Data is an intangible asset with unique economic properties. What are some of the strategies we can use to monetize it? Follow me as I unpack this with Doug Laney, author of the book "Infonomics".
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[00:00:00] Loris Marini: Today I’m speaking with Doug Laney, an absolute authority on data and analytics strategy. This is a man who doesn't need any introduction, but I'm going to do it anyway.
Doug is a three-time recipient of Gartner’s annual Thought Leadership Award. 20 years ago, he defined the term big data as being characterized by the large volume, velocity, and variety - the three Vs of big data. Doug is a thought leader, consultant, advisor, author, speaker, mentor, instructor, and a data strategy expert on everything involving data strategy.
After many years of being Gartner's Chief Data Officer, doing research and advisory practice, Doug joined West Monroe as a specialist in data monetization and valuation on how to leverage big data to drive innovation. He regularly writes for Gartner, Forbes, and Information Management magazine. He has been published in the Wall Street Journal, The Financial Times, and he recently authored the bestselling book. Infonomics: How to Monetize, Manage and Measure Information for a Competitive Advantage. And that's exactly what we're going to talk about today. So, Doug, let me give you my warmest 6 AM welcome to The Data Project.
[00:03:12] Doug Laney: Hi Loris, great to be with you at 3 PM here in Chicago.
[00:03:17] Loris Marini: Fantastic. Let's dive straight into it because I'm being mindful of your time. How should we think about asset and information assets? What’s the difference?
[00:03:31] Doug Laney: So, an asset is pretty well-defined by accounting regulations as being something that is owned and exclusively controlled, exchangeable for cash. And that generates what accounts call probable future economic benefit. Of course, if you took accounting back when I did, accountants hadn’t come around to recognizing that an asset has probable future economic benefits, their focus was more on the current benefits that it was generating. Which is interesting because there are a lot of pundits out there who will suggest that data only has value when it is used, which is entirely inconsistent with the way that valuation experts and accountants value other assets. I know we were kind of going into the data talk in a little bit, but yeah, an asset is something that's owned and controlled, exchangeable for cash, and generates probable future economic benefit.
It also needs to be separable from other assets. That's how accounts define it. And I think there's no argument, no debate that information meets those criteria. It's just that the accounting profession globally hasn't come around to recognizing data as an asset, only under some very rare circumstances.
[00:04:57] Loris Marini: Yeah. And I do believe that this is sort of the core issue of a lot of dysfunctions that we see in the data industry. And hopefully, we can try to unpack some of those today.
[00:05:06] Doug Laney: Dysfunctions as well.
[00:05:09] Loris Marini: I mean information yes, it's an asset, but sometimes it's hard for people to get their head around it.
Some might say it's because you can’t touch it. It doesn't have weight, no physical form. So, in a sense, it's sort of intangible but it's definitely not the only example of an intangible asset.
[00:05:36] Doug Laney: No, there are other intangible assets that you can't hold and get your head around very well. Copyright or trademark or even a patent, those are recognized intangible assets that are recognizable on corporate balance sheets and they've been so for a hundred years, since the asset classes were defined, coming out of The Great Depression.
Those were defined asset classes, but information wasn't and you might ask why, and probably because most information at the time was physical. It was in books or magazines or ledgers. And we really didn't think about it as an asset separate from its physical manifestation. But in today’s world that thinking doesn't really hold.
[00:06:27] Loris Marini: No, it doesn't.
[00:06:28] Doug Laney: So here we are nearly 100 years later, almost 190 years later and the keepers of the definition of what constitutes an asset haven't come around to even recognizing their own definition and how it applies to information.
So, it's a bit frustrating. And like you say, it's something that contributes, I think, to the mismanagement of data, because we don't recognize it as an asset. We can talk about that.
[00:06:56] Loris Marini: Yeah, absolutely. Just one note on this. You mentioned a hundred years and it made me think about the two biggest events that shaped the landscape of technology then. I believe it was in 1947 when the team of William Shockley in Bell Labs figured out a way to build the equivalent of a water tap for electricity, the transistor that was then the building block for everything that is digital today.
And around that time, I think it was Claude Shannon that devised the theory of information. They really built the mathematical framework to understand what information is. Super abstract stuff.
[00:07:46] Doug Laney: How we compress and send data today.
[00:07:50] Loris Marini: That's exactly why we can talk right now.
[00:07:53] Doug Laney: These models are at the core of that, even today.
[00:07:55] Loris Marini: Yeah, absolutely everything. I mean, I think 97% of the internet traffic goes through optical fibers under the ocean. And that technology is only possible because of those events. They’re the reason why we can build such a reliable system and how data can flow through these systems at any point around the planet.
