Showing posts with label Cloud computing. Show all posts
Showing posts with label Cloud computing. Show all posts

Thursday, February 18, 2021

How HPE Pointnext ‘Moments’ Provide a Proven Critical Approach to Digital Business Transformation


Transcript of a discussion with
HPE Pointnext Services experts as they detail a multi-step series of “Moments” that guide organizations on their transformations.

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

Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of Innovation video podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this timely discussion on effective paths for businesses to attain digital transformation.

Even as a vast majority of companies profess to be seeking digital business transformation, few proven standards or broadly accepted methods stand out as the best paths to take. And now, the COVID-19 pandemic has accelerated the need for bold initiatives to make customer engagement and experience optimization an increasingly data-driven and wholly digital affair.

Stay with us now as we welcome a panel of HPE Pointnext Services experts as they detail a multi-step series of “Moments” that guide organizations on their transformations. Here to share the Hewlett Packard Enterprise view on helping businesses effectively innovate for a new era of pervasive digital business are our panelists.

We’re here with Craig Partridge, Senior Director Worldwide, Digital Advisory and Transformation Practice Lead at HPE Pointnext Services. Welcome back, Craig.

Craig Partridge: Hey, Dana, good to see you again.


Gardner:
We’re also here with Yara Schuetz, Global Digital Advisor -- one of many -- at HPE Pointnext Services. Welcome, Yara.

Yara Schuetz: Hello, Dana, nice to be here.

Gardner: We’re also here with Aviviere Telang, Global Digital Advisor at HPE Pointnext Services. Welcome, Aviviere.

Aviviere Telang: Thanks, Dana, glad to be here.


Gardner:
And we’re joined by Christian Reichenbach, Global Digital Advisor at HPE Pointnext Services. Welcome, Christian.

Christian Reichenbach: Thanks, Dana, for having me.

Gardner: And we welcome Amos Ferrari, Global Digital Advisor at HPE Pointnext Services. Welcome, Amos.

Amos Ferrari: Thank you, Dana. Thank you, for inviting me.

Gardner: Craig, while some 80 percent of CEOs say that digital transformation initiatives are under way, and they’re actively involved, how much actual standardization -- or proven methods -- are available to them? Is everyone going at this completely differently? Or is there some way that we can help people attain a more consistent level of success?

Common ground in digital transformation

Partridge: A few things have emerged that are becoming commonly agreed upon, if not commonly executed upon. So, let’s look at those things that have been commonly agreed-upon and that we see consistently in most of our customers’ digital transformation agendas.

Partridge
The first principle would be – and no shock here -- focusing on data and moving toward being a data-driven organization to gain insights and intelligence. That leads to being able to act upon those insights for differentiation and innovation.

It’s true to say that data is the currency of the digital economy. Such a hyper-focus on data implies all sorts of things, not least of all, making sure you’re trusted to handle that data securely, with cybersecurity for all of the good things come that out of that data. 

Another thing we’re seeing now as common in the way people think about digital transformation is that it’s a lot more about being at the edge. It’s about using technology to create an exchange value as they transact value from business-to-business (B2B) or business-to-consumer (B2C) activities in a variety of different environments. Sometimes those environments can be digitized themselves, the idea of physical digitization and using technology to address people and personalities as well. So edge-centric thinking is another common ingredient.

The third element that’s becoming increasingly common and which underpins digital ambition is the need for greater agility in the way technology is brought into organizations and assembled to build new digital products and experiences. That means shifting organizations rapidly into a cloud-enabled future. Not just establishing the right cloud-use baseline, in terms of platforms and services that IT run, but also increasingly bringing in the development community as well.  And that requires making sure that they’re all coordinated strategically to leverage the cloud in a way that benefits the agility agenda of the organization.

These may not form an exact science, in terms of a standardized method or industry standard benchmark, but we are seeing these common themes now iterate as customers go through digital transformation.

Gardner: It certainly seems that if you want to scale digital transformation across organizations that there needs to be consistency, structure, and common understanding. On the other hand, if everyone does it the same way, you don’t necessarily generate differentiation.

