069 – The Role of Creativity and Product Thinking in Data Monetization with ‘Infonomics’ Author Doug Laney

Experiencing Data with Brian O'Neill (Designing for Analytics)
Experiencing Data with Brian T. O'Neill
069 - The Role of Creativity and Product Thinking in Data Monetization with ‘Infonomics’ Author Doug Laney

Doug Laney is the preeminent expert in the field of infonomics — and it’s not just because he literally wrote the book on it.

As the Data & Analytics Strategy Innovation Fellow at consulting firm West Monroe, Doug helps businesses use infonomics to measure the economic value of their data and monetize it. He also is a visiting professor at the University of Illinois at Urbana-Champaign where he teaches classes on analytics and infonomics.

On this episode of Experiencing Data, Doug and I talk about his book Infonomics, the many different ways that businesses can monetize data, the role of creativity and product management in producing innovative data products, and the ever-evolving role of the Chief Data Officer.

In our chat, we covered:

  • Why Doug's book Infonomics argues that measuring data for its value potential is key to effectively managing and monetizing it. (2:21)
  • A 'regenerative asset': Innovative methods for deploying and monetizing data — and the differences between direct, indirect, and inverted data monetization. (5:10)
  • The responsibilities of a Chief Data Officer (CDO) — and how taking a product management approach to data can generate additional value. (13:28)
  • Why Doug believes that a 'lack of vision and leadership' is partly behind organizational hesitancy of data monetization efforts. (17:10)
  • ‘A pretty unique skill’: The importance of bringing in people with experience creating and marketing data products when monetizing data. (19:10)
  • Insurance and torrenting: Creative ways companies have leveraged their data to generate additional value. (24:27)
  • Ethical data monetization: Why Doug believes consumers must receive a benefit when organizations leverage their data for profit. (27:14)
  • The data monetization workshops Doug runs for businesses looking to generate new value streams from its data. (29:42)

Quotes from Today’s Episode

“Many organizations [endure] a vicious cycle of not measuring [their data], and therefore not managing, and therefore not monetizing their data as well as they can. The idea behind my book Infonomics is, flip that. I’ll just start with measuring your data, understanding what you have, its quality characteristics, and its value potential. But vision is important as well, and so that’s where we start with monetization, and thinking more broadly about the ways to generate measurable economic benefits from data.” - Doug (4:13)

“A lot of people will compare data to oil and say that ‘Data is the new oil.’ But you can only use a drop of oil one way at a time. When you consume a drop of oil, it creates heat and energy and pollution, and when you use a drop of oil, it doesn’t generate more oil. Data is very different. It has unique economic qualities that economists would call a non-rivalrous, non-depleting, and regenerative asset.” - Doug (7:52)

“The Chief Data Officer (CDO) role has come on strong in organizations that really want to manage their data as an actual asset, ensure that it is accounted for as generating value and is being managed and controlled effectively. Most CDOs play both offense and defense in controlling and governing data on one side and in enabling it on the other side to drive more business value.”- Doug (14:17)

“The more successful teams that I read about and I see tend to be of a mixed skill set, they’re cross-functional; there’s a space for creativity and learning, there’s a concept of experimentation that’s happening there.” - Brian (19:10)

“Companies that become more data-driven have a market-to-book value that’s nearly two times higher than the market average. And companies that make the bulk of their revenue by selling data products or derivative data have a market-to-book value that’s nearly three times the market average. So, there's a really compelling reason to do this. It’s just that not a lot of executives are really comfortable with it. Data continues to be something that’s really amorphous and they don’t really have their heads around.” - Doug (21:38)

“There’s got to be a benefit to the consumer in the way that you use their data. And that benefit has to be clear, and defined, and ideally measured for them, that we’re able to reduce the price of this product that you use because we’re able to share your data, even if it’s anonymously; this reduces the price of your product.” - Doug (28:24)

Links referenced


Brian: Hello, everyone. Welcome back to Experiencing Data. This is Brian O’Neill. Today I’ve got the author of Infonomics, a great text that’s out. Doug Laney, who is a fellow in data and analytics at West Monroe, you’re also a professor. We’re going to talk about productizing data and monetizing data, some of the issues there, as well as Chief Data Officer v4.0, the product-oriented CTO. Welcome to the show, Doug, how are you doing?