We don't think about it, but the whole underlying framework is information theory. To the experts in those fields, it makes sense then that information is something valuable. But when you move into a business lens and say, “okay, this is the information we already generate no matter what, because we do stuff in the world. We interact, we have relationships. There are many other ways we can monetize it.” And that's where you come in. You take that lens and say, “I'll show you the many ways you can put a dollar value on it.”
[00:08:57] Doug Laney: Right. So that's part of Infonomics: monetizing data. You're thinking about the variety of ways that an organization can generate measurable economic benefits from data.
Monetization isn't just about selling data, but it's about generating economic benefits from it. And we've identified that there are eight or nine kinds of ways to generate value from data, in addition to selling it.
[00:09:27] Loris Marini: Do you want to dive into this? I mean, nine ways, that's way more than what I was thinking.
[00:09:33] Doug Laney: They've turned into what we call direct and indirect methods for data monetization. Indirect methods are ones in which data is used internally to generate and get measurable value. And so, we can use data to improve the performance of a process. We can use data to identify opportunities to develop new products or enter new markets. We can use data to enhance or even digitize existing products or services. We can use data to forge or streamline partner relationships as well and do so in ways that are measurably beneficial to the organization. So that's what we call indirect monetization because we're not really getting anything contractually in return for it.
Now the direct data monetization methods are more about externalizing data and making it available to others by either bartering or trading it in return for goods and services or perhaps favorable commercial terms, like discounts or things that are contractual that you might have between trading partners. We can also sell raw data, either by ourselves or through an emerging class of data brokers and data marketplaces that are out there. We can, instead of selling the raw data, offer insights or analysis, or reports at various levels. And then more recently clients have become concerned that they cannot monetize their customer data because of various privacy regulations, GDPR regulations in various countries and states.
And my response to that is, “Listen, you're not really thinking creatively enough about it. Yes. I can't sell you my customer data, but I can sell your stuff to my customers without ever exposing who those customers are.” I refer to that as an inverted data monetization model. It's really kind of a classic referral model, where I'm referring you customers and getting a commission or referral fee or something like that. Or I'm referring your stuff to my customers. Again, I'm not exposing who those customers are. An example would be, we're working with a hospital and the hospital knows who its diabetes patients are, but it can't sell that data, but it can sell to those patients others' products and services like healthy meal plans or gym memberships or at-home glucose monitoring test kits. And then again, take a referral fee from that or commission.
So it's entirely possible to monetize customer data, even in light of these privacy regulations, it just takes a bit of creativity.
[00:12:29] Loris Marini: Yeah. Is this the part in the book where you mentioned that is about building a trust network? It’s where you have a version of the truth that’s reliable and you’re open to share it with the rest of the world and build relationships.
[00:12:43] Doug Laney: Yeah. I mean, there are benefits to sharing data with others, particularly your partners or suppliers, if not the general world. You go into the grocery store and you scan your loyalty card. And you get a discount. But we know what's really happening. What's really happening is it's a barter transaction. I'm exchanging information about me and my purchase for free food. And we don't call it a barter transaction because it doesn't feel good. The discount feels good. The store doesn't really want to have to disclose what they're doing with that data. It's just part of that contractual arrangement. You joined the loyalty club and you get those discounts. So, that's part of that whole trust network as well.
Interestingly, one of the advantages of bartering with data versus selling it outright is that you avoid the tax implications. So, when I get free food at the grocery store for scanning my loyalty card, I'm not paying tax on that food.
[00:13:48] Loris Marini: Huge advantage compared to any other.
[00:13:50] Doug Laney: There are particular advantages to bartering with data over and above selling raw data or licensing it, or offering insights or reports.
[00:14:05] Loris Marini: But before we dive any deeper, I just want to make a quick mention that there is a growing concern in the general public that these types of practices of using information, particularly personal information to improve how marketing is targeting their advertisements to sell more stuff, sometimes without your explicit consent. It’s something that makes people feel a little bit uncomfortable.
And for the purpose of this discussion, I want to share my view. I think of information akin to the gravitational field of the planet. It's a potential energy. And information is contextual. If you tell me something that I already know, that has zero influence on me. But if you told me something new that conveys a lot of information and influence. So, it really depends on who consumes that information too. We could go on and on and make parallels between the two. But I guess what I'm trying to say is that information is a potential, and it's up to you how you use it. And hopefully, we’ve evolved in that we have a number of regulations that define the terms in which information can be used and leveraged. There is a regulatory framework around it, and we can feel comfortable with that.