How do you best attain a balance between standardization and innovation?

Partridge: It’s a really good question because there are components of what I just described that can be much more standardized to deliver the desired outcomes from these three pillars. If you look, for example, at cloud-use-enablement, increasingly there are ways to become highly standardized and mobilized around a cloud agenda.

Moving toward containerization and leveraging microservices, or developing with an open API mindset, these are now pervasive principles in almost every industry. IT has to bring its legacy environment to play in all of that at high velocity and high agility.

And that doesn’t vary much from industry to industry. Moving toward containerization, for example, and leveraging microservices or developing with an open API mindset -- these principles are pervasive in almost every industry. IT has to bring its legacy environment to play in that discussion at high velocity and high agility. So there is standardized on that side of it.

The variation kicks in as you pivot toward the edge and in thinking about how to create differentiated digital products and services, as well as how you generate new digital revenue streams and how you use digital channels to reach your customers, citizens, and partners. That’s where we’re seeing a high degree of variability. A lot of that is driven by the industry. For example, if you’re in manufacturing you’re probably looking at how technology can help pinpoint pain or constraints in key performance indicators (KPIs), like overall equipment effectiveness, and in addressing technology use across the manufacturing floor.

If you’re in retail, however, you might be looking at how digital channels can accelerate and outpace the four-walled retail experiences that companies may have relied on pre-pandemic.

Gardner: Craig, before we drill down into the actual Moments, were there any visuals that you wanted to share to help us appreciate the bigger picture of a digital transformation journey?

Partridge: Yes, let me share a couple of observations. As a team, we engage in thousands of customer conversations around the world. And what we’re hearing is exactly what we saw from a recent McKinsey report.


There are number of reasons why seven out of 10 respondents in this particular survey say they are stalled in attaining digital execution and gaining digital business value. Those are centered around four key areas. First of all, communication. It sounds like such a simple problem statement, but it is so hard to sometimes communicate what is a quite complex agenda in a way that is simple enough for as many people as possible -- key stakeholders – to rally behind and to make real inside the organization. Sometimes it’s a simple thing of, “How do I visualize and communicate my digital vision?” If you can’t communicate really clearly, then you can’t build that guiding coalition behind you to help execute. 

A second barrier to progress centers on complexity, so having a lot of suspended, spinning plates at the same time and trying to figure out what’s the relationship and dependencies between all of the initiatives that are running. Can I de-duplicate or de-risk some of what I’m doing to get that done quicker? That tends to be major barrier.

The third one you mentioned, Dana, which is, “Am I doing something different? Am I really trying to unlock the business models and value that are uniquely mine? Am I changing or reshaping my business and my market norms?” The differentiation challenge is really hard.

The fourth barrier is when you do have an idea or initiative agenda, then how to lay out the key building blocks in a way that’s going to get results quickly. That’s a prioritization question. Customers can get stuck in a paralysis-by-analysis mode. They’re not quite sure what to establish first in order to make progress and get to that minimum valuable product as quickly as possible. Those are the top four things we see.

To get over those things, you need a clear transformation strategy and clarity on what it is you’re trying to do. As I always say before the digital transformation -- everything from edge, business model, how to engage with customers and clients, and through to a technology-as-assembly -- to deliver those experiences and differentiation you have to have a distinctive transformation strategy. It leads to an acceleration capability, getting beyond the barriers, and planning the digital capabilities in the right sequence.

You asked, Dana, at the opening if there are emerging models to accomplish all of this. We have established at HPE something called Digital Next Advisory. That’s our joined customer engagement framework, through which we diagnose and pivot beyond the barriers that we commonly see in the customer digital ambitions. So that’s a high-level view of where we see things going, Dana. 

Gardner: Why do you call your advisory service subsets “Moments,” and why have you ordered them the way you did?

Moments create momentum for digital

Partridge: We called them Moments because in our industry if you start calling things services then people believe, “Oh, well, that sounds like just a workshop that I’ll pay for.” It doesn’t sound very differentiated.

We also like the way it expresses co-innovation and co-engagement. A moment is something to be experienced with someone else. So there are two sides to that equation.

