Infonomics Transcript
Is It Time to Split Up the Information and Technology of IT?

Read the transcript of my podcast with Doug Laney, who currently heads the data and analytics strategy consulting practice for Caserta. Doug is also on the faculty of the University of Illinois Gies Business School and is author of the book, Infonomics. In this podcast we discuss how information needs to be treated like an asset, how data is potentially more valuable than oil, how Infonomics applies to the Digital Age, what specific behaviors organizations demonstrate that have achieved information maturity, and finally, how to determine the return on information assets for a company. It follows:

Peter Schooff: Welcome to another Data Decisioning podcast. This is Peter Schooff, editor of Data Decisioning, and today I'm absolutely thrilled to be speaking with Doug Laney, the father of Infonomics. Having researched and recently published a widely acclaimed book on the topic, Infonomics introduces a novel approach for business and IT leaders to measure, manage, and monetize information as a true corporate asset. In this discussion, we're going to drill down on how implementing these groundbreaking ideas can help any organization thrive in the digital age. I have to say this is an absolutely vital podcast for businesses. Alright, so Infonomics—How did you develop and conceive of the concept of Infonomics?

Doug Laney: I've always been fascinated by data and the possibilities of data and how data is organized and can be used, but really, it kind of hit me hard after an unfortunate event,which was the 911 terror attacks. Some clients started calling me at Gartner, lamenting not only the tragic loss of life, but also the loss of their data. This was in the days before a lot of cloud and off site backups, so businesses in the Twin Towers lost their data as well. And it became a bit of an existential event for them. So naturally, what some of them did was submit claims for the value of the data that they lost to their insurance companies and the insurance company said, "Well, hold on a second, we don't think that data constitutes property and therefore it's not covered by your PNC policies."

But it was somewhat unclear, and there were a number of court cases that ensued. But then the insurance industry realized it was exposed. And it updated the commercial general liability policy template to explicitly exclude data from PNC policies, they did that barely a month after 911. So that kind of caught my attention and I was like, "Well, hey, isn't data an asset? You know, I think it would be the criteria of an asset."

So I cracked open my accounting books and, lo and behold, an asset is something that is exchangeable for cash and generates probable future economic value and is owned and controlled. And so I was thinking, you know, information really meets those criteria, why isn't it considered an asset on the balance sheets? In fact, the accounting profession realized that it was exposed as well and said, "Well, if the insurance industry is not going to recognize data of property, we're not going to recognize it as an asset."

And therefore, they updated a key accounting standard to prohibit organizations from capitalizing or recording the value of their data on their balance sheets. So even if you're a database business, like Google or Facebook or an Amazon or something, you can't put the value of all that data you have, even if you're a data broker like Experian, or Dun and Bradstreet, you can't put the value of that data on your balance sheet. So it occurred to me that maybe this is a reason why a lot of companies aren't really managing their data because they don't really understand its value characteristics. So then I started working with some colleagues and clients and accountants and economists and valuation experts to try to understand, you know, what would it mean to consider data as an actual asset in terms of valuing it—even just internally.

PS: Well, we're firmly in the digital age, and it seems like everything's happening from IoT to artificial intelligence to machine learning. Now, how has that impacted Infonomics today? Does it change your advice at all?

DL: Not too much. But I would say puts a little more pressure on organizations to do more with data. All this data they're collecting through IoT and other digital business channels to collect and compile and generate potentially valuable data. From an AI and machine learning standpoint, it puts pressure or opportunity on the things that you can do with data to monetize it or to generate economic value. We've talked as part of Infonomics, you mentioned monetizing data, and really what we mean by that is to generate economic value, whether that's internally or externally and qualify that measurable economic value. That's the way to monetize any asset. 

I also think this puts some pressure on our organizations to bring on board or anoint somebody as a data curator or have a data procurement department. You see most organizations have a whole department in charge of procuring office supplies but they don't have anybody in charge of procuring data supplies. And so a lot of the valuable data for organizations comes from external sources, like harvested web content, or open data, or syndicated data from data brokers, or exchange for partners or suppliers, or are available over data marketplaces, or social media as well. So there's a lot of external data out there that companies tend to be ignoring that has great value.

PS: So it seemed like 9/11 was kind of a line in the sand. So here we are almost 20 years later, how do you measure information as an asset, because companies clearly pay attention to ROI?

DL: So, I'm kind of the first to admit that there's really nothing, or not a lot, new about Infonomics. It's about borrowing or leveraging ideas from other disciplines and applying them to information—really just treating information as an asset. So when we look at measuring other assets, we use three main approaches: cost approach, the market approach, or the income approach, you know, to determine what is the cost basis of generating or collecting or buying that asset? What is the market value if we were to sell it or license it to others? And then what is its impact on an income stream or expense savings? And so that's the economic value-add. 

So there's really not a lot new there. But what I've done is adapt those models for some of the nuances of data—like, you know, data, when you use it, it doesn't get used up. So you can use it again and again, and you can use it for multiple purposes. And when you sell data, you're really not transferring ownership of it, you're licensing it. So we've modified the models to accommodate that. Now, some companies are not really comfortable yet quantifying data in financial terms. So we've developed some foundational models as well that look at kind of an aggregation of data-quality characteristics and data scarcity, or data utility, or utilization, or relevance.

Then there's a third one that looks at data assets' impact on nonfinancial key performance indicators. And so what gets really interesting is where some of our clients have combined these models or juxtaposed them to identify, say, data that has a high potential value but low economic value and said, "Hey, this is data we need to do more with if we're going to generate great ROI from it," or, "we should just dispose of it altogether."

PS: Really interesting. So it really does sound like they need to read Infonomics, if they haven't already. Now, I took a pretty close look at your book, and you bring up the term information maturity. Can you tell me what a company looks like in their day-to-day operations that has achieved information maturity?

