Podcast

Full Episode Transcript:

Jon: Doug Henschen, Vice-President and Principal Analyst of Constellation Research, joins us today on the hello, Human podcast where we discuss the latest topics in artificial intelligence and how it’s being applied in the real world.

I’m Jon Knisley, the host of hello, Human, and a long-time technologist working at the intersection of business and emerging IT applications across both customer experience and operational excellence. A big thanks to FortressIQ for sponsoring the program. Be sure to subscribe wherever you listen to podcasts.

In this episode, we’re going to explore how next-generation platforms are rebooting data-to-decision initiatives for leading organizations. Almost every strategic initiative today relies on data and often data at scale, as well as technology optimization to meet the target objectives and get companies to their desired future state.

Welcome to the program, Doug. I’m excited to hear the analyst viewpoint today. Much of Constellation’s work focuses on how business models can be transformed by disruptive technology. I first heard about the firm a few years back with the publication of the now annual BT150, which is that list of 150 or so global executives who are leading innovative business efforts in their organizations. 

Maybe you can start, Doug, by providing a quick introduction to the firm and your research focus, as well as some comments on the trends that you’re seeing in the current environment—what obviously is not a technology-driven disruption. Certainly, the pandemic has disrupted business and the impact—at this point, it’s pretty clear—will be felt long-term.

Doug: Yes, John. Technology can help you weather this storm and deal with change in other contexts. It’s great to be with you. To tell you a little bit about Constellation Research, we’re a 10-year-old tech analyst firm. The same category as a Gartner or Forrester, but we’re really a boutique specialist tech analyst firm. We totally focus on what pioneers and innovators are doing, how they’re embracing technology to really change the way they do business, dramatically differentiate themselves, move that needle, and differentiate themselves.

Pioneers and innovators are only 5% of companies out there that can be in that leadership category, but we spend all our time with these leaders. We do a lot of case studies. We do a lot of different types of research around this innovation, and then we try to share it with fast followers. These are the companies that don’t want to get left behind, that’s maybe 20%–25% of companies. They aren’t necessarily the leaders. They don’t want to hit the same landmines and roadblocks that these pioneers and innovators overcome. That’s the idea is to share the insights and knowledge from those leaders and innovators.

What we don’t do is rubber-stamp the 20-year-old buying decisions for cautious adopters and laggards. I, myself, have been at Constellation a little over five years. My research domain is called Data-to-Decisions, which actually is very broad. It covers a lot of things—ingest, transform data, integrate the platforms that we see today, the use of data, BI, analytics, data scientists, and data science. But it’s not about any one of those silos. It’s about that end-to-end process of taking data and using it to drive decisions, using it to drive actions. In this pandemic environment, we’ve seen that the companies that can do that have huge advantages.

Our founder, Ray Wang, wrote a book about digital transformation about five years ago. Now, it’s the topic de jure. We often hear that in the last six months, companies have done more digital transformation than they did in the past six years. It’s all about getting more agile, getting more data-driven. It’s put the move into the cloud on steroids. The companies that are ahead of the curve are in a good position.

Jon: I appreciate that focus on emerging technologies as one who’s been involved in a lot of first-in-class technologies through a bunch of different cycles that can be a challenge. But seeing the impact that they can have in an organization—both the established players as well as the emerging ones—is just massive. So, that’s great to hear, and again, welcome to the program. 

Peter Drucker taught us back in the 50s that what gets measured gets improved. I tend to argue that that was really the start of this journey towards building a more data-driven business culture. Two generations later, we’re now awash in data and have a lot of great tools to manage it.

One of the major challenges that we’re seeing is companies trying to make all this data actionable. What do you see as the role of process orchestration in helping companies manage all this new information that they have?

Doug: When I hear that term, process orchestration, I think of connected, end-to-end processes. The big drive has been to create a transformational customer experience. To do that, you need to underpin it with back-office and processes behind the scenes that aren’t necessarily customer-facing but that has everything to do with delivering a transformational customer experience. 

As for that term, process orchestration, back in 2000–2004, I was editor of a magazine. I started as a tech journalist 25 years ago. Business process management was the thing we talked about. I think the terms have evolved. Today, Constellation sees a convergence ahead. We’re going to see integration, microservices, API management, orchestration workflow, robotic process automation, and AI in this mix of what we’re thinking of as intelligent orchestration that’s maturing. 

It’s not a clearly defined category today, but what is clear is that companies can’t paper over gaps in processes with email, spreadsheets, phone calls, and even paper-based approaches that some companies still use. We’re going to need a combination of humans and machines. But across those processes, you need measurability to drive continuous improvement. That’s really what’s important.

Jon: With this intelligent orchestration, ultimately, do you see technology stacks getting bigger? Is there going to be consolidation or some companies in the same way as we saw with marketing technology? Is it going to be a battle between best-of-breed versus all-in-one solution?