Doug: Thanks, Brian. Great to be with you.

Brian: Yeah, yeah.

Doug: How are you doing?

Brian: I’m doing great, and I want to know how the moon inspired your interest in data. We kind of ended with that on our pre-screening call, and I thought that was really funny. So, tell me about that.

Doug: Yeah. So, one of my earliest memories was my father waking me up, he came into my bedroom and he said, “Doug, it’s time to get up.” Well, at that time I was kind of, like a lot of kids, was asleep in spaceman pajamas and had solar system mobiles and stuff, and so you can imagine what it was time to get up for: it was the first moon landing. And I remember it distinctly, sitting in front of our black and white television and my dad working the rabbit ears on the television to get the signal and watching the moon landing. And I guess what made an impression on me, as I thought back on it, was my father was a world-class engineer, mechanical-electrical engineer but what really made the most impression on him and that got him the most excited, I think, was that this information about the moon landing, and the data, and the images were streaming into our living room and billions of living rooms around the world. And I think that’s really what made an impression on him. It wasn’t the devices and the mechanical capabilities itself, it was that we could see it. And so, I often think back to that and remember that time, and perhaps that’s helped to precipitate my interest in information.

Brian: Yeah, yeah. So obviously, since then, we’ve created a whole lot of data, not always a whole lot of value with this data. Everyone’s scraping it up and storing it because it’s cheap to do so and there’s all this theoretical value behind it. You’ve written a book called Infonomics here. What are the high-level things that a leader needs to be thinking about in terms of monetizing this or maybe not even monetizing it, but creating value with it? From your perspective, where are they getting this wrong? What are you seeing working well? Where are people going to get hung up?

Doug: How much time do we have? A few hours?

Brian: [laugh]. Yeah, yeah.

Doug: Yeah.

Brian: Well, let’s narrow this down. I’m specifically interested—because I know your book talks about a couple different ways to think about leveraging data to drive value and drive revenue in these kinds of things, but I’m particularly interested in productizing it, so turning it into an information service, a software application, a value-add to existing digital services that you have. So, maybe if we scoped it down to that, could you speak to that?

Doug: Sure. Well, it all sort of fits together. There’s really three components, and that is: measuring the data, understanding what you have and what its value proposition is, what its potential value is, what its actual value is, and the bar on the margin that you’re generating on data; the next part is managing data as an actual asset, and the old adage is, “You can’t manage what you don’t measure,” and so because a lot of companies are not compelled to measure their data because accounting practices are still steeped in an 85-year-old history of not considering data an actual balance sheet asset. And because organizations just don’t feel compelled to value their data in the same way that they value other assets. In fact, a lot of companies value their office furniture more than they—or account for their office furniture more than their data.

I think it follows that you can’t really monetize what you’re not managing well. And so the idea behind Infonomics is to reverse that curse because for many organizations, it’s a vicious cycle of not measuring, and therefore not managing, and therefore not monetizing their data as well as they can. So, the idea behind Infonomics is, flip that. And so, I’ll just start with measuring your data, understanding what you have, and its quality characteristics, its value potential. But vision is important as well, and so that’s where we start with monetization, and thinking more broadly about the ways to generate measurable economic benefits from data.

So, when I talk about monetizing data, it’s not necessarily about selling it; it’s about generating new value streams from it, and they may be internal, they may be external. I’ve captured examples of nearly five, six hundred ways that organizations are using data and analytics in innovative and high-value measurable ways. And they sort of fall into seven or eight, kind of, core patterns of internal and external monetization.