DL: Well, I guess there's some outcomes. One of the things that we found is that companies that demonstrate certain kinds of information savvy behaviors—they're rewarded by investors. Companies that demonstrate certain behaviors had a market-to-book value that's nearly two times the market average. And information product companies have a market value that's nearly three times the market average. But in terms of maturity, there really are multiple dimensions to look at: Like what is the company's vision for using data? What is its strategy, its actual approach? How well is it governing data? How well is it set up from an organization and roles perspective? Are they measuring various data quality aspects or the ROI on data and analytics? How well established is the architecture, the lifecycle of data, and of course, the technology (how mature is the technology)?

So we look at several dimensions, and at Caserta—where I'm now leading the strategy practice—we rolled out a maturity model that looks at over 200 discrete characteristics of an organization within the context of those eight dimensions to help them determine areas that needs work. And then they can benchmark themselves and track themselves over time as well. 

PS: At Data Decisioning we do have a focus on decision making. So in this Infonomics approach, how does it help leaders make better decisions?

DL: So I would think decision making is part of that. I mean, more and more, we're concerned with things like automation and using data for automation or even just selling data outright. Not even using it to make decisions ourselves, but to enable our partners or suppliers or customers. So the focus long has been on the data driven organization in terms of decision making, and moving beyond hindsight-oriented analytics to more foresight-oriented analytics, like diagnostic and predictive and prescriptive kinds of analytics. So Infonomics helps to pull organizations along to think more about what is the actual value proposition for the ROI or the return on information assets that we're getting? And what are the variety of ways that we can deploy data including decision making, to generate value from data, but again, it's just all about decision making. We can also sell data, we can use it to automate processes as well. 

PS: I definitely think the insights around Infonomics are quite persuasive. So where would you say is the best place for an executive to begin?

DL: Well, probably to put somebody in charge of data. I have long advocated for the bifurcation of the IT organization to two separate I and T organizations. You know, we've coupled information and technology for a long time because, well, data was kept with inside applications and programs, you're dating back 30, 40 plus years. But we're in a world where data and applications are distinct and can be maintained separately and used for different purposes. So I think it makes sense for an organization to have somebody separately running the technology part of the organization and then separately managing information as an actual asset. 

You know, it's a weird idea. but back in the 1960s, Dr. Gary Becker, under the tutelage of Milton Friedman at the University of Chicago came up with the idea of HR organization, or human capital, and thinking about generating value from labor and workforces, and much the same way as we generate value from other assets, was a really novel approach back in the 1960s. We now take it for granted. And so I'm just kind of standing on the shoulders of giants and saying, "Hey, maybe we should think about data as a separate asset and manage it separately." So that's one thing I would say.

Also, you know, talk to the CFO and say, "Hey Mr. or Ms CFO, I understand data is not a balance sheet asset, but we want to start treating it like it's an actual asset to generate more value from it, or at least to control costs. And, you know, let's think about creating an internal supplemental balance sheet for the value of data." And those conversations go pretty well. I should also mention, you know, we talked about the separate data organization—obviously we're seeing the rise of the chief data officer to take on that mantle and, you know, what happens to the Chief Information Officer role, the CIO role, you know, it may devolve into one that's just a technology officer. In fact, we've seen some clients who now have no CIO and have a chief technology officer to manage the technology assets or the infrastructure and a chief data officer to manage the information assets.

PS: Now we've brought up value add with data. So how about negative ROI. What do you see as a company today that's like, "Data. Nah, I don't need that." What's going to happen to that kind of company?

DL: Well, I wouldn't invest in them, that's for sure. Secondly, I wouldn't work for them if they're not using data well. I mean, we're well into the information age or the, you know, the data economy, and an organization that chooses to continue to treat data as a business byproduct or an occasionally nice to have resource or its fixated on building, you know, pretty pie charts and bouncy bar charts and dashing dashboards instead of actually doing more advanced analytics. They're not going to thrive or survive in this information age. The costs will be probably higher costs for the data, lower economic value generated, they'll have data quality issues that are systemic, and limit what they can do from a business perspective. I think, you know, ultimately, it comes down to you can't manage what you don't measure.

And I think conversely, you can't monetize what you don't manage. And so for a lot of companies this tends to be a bit of a vicious cycle. You know, not measuring their information, so they're not managing it as well as they manage other assets. And if they're not managing it, well, then they're in a poor position to generate economic value from it. So the idea behind Infonomics was to really turn that into more of a virtuous cycle than a vicious one.

PS: Indeed. Now, I like to close out my podcasts with—I know this is a huge topic and Infonomics hit pretty big. Now, if you had to boil it down to one takeaway, what's the one takeaway you'd like listeners to come away with from this podcast?

DL: You know, it's hard to go a day or a week without hearing somebody talk about data as the new oil. And while that certainly reflects an understanding that data has value, it totally misses the point. That data has these unique economic characteristics that make it potentially more valuable than oil. Again, it's something that can be reused over and over, it can be used multiple ways simultaneously, and wherever you're using data it typically generates more data that's valuable. So it's what economists would call a nonrivalrous, nondepleting, regenerative asset. And companies that get that and bake that into their business model and their overall enterprise architecture are really going to thrive in the information age.

PS: Fantastic, absolutely excellent information. This is Peter Schooff of Data Decisioning speaking with Doug Laney, the principal data strategist at Caserta. For those who haven't already picked up the book Infonomics, I urge you to do so. Thank you so much for joining me today, Doug.

DL: Thanks for having me.

Listen to the podcast: Revolutionary Ideas For More Value From Data—Speaking Infonomics With Doug Laney

Peter Schooff | Feb. 7, 2020