Now, what can we do as business leaders to take that information and turn it into a potential to do more stuff, to grow our relationships, to sell more products?
[00:16:00] Doug Laney: Unfortunately, most companies will focus on using data for a single operational purpose and then throwing it up on a dashboard or a scorecard or some kind of report and that's it.
[00:16:13] Loris Marini: Let's dive into that then. After 20 years at Gartner, how do you define information management? I mean, surely you built a view of how we’re doing, how well we're managing information. What are we missing?
[00:16:31] Doug Laney: Well, maybe it's a certain kind of bias based on the type of clients we speak to at Gardner, it's like a psychiatrist who doesn't know any normal people because they only speak to people who are having issues. At Gartner, we sort of feel the same way about our clients, but over the years, I've kind of realized that I think the reason that companies don't manage their data particularly right is because they don't measure it. They're not measuring it the way that they measure their other assets. Most companies will manage their office furniture or calculate the value of their office furniture more so than they do their data assets because we're not compelled to by accounting practices.
I think the way this all fits together is there's the old adage, “You can't manage what you don't measure.” Where you can't manage well what you don't measure well, and I think that's the crux of why organizations don't manage their data as well as they do their other assets, because they're not really compelled to, they don't know what data they have.
They don't know what its potential value is. They don't know what the actual value is. They don't know what its cost basis is. They don't know its potential market value, its contribution to revenue, or expense savings. None of that is measured by most organizations for their data assets, but they do measure that for their physical and financial assets, and sometimes even their human capital, which isn't an asset of course, because you can't own people anymore.
[00:18:06] Loris Marini: Thankfully.
[00:18:06] Doug Laney: Human capital is not on the balance sheet, but just an aside. I like to stand on the shoulders of giants. And one of those is Dr. Gary Becker at the University of Chicago who was working under the famed, Milton Friedman, who came up with the idea of human capital. He said, even though human capital can't be capitalized on the balance sheet, we still can apply asset management principles and practices to the way that we manage workforces and labor to get more value out of them. And, he was entirely right. That led to the modern human resource organization today. I think we can go even further with data because data should be a balance sheet asset. It meets all the criteria. And then the other part of the equation is any asset that we’re not managing particularly well is one that we're not going to be able to generate value from or monetize.
You talked about the three Vs: volume, velocity, and variety of big data. And I didn't come up with the term big data, but just the three Vs. Now I've got kind of the three M's, which are Measuring, Monetizing, and Managing data as an asset. The way they fit together is you can't manage what you don't measure and you can't monetize what you're not managing well. And then that which you're not monetizing well doesn't generate a lot of value. The idea behind Infonomics is to reverse that curse, to flip the script to get companies to start measuring their data so that they get the budgets that they need. They can prioritize data management activities and get the resources they need. And then once that data is managed well, then they're in a better position to generate value from it in a variety of ways.
[00:19:54] Loris Marini: Absolutely. For the listener, I am the CEO of a company and I built an intuition over the last few years that we are largely undervaluing and under-leveraging our information assets. And I want to do something about it.
[00:20:11] Doug Laney: Huh.
[00:20:12] Loris Marini: What would be the first step?
[00:20:15] Doug Laney: Yeah. So, this was something that we've done with clients, helping them measure the cost basis of their data so that they at least know the baseline of what it's costing them to collect, generate, store, secure, manage the data. And that cost is maybe related to infrastructure costs, but we can actually attribute it to specific data sources.
And then with that cost basis in mind you go, “all right, well, we need to generate margin on this stuff. How do we calculate the margin, the profit on top of this data, the ROI on the data over and above the cost basis?” If companies aren't measuring that, then they're probably storing and capturing data that is costing them more than the value it's generating.
And we've seen this with companies who have come to that realization and then made a defensible deletion decision it's called, and are saving millions of dollars a year and unnecessary infrastructure costs. They've taken a look at it and they've said, “oh wow, we need to do more with this data to recoup the investment in it.”
And we need to start looking at new ways to generate value by selling it, licensing it, packaging it up, using it better internally. And so, for all those ways that I mentioned, the good thing about data is it can be used over and over again. And it doesn't deplete when you use it. So why wouldn't an organization try to come up with a variety of ways to leverage their data?
[00:21:45] Loris Marini: Yeah. Another aspect here that you mentioned in the book, which I absolutely love, is the non-rivalry aspect of data.
