. And so I think I might have spent more time listening inside of the products. What does that mean? It certainly means embedding micro-moments, of aggregating feedback, in the product to help understand -- and allows our developers to understand what they need to do. But that need to be done in a very strategic way.

It’s also about making sure that any time anyone in the company wants to listen to customers, you ensure that you have the budget and the resources necessary to make that change -- because otherwise you will alienate your customers.

Another area is you have to have executive leadership. It has to be at the root of your corporate objectives. Anything less than that and you will struggle. It doesn’t mean you won’t have some success, but when you are looking at the root of making experience better, it’s about action. That action needs to be taken by the folks responsible for your products or services. Those folks have to be incented, or they have to be looped in and committed to the program. There has to be a governance model that measures the experience of the customer based on how the customer interprets it -- not how you interpret it.

If, as a company, you interpret success as net-new software sales, you have to shift that mindset. That’s not how your customers view their own success.

Gardner: That’s very important and powerful. Before we sign off, five years in, where do you go now? Is there an acceleration benefit, a virtuous adoption pattern of sorts when you do this? How do you take what you have done and bring it to a step-change improvement or to an even more strategic level?

Turn feedback into action

Bianco: The next step for us is to embed the experience program in every phase of the customer’s journey. That includes every phase of our engagement journey inside of our organization.


So from start to finish, what are the teams providing that experience, whether it’s a service or product? That would be one. And, again, that requires the governance that I mentioned. Because action is where it’s at -- regardless of the feedback you are getting and how many places you listen. Action is the most important piece to making their experience better.

This requires governance because action is where it's at -- regardless of the feedback. Taking action is the most important piece to making the customer experience better.

Another is to move beyond just NPS surveys. Again, it’s not that this is a new concept, but as I watched the impact of COVID-19 on accelerating digital feedback, social forums, and public forums, we measured that advocacy. It’s not just the, “Will you recommend this product to a friend or colleague?” In addition it’s about, “Will you promote this company or not?”

That is going to be more important than ever, because we are going to continue in a virtual environment next year. As much as we can help frame what that feedback might be -- and be proactive -- is where I see success for SAP in the future.

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

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Monday, December 7, 2020

How to industrialize data science to attain mastery of repeatable intelligence delivery

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

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

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

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


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

Here are some excerpts:

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

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

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?

Data’s potential at the edge

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

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

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

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

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

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

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

Industrialization of data science

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

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


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

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

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

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

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

Gardner: Right, this is what US manufacturing, in the bricks and mortar sense, went through back in the 1980s when they moved to business process reengineering, adopted kaizen principles, and did what 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.


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

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

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

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