Brian: I want you to tell—I’m going to ask you what those patterns are, but I want to ask you something about the data management piece which is not my area. But one thing I do know that is happening quite a bit is that because there are so many technical things to get right, I feel like it’s almost a distraction where, if we simply manage the data properly, then we will get this value from it. And so teams are spending a lot of time on infrastructure, plumbing, the latest vendor tool that—let’s move from A to B because now everyone’s using B and B is all the new hype. And I wonder if you see that as well, where these large enterprise initiatives are mostly focused around building the potential to do something with the data, not actually delivering value, but just the building block. And I understand that’s required. Do you see that as well? Or do you see more of a business-driver behind those technology decisions?

Doug: You’re right. There certainly has been a fixation on technology and building monster—you know, my data warehouse is bigger than your data warehouse, or my data lake is deeper than your data lake.

Brian: Yeah. It’s still dirty and stinky, though.

Doug: [laugh]—while savvy executives have moved beyond that and really just thinking about the variety of ways to generate value from data. And yes, a lot of it starts with not technology, but with even changing the culture in the organization to get people to underst—become more data literate, or data fluent, and to understand the possibilities and also the challenges in managing and leveraging data. Things like master data management, metadata management, and data quality, and data governance are all really core capabilities that come before an organization’s ability to really monetize their data well.

Brian: Do you think, though, that if they don’t have one of these seven or eight broad category type initiatives as some kind of strategy and plan, that the rest of that is potentially going to be a failed initiative? Or do you think, don’t even bother to ask the question about how you’re going to monetize until you have the raw playground in shape? What’s your thinking there?

Doug: Yeah, most organizations are fixated on doing hindsight-oriented reporting and using data for some single operational purpose. So, one thing that we like to encourage our clients to do is think of ways to use data for generating insights to use it in more predictive ways, or even in prescriptive ways. Ninety-seven percent of the examples that I’ve compiled have nothing to do with building pretty pie charts or bouncy bar charts; they all have to do with doing more advanced analytics with data, or deploying data in more innovative ways. But you bring up an interesting, differentiating quality of data. A lot of people will compare data to oil, right, data is the new oil.

But oil, you can only use a drop of oil one way at a time. When you consume a drop of oil, it creates heat and energy and pollution, and that when you use a drop of oil, it doesn’t generate more oil. Data is very different. It has unique economic qualities that economists would call it a non-rivalrous, non-depleting, and regenerative asset. Companies that really get that think about ways to deploy their data in multiple ways, not just get fixated on one way to do so, and that whenever they use data, they set up systems to capture more data about wherever and whenever that data was used. So, that’s really a way to generate high margin on your data, to monetize it in a multitude of ways.

Brian: Mm-hm. What are some of those seven or eight categories, the high roll-up categories? And if you have anecdotal examples of that, particularly innovative companies? If you can’t use the names, that’s fine, but I’m more interested in—

Doug: Yeah, I could some—

Brian: —what the work was.

Doug: Some names.

Brian: Yeah, yeah.

Doug: So, they fall into two classes: indirect data monetization and direct data monetization. Indirect data monetization is about using data more internally to improve business process performance or effectiveness, to reduce risk or improve compliance—again, in a measurable way—to develop new products or markets, to build or solidify partner relationships. And so that’s really the main ways to use data internally. But again, if you’re not measuring data’s contribution to any of those areas, then it’s hard to claim that you’re monetizing it. So again, measuring it is important.

And then externally, we refer to it as direct data monetization where you’re using data to barter or trade for goods and services or favorable commercial terms. Maybe you’re enhancing existing products or services with data, or digitalizing existing products or services. Perhaps, yes, you’re selling raw data directly or some kind of data derivative, either yourself or through a data marketplace or through a data broker, can offer insights and analyses and reports to your customers and suppliers and partners. And then there’s one that I didn’t write about in the book that came up more recently in light of GDPR and the California Privacy Act. Clients have come to me and they said, “Well, we can’t monetize our customer data because of privacy regulations.”