In terms of how we sequence them, actually they’re not sequenced. And that’s key. One of the things we do as a team across the world is to work out where the constraint points and barriers are. So think of it as a methodology.

And as with any good methodology, there are a lot of tools in the toolkit. The key for us as practitioners in the Digital Next Advisory service is to know what tool to bring at the right point to the customer.

As with any good methodology, there are a lot of tools in the toolkit. The key for us as practitioners in the Digital Next Advisory service is to know what tool to bring at the right point to the customer.

Sometimes that’s going to mean a communication issue, so let’s go solve for that particular problem first. Or, in some cases, it’s needing a differentiated technology partner, like HPE, to come in and create a vision, or a value proposition, that’s going to be different and unique. And so we would engage more specifically around that differentiation agenda.

There’s no sequencing; the sequencing is unique to each customer. And the right Moment is to make sure that the customer understands it is bidirectional. This is a co-engagement framework between two parties.

Gardner: All right, very good. Let’s welcome back Yara.

Schuetz: To reiterate what Craig mentioned, when we engage with a customer in a complex phenomenon such as digital transformation, it’s important to find common ground where we can and then move forward in the digital transformation journey specific to each of our customers.

Common core beliefs drive outcomes

We have three core beliefs. One is being edge-centric. And on the edge-centric core belief we believe that there are two business goals and business outcomes that our customers are trying to achieve.


In the top left, we have the human edge-centric journey, which is all about redefining customer experiences. In this journey, for example, the corporate initiative could mean the experiences of two personas. It could be the customer or the employees.

These initiatives are designed to increase revenues and productivity via such digital engagements as new services, such as mobile apps. And also to complement this human-to-edge journey we have the physical journey, or the physical edge. To gain insight and control means dealing with the physical edge. It’s about using, for example, Internet of things (IoT) technology for the environment the organization works in, operates in, or provide services in. So the business objective here in this journey consists of improving efficiency by means of digitizing the edge.

Complementary to the edge-centric side, we also have the core belief that the enterprise of the future will be cloud-enabled. By being cloud-enabled, we again separate the cloud-enabled capabilities into two distinct journeys.

The bottom right-hand journey is about modernizing and optimization. In this journey, initiatives address how IT can modernize its legacy environment with, for example, multi-cloud agility. It also includes, for example, optimization and management of services delivery, where different workloads should be best hosted. We’re talking about on-premises as well as different cloud models to focus the IT journey. That also includes software development, especially accelerating development. 

Schuetz

This journey also involves the development improvement around personas. The aim is to speed up time-to-value with cloud-native adoption. For example, calling out microservices or containerization to shift innovation quickly over to the edge, using certain platforms, cloud platforms, and APIs.

The third core belief that the enterprise of the future should strive for is the data-driven, intelligence journey, which is all about analyzing and using data to create intelligence to innovate and differentiate from competitors. As a result, they can better target, for example, business analytics and insights using machine learning (ML) or artificial intelligence (AI). Those initiatives generate or consume data from the other journeys.

And complementary to this aspect is bringing trust to all of the digital initiatives. It’s directly linked to the intelligence journey because the data generated or consumed by the four journeys needs to be dealt with in a connected organization with resiliency and cybersecurity playing leading roles resulting in interest to internal as well as external stakeholders. 

At the center is the operating model. And that journey really builds the center of the framework because skills, metrics, practices, and governance models have to be reshaped, since they dictate the outcomes of all digital transformation efforts.

So, the value chain in the heart of an organization needs to evolve in order to optimize existing offerings and to create new digital ones. And as you’ve probably already observed, by now, the four outside business journeys, they overlap in the middle, which I’ve just described as the intelligence operating model and trust piece.


These components build the enabling considerations that one must consider when you’re pursuing different business goals such as driving revenues, building productivity, or modernizing existing environments via multi-cloud agility. To put that all in the context of what many companies are really asking for right now is to put it in the context of everything-as-a-service.

Everything-as-a-service does not just belong to, for example, the cloud-enabled side. It’s not only about how you’re consuming technology. It also applies to the edge side for our customers, and in how they deliver, create, and monetize their services to their customers.