Doug: I think we’re going to see platforms on which people will start to build very custom applications. Instead of these standardized enterprise applications, they’re going to build more and more. There’s just tons and tons of last-mile applications that companies need to build that haven’t been there. There’s just too unique to the organization to have a commercial software vendor provide that application. Companies are going to be using this mix of technologies to build their own applications and experiences on top of what I’d call intelligent orchestration platforms.

Jon: Cool. One topic that I keep seeing and hearing more about is this idea of closed-loop analytics. How do those shift aid companies? What do you see as some of the major challenges in achieving it?

Doug: When I think closed loop, I think of learning. I think of cognitive systems. Let’s put that in context. People have been talking about AI. I think a real shift in the market in recent years was 2011. It’s almost 10 years ago now that Watson beat the human champions of jeopardy. That was a watershed moment when AI came back into fashion. Back then, the talk was of curing cancer, let alone driving better decisions. But it was turning out to be much harder than we thought, much more challenging than the hype was envisioning 9–10 years ago. It’s hard for a couple of reasons. We find that you have to close the loop at multiple levels.

It’s not just closing the loop on data. It’s closing the loop on the metadata. What’s the context behind that data? It’s closing the loop on the graph of human and machine interactions with systems. It has been this long-standing separation of analytics, whether it’s BI or more advanced data science, separation of that from the transactional environment and the processes.

We’ve had this swivel chair integration. Where you’re in one system, and then you go off and you look at the report, or you go off and look at a dashboard to help make your decision. Of course, that’s open to inconsistent human interpretation. That is tied to this second challenge of having the context. It’s not just about the data. You really have to know something about it.

It’s the metadata around that data. Who is this customer? What is their history? What is the stage of the process they’re in? When did we talk to them last? Do they have a service issue that’s unresolved? Are they near the end of their contract? It’s not just data, it’s the context around the data. 

That’s one reason I’ve been doing a lot of research and been advocating embedded approaches that next-generation applications that bring the data and the context right into the context of the application. You have both a human understanding and machine understanding, and you can drive better actions, whether those actions are carried out and executed by a machine or by a human.

Jon: Much like consulting as an analyst, you see lots of different companies across lots of different industries. Can you share any real-world stories with us? Examples of high performing companies that are more mature in their data to insights journey, and really starting to drive value from leveraging their data platforms?

Doug: I’ll use an example that’s often cited by our founder, Ray Wang. He cited it in his book. If you start talking about the hottest stocks of the last 10 years, 2010–2020, everybody would probably immediately think of the FANG companies, Facebook, Amazon, Netflix, Google, et cetera; these born in the internet age companies. You’d be right. If you look at Motley Fool, number one over that last decade was Netflix, but number two is a brick-and-mortar company you might not think of, and that’s Domino’s Pizza. 

If you really look closely at what they’ve done, they’re absolutely a pioneer and an innovator. If you go through their website or their phone app, a customer can track the order. They can see the status of the pizza that they ordered. Who’s making it, when it’s out of the oven, when it’s in the delivery vehicle, who’s dropping it off, and when it will arrive. 

That is process awareness and Domino’s obviously uses this internally for optimization and improvement. They use it for a great customer experience. I’m talking about the delivery and the visibility standpoint. I’m not necessarily a fan of their pizza but they continue to work on this, continue to work on making it easier and easier to interact with the company and to get dinner. 

They did it with a website. They did it with an app. They added a smartwatch. They added a smart speaker. They do everything they can and they keep working at it. It’s totally data-driven. They’re process-aware. They’re continuously improving, and in this pandemic environment, they’ve only been growing.

Jon: I truly appreciate the Domino’s reference. I’m a big fan of the app. My daughter might fight you over the quality of the pizza question. She’s a major fan of it.

Doug: I’m in the New York area. I’m used to the New York basis. My frame of reference is a little different than most people. 

Jon: But I totally get it. They see themselves as a technology company that does pizza, along the same lines, as you often hear Capital One. They’re a technology company that does banking. Great example there.

Doug: I’ll mention another example. I’ll count them. I’m not a stock owner. I don’t have to disclaim this but my own experience. In the pandemic, I wanted to take advantage of these historically low-interest rates. I called my well-known large bank that holds a great majority of mortgages these days, figuring they know me. I’m their customer already. They should be able to provide better service. I was on hold for an hour. I’ve used the bot and left messages. I didn’t hear back for a week. I interacted with Rocket Mortgage, had a lock-in on the same day, I had a great seamless experience. Four or five weeks later, their agent was in our driveway with a mask and an iPad. They wiped it off and almost every document we needed to sign.

I just took that iPad back into our home while they stayed in the driveway. My wife and I could digitally sign almost every document. The only documents that still required wet signatures were those that were required by the state. It was a tremendous customer experience. I’ve since had very proactive communications. This is when your first mortgage payment is going to be due, what portion went to tax, what portion went to the principal, et cetera. Great customer experience. Again, I’m not a stockholder or anything, just another example of a company that has a great digital experience.