And I say, “Well, I call BS on that. You just are not being creative enough.” Flip the model. I can’t sell you my customer data, but I can sell your stuff to my customers without ever having to expose who they are. And that’s what I refer to as inverted data monetization.

And so that’s something that we’re working on with hospitals, and banks, and companies in highly regulated industries where they really can’t share customer data. So, for example, we’re working with a hospital who knows who its diabetes patients are but can’t sell that data to anyone, but they can sell to those patients healthy meal plans, or gym memberships, or at home glucose monitoring testing kits. And then take a cut of that action, take a commission on it. So, there are definitely ways to monetize your customer data without having to expose who it is. There are all sorts of great examples of companies generating measurable economic value from their data.

I love the story about Walmart, where they had a great search engine, but one week, they realized that it was not getting people to what they were looking for, the search term was the word, “House,” and it was taking people to housing goods, and dog houses, and housewares and it wasn’t at all it folks were looking for. You can imagine what it might have been once they looked into it and they realized that those searches corresponded with the week that a certain television show premiered. The television show, House, the medical drama. And what people were looking for, obviously, was the box DVD set from the previous seasons or the ability to stream previous seasons. And so Walmart realized that their search engine wasn’t taking into account trends, what was trending on social media or elsewhere; it was just kind of staring at its own navel.

And so when they upgraded the search engine to incorporate trend data from social media, they realized that they were able to reduce shopping cart abandonment by 10 to 15%, which in Walmart terms is, like, a billion dollars a year or something in additional revenue. So, able to monetize it that way. Another great story is Lockheed Martin that took an idea—I worked with them and they took this idea to analyze project communications and documentation to identify leading indicators of project issues, rather than just simply relying on the old status reporting method, where somebody types up a status report and then it gets escalated, and so forth. They were able to generate three times greater foresight into project issues like scope, budget personnel, technology-related issues, and are saving hundreds of millions of dollars a year and in cost overruns on the product lines, like fighter jets, where they’re deploying this. Which is great for us as American citizens; we’re their customer [laugh] so.

And then, of course, there’s companies that are selling data directly, like Dollar General sells its inventory, and supply chain data, and pricing, and shopping basket data to its CPG suppliers, and in doing so they have a self-funding data lake. And I think that’s something aspirational for any company. I think, shame on any Chief Data Officer or CIO who doesn’t have a self-funding data warehouse or data lake; they just are—or if they’re just using it for generating financial reports and sales reports, then, again, shame on them.

Brian: Yeah. Let’s kind of switch over to this Chief Data Officer v4.0, which talks about this product orientation. For listeners who haven’t seen Gartner’s definition here, what is that and why is that significant? It sounds like it’s a counterpart to some other way of being a CDO, so what is that about?

Doug: So, I don’t know if I should give the Garner definition. I’m no longer with Garner, but I—

Brian: [laugh]. Or your own.

Doug: I might violate so—[laugh]—

Brian: Sure.

Doug: —I might violate something.

Brian: Use your own. Yeah.

Doug: So, the CDO is somebody, basically, who is accountable and responsible for the company’s data assets. And while you may say, “Well, isn’t that the CIOs role?” Well, yeah, perhaps it should be or should have been, but most CIOs act as if their middle name is infrastructure not information. They’re focused on technology and sourcing services and all that and not really managing data as an actual asset. So, the CDO role has come on strong in organizations that really want to manage their data as an actual asset, ensure that it is accounted for as generating value, is being managed and controlled effectively.

So, most CDOs play both offense and defense in controlling and governing data on one side and in enabling it on the other side to drive more business value. I actually long have been an advocate of bifurcating the IT organization into separate ‘I’ and ’T’ organizations, one that’s focused on information and the other that’s focused on technology. Where this leaves the CIO’s role going forward is debatable. I have lightly advocated for organizations to have a Chief Data Officer and a Chief Technology Officer, and perhaps dispense with the CIO role altogether. And some organizations have implemented that idea and are doing so successfully.