Gardner: Yara, please tell us how organizations are using all of this in practice. What are people actually doing?

Communicate clearly with Activate

Schuetz: One of the core challenges we’ve experienced together with customers is that they have trouble framing and communicating their transformation efforts in an easily understandable way across their entire organizations. That’s not an easy task for them.

Communication tension points tend to be, for example, how to really describe digital transformation. Is there any definition that really suits my business? And how can I visualize, easily communicate, and articulate that to my entire organization? How does what I’m trying to do with technology make sense in a broader context within my company?

So within the Activate Moment, we familiarize them with the digital journey map. This captures their digital ambition and communicates a clear transformation and execution strategy. The digital journey map is used as a model throughout the conversations. This tends to improve how an abstract and complex phenomenon like digital transformation can be delivered as something visual and simple to communicate.

Besides simplification, the digital journey map in the Activate Moment also helps describe an overview and gives a structure of various influencing categories and variables, as well as their relationship with each other, in the context of digital transformation.

Besides simplification, the digital journey map in the Activate Moment also helps describe an overview and gives a structure of various influencing categories and variables, as well as their relationship with each other in the context of digital transformation. It provides our customers guidance on certain considerations, and, of course, all the various possibilities of the application of technology in their business.

For example, at the edge, when we bring the digital journey map into the customer conversation in our Activate Moment, we don’t just talk about the edge generally. We refer to specific customer needs and what their edge might be.

In the financial industry, for example, we talk about branch offices as their edge. In manufacturing, we’re talking about production lines as their edges. If in retail, you have public customers, we talk about the venues as the edge and how – in times like this and the new normal – they can redefine experience and drive value there for their customers there.

Of course, this also serves as inspiration for internal stakeholders. They might say, “Okay, if I link these initiatives, or if I’m talking about this topic in the intelligence space, [how does that impact] the digitization of research and development? What does that mean in that context? And what else do I need to consider?”


Such inspiration means they can tie all of that together into a holistic and effective digital transformation strategy. The Activate Moment engages more innovation on the customer-centric side, too, by bringing insights into the different and various personas at a customer’s edge. They can have different digital ambitions and different digital aspirations that they want to prosper from and bring into the conversation.

Gardner: Thanks again, Yara. On the thinking around personas and the people, how does the issue of defining a new digital corporate culture fit into the Activate Moment?

Schuetz: It fits in pretty well because we are addressing various personas with our Activate Moment. For the chief digital officer (CDO), for example, the impact of the digital initiatives on the digital backbone are really key. She might ask, “Okay, what data will be captured and processed? And which insights will we drive? And how do we make these initiatives trusted?”

Gardner: We’re going to move on now to the next Moment, Align, and orchestrating initiatives with Aviviere. Tell us more about the orchestrating initiatives and the Align Moment, please.

Align with the new normal and beyond

Telang: The Align Moment is designed to help organizations orchestrate their broad catalog of digital transformation initiatives. These are the core initiatives that drive the digital agenda. Over the last few years, as we’ve engaged with customers in various industries, we have found that one of the most common challenges they encounter in this transformation journey is a lack of coordination and alignment between their most critical digital initiatives.


And, frankly, that slows their time-to-market and reduces the value realized from their transformation efforts. Especially now, with the new normal that we find ourselves in, organizations are rapidly scaling up and broadening out that their digital agenda.

As these organizations rapidly pivot to launching new digital experiences and business models, they need to rapidly coordinate their transformation agenda against an ever-increasing set of stakeholders -- who sometimes have competing priorities. These stakeholders can be the various technology teams siting in an IT or digital office, or perhaps the business units responsible for delivering these new experience models to market. Or they can be the internal functions that support internal operations and supply chains of the organizations.

We have found that these groups are not always well-aligned to the digital agenda. They are not operating as a well-oiled machine in their pursuit of that singular digital vision. In this new normal, speed is critical. Organizations have to get aligned to the conversation and execute on all of the digital agenda quickly. That’s where the Align Moment comes in. It is designed to generate deep insights that help organizations evaluate a catalog of digital initiatives across organizational silos and to identify an execution strategy that speeds up their time-to-market. 