Jon: While we’re on that topic, that reference made me think about the other one, in that space a little bit, at least on the financial space, the Lemonade Insurance. The ability for them to process a claim for a minor loss in literally seconds—I think it’s down to 10 seconds now—and money appearing in people’s accounts the next day. Just incredible stories you hear about ease of use and an entirely frictionless experience. Obviously has an impact on some of those larger, more established organizations.

Doug: Yeah. The insurance industry has also raised the bar on having your insurance policy be based on usage. The snapshot type thing doesn’t mess with my discount. Now, every company seems to have a way to capture your actual driving data and charge you your premium based on your actual driving.

Jon: Very cool. Turning to this, you mentioned it briefly before this convergence of humans and technology. We hear a lot about the front-office applications, your work on the more data analytics side, more operational back-office areas, specifically analytics. What’s your prognosis trends around this issue of human-agent teaming and partnership?

Doug: Well, companies in the C-suite are increasingly going to have four choices. When do we trust intelligent machine automation? When do we augment the machine with a human? When is it still primarily a human process but we can assist them with recommendations and suggested actions? When do we leave it in the hands of human judgment, either because of creativity or a lot of intuition, or interpretation is needed where there’s complexity, where there’s risk, et cetera? 

Today, we’ve already seen plenty of examples of bots. Simple bots to more sophisticated digital workers for specific roles and industries. Everything from time-off requests approval, password resets, more complex things where you have voice-text interaction or nuanced interpretation of human intent. More complex workflows like initial screening of customer support calls, not with ABC wrote a menu, but an actual natural language conversation in which the machine can actually discern intent and probably handle the 80% of the calls and then route the other 10% to 20% to the human.

What we’re seeing now is progress is happening. It’s really starting to gel in the uber management of the total team environment, overseeing the toll process with both digital agents and humans. It’s not just a bunch of disconnected silos of automation, these bots. You need kind of an overarching sense of what’s happening in the total process. You need to monitor and load-balance across the humans and the digital agents in the process. You need to be able to measure, optimize, and sense when needs are changing. Then, you can change processes accordingly and retrain both the humans and these digital agents that sometimes you might have to take the agent offline and switch in the human because something is happening dynamically in the business environment. That’s what we see ahead. The progress is now happening, where it’s overall monitoring of the process, not just adding more and more bots to have silos of automation.

Jon: I love that comment on augmentation and the recognition that some processes should and remain human-based processes. I think too often we say, let’s throw more technology at it, it’s all got to be technology, but depending on the experience you’re trying to deliver, sometimes it does require that human interaction, that human contact that people desire. That’s a great point to end on.

To recap today’s conversation with Doug Henschen, the Vice-President and Principal Analyst at Constellation Research, certainly, one of the leading research and advisory firms looking at the impact of disruptive technologies on business models, with the ongoing return to business leading organizations continue to pursue digital transformation and innovation goals even as they seek efficiencies and optimizations. These initiatives really depend on data and often data at scale, as well as technology optimization to meet those target objectives and achieve the target future state. The next-generation platforms that are emerging or are reshaping the data-to-decision journey for many organizations. 

Thank you, Doug, for joining me today. I want to give you an opportunity to make any closing comments or provide any final thoughts you may have. But I also have one final question for you. I’m a bit of an information junkie, always looking for the latest and greatest resources out there. My question to you is as an analyst, what resource—website, newsletter, podcasts, anything at all—do you rely on to be successful and knowledgeable on your role?

Doug: I would caution any technologists not to get too carried away from the need for the technology. My go-to every morning is a thorough read of the business section—The New York Times, Wall Street Journal—something a little deeper than a lot of small-town papers in terms of depth of what’s happening in the business. You need that context and grounding of what’s happening in the economy, what are the business trends, what are the business needs. You can just read the headlines from day-to-day, see what companies are standing out, and how they’re differentiating themselves.

In terms of technology, I would have two sites that I spent a lot of time on, datanami.com is great for news and analysis of all things analytics and data science. Some great reporters there like Alex, Woody, and George Leopold. For a deeper, more scholarly take on data science and what’s happening in data science and analytics, KDnuggets is a great site. Case studies, in-depth how-to, opinion, a rich resource on what’s happening in data science.

Jon: That’s great advice. We’ll make sure to put all those resources in the show notes. For reference, I’ll give you my resource in this episode that I can’t miss and I’m going to stay close to home for you, Doug. That would be DisrupTV, the weekly web series hosted by your colleague that you’ve mentioned Ray Wang and Vala Afshar from Salesforce. There’s always a nugget that I find in every show that I can use for my work.

Doug: Yes. Thanks for the […]. Great weekly resource. 

Jon: It is a great weekly resource indeed. I think that’s a wrap on today’s show. Thank you, Doug, for joining me and FortressIQ for their sponsorship. I’m Jon Knisley, and this has been hello, Human.