Brian: So, understood on the CDO thing. What is this product orientation—though—about?

Doug: Yeah, so it’s really about generating value from data and taking a product management approach to data to generate value, both internally and externally. I’m the first one to admit there’s not a whole lot new in Infonomics; it’s about adapting traditional ideas about measuring, and managing, and monetizing other assets, but applying them to data. So, taking a standard product management approach is one way to think about generating more value from your data.

Brian: So, theoretically if, where, that sounds good, I get that; I want to go that direction. What kinds of skills does a CDO that wants to champion this need? Do you go hire product managers? What kinds of people—or skill sets rather—not—it doesn’t have to be job titles, but what are the skills necessary to look at this pile of stuff in the lake and say, “Here’s opportunities?” There’s a lot of creativity that needs to go into that I would think.

Doug: Absolutely. Hire a product manager. Hire somebody who has developed data products before, go hire somebody from Nielsen, or IRI, or some data broker out there, Experian or S&P, and find somebody who’s been monetizing data before in that more traditional data marketplace and put them in charge of your data monetization efforts in your organization. Other critical roles are the role of the—I call them the ‘data curator,’ somebody who is out there identifying external data assets that potentially can be integrated with your own and monetized in new and innovative ways. And then the traditional data architects and other folks like that, data engineers and so forth. But yeah, to take a real product management approach, hire a product manager.

Brian: Yeah, yeah.

Doug: Yeah.

Brian: We talk a lot about this idea of product over a project on the show, and I’m curious if there’s been a business strategy to try to start monetizing data to bring more customer value, find new customers with data, et cetera, et cetera, is the challenge that organizations have more in the identifying the problem space and the need, or is it in actually making it? Like, “We know what would be valuable, but creating it is difficult.” That’s very different than, “We don’t know if anyone cares about whether or not we try to sell this stuff,” or we wrap it into a service or improve a digital service or whatever, and it’s like, throw it at the wall and see if anyone cares. Which of those challenges is harder?

Doug: I think it’s the lack of vision and leadership is the bigger challenge right now. Most companies already have the ability to integrate and create data products of various kinds, given their existing skills and technologies. So really, it’s a lack of vision and leadership and focus. You know, I understand. Listen, companies want to be focused on their core products and services, and thinking about creating a new product line that’s a data product line is something that’s a little bit outside of the box for a lot of companies. And so, that’s where we come in and we’ll help them do that or launch that.

We can even bring in financing now. So, working within an investment bank that says, “Hey, we’ve got a lot of money on the sidelines that’s looking for new investment opportunities. We want to take the same kind of approach to speculating in oil wells and speculating in companies’ data assets.” So, we’ll fund the exploration of a company’s data to look at whether there are opportunities. And if there are, then we’ll fund the development of the data products and then pay the owner of that data a residual, a royalty. And so for companies that really don’t want to be distracted with a data monetization effort of their own, this is a way to do that and still generate some additional revenue and an income.

Brian: Yeah, yeah. The more successful teams that I read about and I tend to see tend to be mixed skill set, they’re cross-functional; there’s a space for creativity and learning, there’s a concept of experimentation that’s happening there. Are these things necessary in doing this successfully, do you think?

Doug: Yeah, I would say so. Certainly, people who are subject matter experts, people who understand the data, people who understand product management, people who understand the marketplace for data, people who understand the needs of your extended business ecosystem. When we engage in these data monetization workshops with clients, we’re looking not only at uses internally and potential data products and uses of data among their existing suppliers and partners, but we’re looking at their partners’ suppliers, and their suppliers’ partners, and their partners’ customers. We’re looking at that extended business ecosystem for opportunities to leverage the data.

Brian: The process of developing this, I’m curious in terms of the people that you might bring in. So, do you think you’d be better off bringing in somebody that has demonstrated experience building data products in an entirely different industry, or someone who knows your domain super well, but maybe they haven’t done an initiative like this before? Do you see a clear answer there as to where to get started, if you were to say, you know, “We need to do the”—

Doug: I’d say the former.