Telang
So what does that actually look like? During the Align Moment, we bring together a diverse set of stakeholders that own or contribute to the digital agenda. Some of the stakeholders may sit in the business units, some may sit in internal functions, or maybe even on the digital office. But we bring them together to jointly capture and evaluate the most critical initiatives that drive the core of the digital agenda.

The objective is to jointly blend our own expertise and experience with that of our customers to jointly investigate and uncover the prerequisites and interdependencies that so often exist between these complex sets of enterprise-scale digital initiatives.

During the Align Moment, you might realize that the business units need to quickly recalibrate their business processes in order to meet the data security requirements coming in from the business unit or the digital team. For example, one of our customers found out during their own Align Moment that before they got too far down the path of developing their next generation of digital product, they needed to first build in data transparency and accessibility as a core design principle in their global data hub.

The methodology in the Align Moment significantly reduces execution risk as organizations embark on their multi-year transformation agendas. Quite frankly, these agendas are constantly evolving because the speed of the market today is so fast.

Our goal here is to drive a faster time-to-value for the entire digital agenda by coordinating the digital execution strategy across the organization. That’s what the Align Moment helps our customers with. That value has been brought to different stakeholders that we’ve engaged with.

The Align Moment has brought tremendous value to the CDO, for example. The CDO now has the ability to quickly make sense and -- even in some cases -- coordinate the complex web of digital initiatives running across their organizations, regardless of which silos they may be owned within. They can identify a path to execution that speeds up the realization of the entire digital agenda. I think of it as giving the CDO a dashboard through which they can now see their entire transformation on a singular framework.

We have found that the Align Moment delivers a lot of value for digital initiative owners. Because we work jointly across silos to de-risk, the execution pass implements that initiative whether it's technology risk, process risk, or governance risk.

We’ve also found that the Align Moment delivers a lot of value for digital initiative owners. Because we jointly work across silos to de-risk, the execution pass implements that initiative whether it’s a technology risk, process risk, or governance risk. That helps to highlight the dependencies between these competing initiatives and competing priorities. And then, sequencing the work streams and efforts minimizes the risk of delays or mismatched deliverables, or mismatched outputs, between teams.

And then there is the chief information officer (CIO). This is a great tool for the CIO to take IT to the next level. They can elevate the impact of IT in the business, and in the various functions in the organization, by establishing agile, cross-functional work streams that can speed up the execution of the digital initiatives.

That’s in a nutshell what the Align Moment is about, helping our customers rapidly generate deep insights to help them orchestrate their digital agenda across silos, or break down silos, with the goal to speed up execution of their agendas.

Advance to the next big thing 

Gardner: We’re now moving on to our next Moment, around stimulating differentiation, among other things. We now welcome back Christian to tell us about the Advance Moment.

Reichenbach: The train-of-thought here is that digital transformation is not only to optimize businesses by using technology. We also want to emphasize that technology is used to transform businesses by leveraging digital technology.

Reichenbach

That means that we are using technology to differentiate the value propositions of our customers. And differentiation means, for example, new experiences for the customers of our customers, as well as new interactions with digital technology.

Further, it’s about establishing new digital business models, gaining new revenue streams, and expanding the ecosystem in a much broader sense. We want to leverage technology to differentiate the value propositions of our customers, and differentiation means you can’t do whatever one is doing by just copycatting, looking to your peers, and replicating what others are doing. That will not differentiate the value proposition.

Therefore, we specifically designed the Advance Moment where we co-innovate and co-ideate together with our customers to find their next big thing and driving technology to a much more differentiated value proposition.

Gardner: Christian, tell us more about the discreet steps that people need to do in order to get through that stimulating of differentiation.

Reichenbach: Differentiation comes from having new ideas and doing something different than in the past. That’s why we designed the Advance Moment to help our customers differentiate their unique value proposition.