Brian: The former?

Doug: Bring in the fl—bring in the fresh flesh—[laugh]. The fresh blood.

Brian: The fresh flesh?

Doug: Yeah.

Brian: [laugh].

Doug: Somebody who the flesh—[laugh] the fresh flesh.

Brian: [laugh].

Doug: Somebody who understands how to create data products and market it is more important. Understanding the data for a business, it takes a few weeks, or maybe a couple months to really get your head around, but the process of productizing data is a pretty unique skill.

Brian: Mm-hm. Can you tell me a little bit about where organizations maybe have that part right, but they get hung up? Where are they going to see friction in that process? Let’s assume they have brought in product management, and they have the right type of creative skills, and they have all the technology that could ever need; where are they going to get it wrong?

Doug: Yeah. One is an investment in focus and prioritization. So, companies want to focus on their core business, and selling data or creating data products may not be a core business for them. They’re not a Facebook, or a Google, or a Nielsen. But I think there’s an imperative.

We’ve seen that companies that become more data-driven have a market-to-book value that’s nearly two times higher than the market average. And companies that are data product companies that make the bulk of their revenue by selling data products or derivative data have a market-to-book value that’s nearly three times the market average. So, there’s really compelling reason to do this. It’s just not a lot of executives are really comfortable with it. Data continues to be something that’s really, kind of, amorphous and they don’t really have their heads around.

It’s not something that they’re accounting for because the accounting practices don’t compel them to do so. And data, when you start looking at it, it’s pretty complex. There’s a lot of redundant data, there’s data quality issues, there’s data flow issues, there’s ethical and legal issues regarding the use of data. And so once you start peeling it back, it’s pretty hairy for an executive to get their heads around.

Brian: Do you think that there’s still a tendency to want to throw data science at that at the beginning and say, “We need machine learning, we need AIm and that’s the same thing as data monetization?”

Doug: Is one way of monetizing data, and typically you’re using it internally for those purposes. And I’m a big fan of experimentation. There are a lot of pundits out there who say, “Well, 80% of data warehouse projects fail,” or whatever. I said, “Great. You know, fail fast, move on.” Build models, test them, A/B test them.

If they’re working, put them into production; continue to tweak them over time. Most of the high-value examples in this library that I’ve compiled are not enterprise-class, yadda, yadda kind of projects; they are targeted, vocational, functionally specific ideas that somebody had to analyze data or bring a couple different datasets together to generate some new kind of insight, or predictive, or prescriptive kinds of analysis. So, I’m a huge fan of experimentation. It’s just most companies that maybe—may be—comfortable doing R&D within their own market, but they’re not comfortable doing R&D with data yet.

Brian: Interesting. What’s the… is there a skill gap there? What’s the gap there?

Doug: I still think it’s a vision gap, for the most part. I also hear that a lot of data scientists spend a lot of time curating, and harvesting, and integrating data, and they really shouldn’t be doing that. So, don’t hire a data scientist unless you can pair them with a data engineer. You really don’t want your data scientist having to munge data. They should be the ones who are developing models.

And so the skills gap may be in companies that hire a data scientist without pairing them with somebody who can do data, right? And that’s a very quick way to not generate a lot of good results, and then also to lose your data scientist because they want to work somewhere better than that kind of environment.

Brian: Yeah, you hear that—I hear that frequently. Are there some other examples you can share? I’m particularly interested in very creative approaches teams may have taken. Maybe it’s a very esoteric example that maybe no one would have thought of before. Do you have any of those you could share with us from your research?

Doug: All right. So, there’s a—I’m meeting with—and I can talk about this because they talk about it publicly and have launched publicly—but I was meeting with the Chief Data Officer at Allstate Insurance Company. And he says, “Listen, the automobile companies, one in particular, wants to buy our claims data because they want to analyze it to build better and safer cars. We can generate a revenue stream from it, and then consumers get better and safer cars.” It’s a win, win, win—what I call a triple play—win, win, win for everybody.

Allstate said, “However, you know, our motto is ‘we’re the good hands company,’ and so we can’t be seen as selling our customer data, even if we anonymize it, redact it, encrypt it, et cetera—or aggregate it.” And I said, “So, this is not a technology issue. It’s not a data issue. It’s not a business model issue. It’s a brand issue. So, why don’t you take this idea off brand—off of the Allstate brand—and create a joint venture or a separate company or something like that?”

And they said, “Oh, yeah. Maybe that’s what we should do.” Within seven months, they had launched a company called Arity—A-R-I-T-Y—and that’s now a platform for monetizing not only Allstate data but other data for automobile companies and others. So, I think the creative idea there was to move this off brand and form a real platform for data monetization.

Another fun story was, I was advising a technology company that contacted me and they said, “Listen, we’ve built a better database for analyzing data. It’s better, faster, stronger.” I said, “So, is everybody and their brother claiming that they got a better, faster analytic database?” I said, “What have you tested this on? Have you proven it?”

And they said, “Well, we’ve ingested BitTorrent traffic.” I said, “Well, how much?” And they said, “All of it,” I said, “What?” “Yeah. We’re streaming all BitTorrent traffic into our database.” And they said, “We can actually predict Nielsen ratings a week before Nielsen can because we know what’s being traded illicitly over BitTorrent.” I said, “The real value in this business is not your database; it’s the data.” I said, “Why don’t you build a business monetizing this data?” And so that’s what they did. They’re now one of the top, one of the—Tru Optik is the name of the company—they’re now one of the top OTT—or Over The Top—media data providers. So, they entirely pivoted their company. Anyway, I’m not sure—

Brian: Yeah, yeah.

Doug: —if that’s what you’re looking for, but a couple of fun examples.

Brian: I think it’s good to hear just a wide variety of these to spark people’s creativity, and all of that. I did want to ask about the ethical piece, and like just—and I want to hear about your workshop, too and whether that comes into this because there’s the risk and compliance mentality that can go with ethics, which is about, “Let’s not get sued,” and then there’s the, “What kind of culture do we want to have? What kind of place do we want to live in?” And we have to pay the bills and pay salaries and all that in their businesses, so I get that. Where does this come in?

At what point do we think about ethics and is this the right thing to do? You know, Allstate talked about this, as well. On a quick glance, I would make the argument that I really like Seth Godin’s framing on this, which is, “Customers don’t want to be surprised.” And so, the question would be, if we’re making better cars, your data is private is totally anonymized. And yeah, they have a business over there.

The question is, would someone be surprised in a negative way about that? I don’t know. I haven’t heard a better framing yet to understand ethical issues, but where does that come into play in your process in your workshops, and just how you tell teams to think about this so that they’re not just blindly charging forward and, “Check the risk. The lawyer said, ‘yes.’ Check.” You know?

Doug: So, transparency is certainly one aspect. The other is that there’s a quid pro quo nature to it. There’s got to be a benefit to the consumer in the way that you use their data. And that benefit has to be clear, and defined, and ideally measured for them, that we’re able to reduce the price of this product that you use because we’re able to share your data, even if it’s anonymously; this reduces the price of your product. Imagine what people would have to pay for Facebook today if Facebook couldn’t leverage the customer’s data at all.

We’d have to pay, I don’t know, $100 a year or whatever for it, and it would diminish the value of the company. So yeah, surprise, transparency, and then making sure that there’s a clear benefit to the ultimate producer of the data: the consumer themselves. Yeah. One fun example of this is—that we’re all very comfortable with—is the grocery store. You go into the grocery store and you scan your loyalty card, and you get a discount.