The Advance Moment is designed as a thinking exercise that we do together with our customers across their diverse teams, meaning product owners, technology designers, engineers, and the CDO. This is a diverse team thinking about a specific problem they want to solve, but they shouldn’t think about it in isolation. They should think about what they do differently in the future to establish new revenue streams with maybe a new digital ecosystem to generate the new digital business models that we see all over the place in the annual reports from our customers.

Everyone is in the race to find the next big thing. We want to help them because we have the technology capabilities and experience to explain and discuss with our customers what is possible today with such leading technology as from HPE.

We can prove that we’ve done that. For example, we sit down with Continental, the second largest automotive part supplier in the world, and ideate about how we can redefine the experience of a driver who is driving along the road. We came up with a data exchange platform that helps our co-manufacturers to exchange data between each other so that the driver who’s sitting in the car gets new entertainment services that were not possible without a data exchange platform.

Our ideation and our Advance Moment are focused on redefining the experience and stimulating new ideas that are groundbreaking -- and are not just copycatting what their peers are doing. And that, of course, will differentiate the value propositions from our customers in a unique way so that they can create new experiences and ultimately new revenue streams.

We're addressing particular personas within our customer's organization. That's because today we see that the product owners in a company are powerful and are always asking themselves, "How can I bring my product to the next level?"

We’re addressing particular personas within our customer’s organization. That’s because today we see that the product owners in a company are powerful and are always asking themselves, “How can I bring my product to the next level? How can I differentiate my product so that it is not easily comparable with my peers?”

And, of course, the CDO in the customer organizations are looking to orchestrate these initiatives and support the product owners and engineers and build up the innovation engine with the right initiatives and right ideas. And, of course, when we’re talking about digital business transformation, we end up in the IT department because it has to operate somewhere.

So we bring in the experts from the IT department as well as the CIO to turn ideas quickly into realization. And for turning ideas quickly into something meaningful for our customers is what we designed the Accelerate Moment for.

Gardner: We will move on next to the Moment with Amos and learn about the Accelerate Moment, of moving toward the larger digital transformation value.

Accelerate from ideas into value

Ferrari: When it comes to realizing digital transformation, let me ask you a question, Dana. What do you think is the key problem our customers have?

Gardner: Probably finding ways to get started and then finding realization of value and benefits so that they can prove their initiative is worthwhile.

Ferrari: Yes. Absolutely. It’s a problem of prioritization of investment. They know that they need to invest, they need to do something, and they ask, “Where should I invest first? Should I invest in the big infrastructure first?”

But these decisions can slow things down. Yet time-to-market and speed are the keys today. We all know that this is what is driving the behavior of the people in their transformations. And so the key thing is the Accelerate Moment. It’s the Moment where we engage with our customers via workshops with them.

We enable them to extrapolate from their digital ambition and identify what will enable them to move into the realization of their digital transformation. “Where should I start? What is my journey’s path? What is my path to value?” These are the main questions that the Accelerate Moment answers.


As you can see, this is a part of the entire HPE Digital Next Advisory services, and it’s enabling the customer to move critically to the realization of benefits. In this engagement, you start with the decision about the use cases and the technology. There are a number of key elements and decisions that the customer is making. And this is where we’re helping them with the Accelerate Moment.

To deliver an Accelerate Moment, we use a number of steps. First, we frame the initiative by having a good discussion about their KPIs. How are you going to measure them? What are the benefits? Because the business is what is thriving. We know that. And we understand how the technology is the link to the business use case. So we frame the initiative and understand the use cases and scope out the use cases that advance the key KPIs that are the essential platform for the customer. That is a key step into the Moment.

Another important thing to understand is that in a digital transformation, a customer is not alone. No customer is really alone in that. It’s not successful if they don’t think holistically about their digital ecosystems. A customer is successful when they think about the complete ecosystem, including not only the key internal stakeholders but the other stakeholders surrounding them. Together they can enable them to build a new digital value and enable customer differentiation.

The next step is understanding the depth of technology across our digital journey map. And the digital journey map helps customers to see beyond just one angle. They may have started only from the IT point of view, or only from the developer point of view, or just the end user point of view. The reality is that IT now is becoming the value creator. But to be the value creator, they need to consider the entire technology of the entire company.

Ferrari
They need to consider edge-to-cloud, and data, as a full picture. This is where we can help them through a discussion about seeing the full technology that supports the value. How can you bring value to your full digital transformation?

The last step that we consider in the Accelerate Moment is to identify the elements surrounding your digital transformation that are the key building blocks and that will enable you to execute immediately. Those building blocks are key because they create what we call the minimal value product.

They should build up a minimum value product and surround it with the execution to realize the value immediately. They should do that without thinking, “Oh, maybe I need two or three years before realize that value.” They need to change to asking, “How can I do that in a very short time by creating something that is simple and straightforward to create by putting the key building blocks in place.”

This shows how everything is linked and how we need to best link them together. How? We link everything together with stories. And the stories are what help our key stakeholders realize what they needed to create. The stories are about the different stakeholders and how the different stakeholders see themselves in the future of digital transformation. This is the way we show them how this is going to be realized.

The end result is that we will deliver a number of stories that are used to assemble the key building blocks. We create a narrative to enable them to see how the applied technology enables them to create value for their company and achieve the key growth. This is the Accelerate Moment.

Gardner: Craig, as we’ve been discussing differentiation for your customers, what differentiates HPE Pointnext Services? Why are these four Moments the best way to obtain digital transformation?

Partridge: Differentiation is key for us, as well as for our customers across a complex and congested landscape of partners that the customers can choose. Some of the differentiation we’ve touched on here. There is no one else in the market, as far as I’m aware, that has the edge-to-cloud digital journey map, which is HPE’s fundamental model and allows us then to holistically paint the story of not only digital transformation and digital ambition -- but also shows you how to do that at the initiative level and to how plug in those building blocks.

I’m not saying that anybody with just the maturity of an edge-to-cloud model can bring digital ambition to life, to visualize it through the Activate Moment, orchestrate it through the Align Moment, create differentiation through the Advance Moment, and then get to quicker value with the Accelerate Moment.

Gardner: Craig, for those organizations interested in learning more, how do they get started? Where can they go for resources to gain the ability to innovate and be differentiated?

Partridge: If anybody viewing this has seen something that they want to grab on to, that they think can accelerate their own digital ambition, then simply pick up the phone and call HPE and your sales rep. We have sales organizations from dedicated enterprise managers at some of that biggest customers around the world, on through to small- to medium-sized businesses with our inside-sales organization. Call your HPE sales rep and say the magic words “I want to engage with a digital adviser and I’m interested in Digital Next Advisory.” And that should be the flag that triggers a conversation with one of our digital advisers around the world.


Finally, there’s an email address,
digitaladviser@hpe.com. If worse comes to worst, throw an email to that address and then we’d be able to get straight back to you. So, it should make it as easy as possible and just reach out to HPE advisors in advance.

Gardner: I’m afraid we have to leave it there. We’ve been examining how to transform organizations to effectively innovate for a new era a pervasive digital business. And we’ve learned how HPE Pointnext Services advises organizations across the multi-step series of Moments that guide organizations on and through their transformations.

Please join me in thanking our guests:

  • Craig Partridge, Senior Director Worldwide, Digital Advisory and Transformation Practice Lead, at HPE Pointnext Services;

  • Yara Schuetz, Global Digital Advisor at HPE Pointnext Services;

  • Aviviere Telang, Global Digital Advisor at HPE Pointnext Services;

  • Christian Reichenbach, Global Digital Advisor at HPE Pointnext Services, and

  • Amos Ferrari, Global Digital Advisor at HPE Pointnext Services.

Thank you all very much. And a big thank you as well to our audience for joining the sponsored BriefingsDirect Voice of Innovation discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host of this ongoing series on HPE-supported discussions.


Thanks again for listening, please pass this along to your IT community, and be sure to come back next time.

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

Transcript of a discussion with HPE Pointnext Services experts as they detail a multi-step series of “Moments” that guide organizations on their transformations. Copyright Interarbor Solutions, LLC, 2005-2021. All rights reserved.

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

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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.

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