Well, we know what’s really happening. It’s not—we call it a discount so we feel good about it, but what’s really happening is we’re exchanging information about us and our purchase for free food. And we’ve rebranded it as a discount just, again, to feel good about it, but this kind of thing is happening more and more in the B2B world as well. Even though the grocery stores aren’t entirely transparent about what they do with our data, we feel great about it because we’re getting free food.

Brian: Yeah, yeah. Tell me about your workshop. How does that work? Who’s it for? What’s it like?

Doug: The data monetization workshops is the initial step in the overall data monetization approach. And the workshops involve understanding what kind of data is available, laying it out, looking at new ways to intersect it, identifying external data assets from social media, open data, partner data, and so forth, and again, what might happen at the intersection of that data. We run through some hypothesis-generating exercises, we look at the four styles of analytics, we look at the extended business ecosystem and what are the business drivers of all those players in that ecosystem, and how can we help them achieve those drivers? And generally as we go through that workshop we’ll, within a couple of weeks, generate 30, 40, 50 new ideas for leveraging data both internally and externally. Then the next step is to refine those ideas and then prioritize them.

And when we prioritize them, we’re looking not just at ethical and legal factors, but we’re looking at the economic benefits. So, we’ll measure the potential economic value, the market size for this data product, we’ll look at how practical it is to generate. Is it actually marketable? Does it provide some societal benefit, as well, or ecological benefit? How manageable is it? Is the technology in place for us to be able to implement this? Is the data of sufficient quality and availability and timeliness?

We look at a dozen different data quality attributes. And then we also will look at the legalities and the potential ethical issues. Looking at—we have a list of ways that data can be used improperly, or risks that it can incur once it gets out in the wild, and we make sure to walk through each of those and determine the level of risk. And then we’ll see what bubbles to the surface; we map all that out and certain ideas will bubble to the surface as being higher in terms of impact and lower in terms of complexity, and those are the ones that we’ll typically attack. And then we’ll go into the rest of the process, which is about developing and testing the markets, identifying and curating the data, specifying the technical environment, defining the support, and maintenance, and monitoring, and reporting requirements, and then architecting and building out the solutions.

Brian: Is this something you I would assume you do with a single private team for—it’s not a public kind of format?

Doug: Right. It’s generally a private team. These kind of workshops don’t really work well publicly because you’re talking about different company’s data. But we do encourage our clients to bring in partners and suppliers to represent in these workshops—again, so we’re not just staring at our own navel, but bring in some of your friendly suppliers, partners, customers to see what ideas that they might have to participate.

Brian: Got it, got it. Doug, this has been really great. Tell me where can people find you, Infonomics your book, and information on your workshop?

Doug: Yeah. So Infonomics, the book is available on wherever books are sold, other than in stores. It’s only in a few stores [crosstalk 00:32:46]—

Brian: [laugh]. Everywhere except a store.

Doug: [laugh] everywhere except a store. Yeah. Online, it’s available in hardcover, ebook, and audio book. Thankfully, I don’t read the audiobook, I got a professional to do it.

Brian: James Earl Jones—

Doug: I read the intro.

Brian: —reading your book?

Doug: No, I wish.

Brian: [deep] Infonomics.

Doug: He’s a little pricey.

Brian: [laugh].

Doug: And then you can reach me at West Monroe, dlaney@westmonroe.com, or on LinkedIn; always happy to connect with folks on LinkedIn, discuss this topic and discuss how your own organization might be able to generate new value streams from your data.

Brian: Got it. Got it. And westmonroe.com to learn about the workshop?

Doug: Yeah. Yeah.

Brian: Okay. Excellent.

Doug: I also teach at University of Illinois, the MBA class, and then those materials are also available on Coursera for anyone who just wants to take the Infonomics course for a certificate.

Brian: Oh, okay. Great. Yeah, we’ll include that in the [show notes 00:33:38] as well. So Doug, thanks. This has been a really great chat.

Doug: Thanks, Brian.

Brian: I appreciate talking about Infonomics with us.

Doug: Real pleasure, mate.

Brian: All right, cheers.

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