[00:21:51] Doug Laney: Yeah. That kind of comes from the discussion of data as well. A lot of people will equate data to oil and even say data is the new oil. And it certainly represents that they understand data as a driver of the economy. There's no doubt about that, but it misses the point that data has a unique economic characteristic. It can be used again and again. It's nondepleting. It can be used in multiple ways simultaneously. Try that with any other asset. That means it's non-rivalrous and it typically generates more data whenever you use it. So those are some really unique economic qualities of data that companies who get that, whether it's inherently or more explicitly, are really the ones that are thriving in the economy today.
Jeff Bezos talks about the flywheel concept, right? And his business is using data to generate more data. And using data to generate more sales and the sales generate more data. And then the data generates more sales. It's a flywheel concept that they've got there. It's working really well and certainly something that other organizations should strive to emulate at some level.
[00:23:07] Loris Marini: One of the reasons why I love your work is because it goes straight into the core of the problem of that dysfunction that we mentioned at the beginning.
[00:23:16] Doug Laney: I'm a simple guy.
[00:23:17] Loris Marini: Wow. And with a huge brain.
If you talked to data practitioners in the industry, there's a chance that 9 out of 10 are deeply frustrated in one way or the other. You talk to the data scientist and you're like, “I'm asked to build a ridiculously complicated model using the latest in advanced deep learning or reinforcement learning.“
[00:23:42] Doug Laney: What frustrates them is that the data isn't prepared for them.
[00:23:46] Loris Marini: Exactly. And we spend 80% to 90% of our time doing, teaching, and talking about databases and stuff that the engineers should do. You talk to the engineers and they were like, “well, we're developing new features here. We don't have time to deal with it. So an architect should put the pieces together.” Then you talk to the architect or whoever, maybe the CIO, the person that's responsible for building the tools and ensuring that interoperability, and they go, "well, we don't really do that. We just find the best software for the job. And then we hope that everything fits together.”
[00:24:21] Doug Laney: In IT, the T is way bigger than the I. In most cases, CEOs are really Chief Infrastructure Officers, or at least they behave that way. And that's given rise to the role of the Chief Data Officer to kind of balance that out.
[00:24:42] Loris Marini: Do you see CDOs as lagging behind or a growing function?
[00:24:49] Doug Laney: Oh, they’re definitely growing. In fact, they’re being mandated in some industries.
[00:24:52] Loris Marini: Yeah, and if the business understands that there's monetary value around information, then they will deploy the resources. And then it's a matter of figuring out how you’ll get there, but the why comes first. We first need to establish the why.
[00:25:10] Doug Laney: Not just the monetary value, there's the risk side as well. Companies are going to plug that gap. So, kind of a high-functioning CDO is going to balance what we call the offensive defense, the offense side of generating value from data. And then the defense side of reducing data-related risks.
[00:25:28] Loris Marini: Yeah. And so let's take the perspective of the CEO for a second, because my question is, between a CEO and CDO, who should ultimately be responsible for driving the types of initiatives, trying to monetize information within their organization? What will be their challenges?
[00:25:48] Doug Laney: Between who?
[00:25:48] Loris Marini: Between the CEO and CDO.
[00:25:55] Doug Laney: You know, there are some people calling for a Chief Data Monetization Officer. I think that's a little bit too far. We love our chiefs here in the US, I guess. But I think it should be the responsibility of the Chief Data Officer to at least set in motion the plans to drive more value from data. Obviously, that involves a lot of partnering and interaction with business functions and other executives. But the CDO should be the driver of that for sure.
[00:26:36] Loris Marini: Based on what you've seen at Gartner and with organizations that don't have a CDO, they have an existing CIO, they understand that this stuff matters and they want to do something about it. I hear a lot about the struggle of CIOs that they find themselves almost out of place because everybody's talking about the data monetization and the CDO, but they have been sort of overlooking the infrastructure for such a long time.
And they all of a sudden feel like second-class citizens in the equation. What do you see are the challenges between the different roles and responsibilities at the C-level? How can they ensure that people act as an organism because data flows horizontally across marketing, sales, procurement?
[00:27:35] Doug Laney: It's a tough question. And for a lot of organizations, I've long advocated for the bifurcation of the IT organization into separate I and T organizations. There was a time back in the eighties, maybe nineties, where data and technology were very tightly coupled. That's not the case today. We see data being moved to the cloud and into an application. That decoupling is entirely possible with almost all kinds of data and applications today. So, there's really no reason to manage data and infrastructure at the same time. They can be managed separately. And you really need to in order for a company to be more efficient and effective.
So where does this leave the CIO? Well, maybe it leaves the CIO out. I've actually seen some companies who have said, “listen, we don't need a CIO anymore. What we need is a Chief Data Officer and Chief Technology Officer.” And that works for them.
In other cases, the CIO has become more of like a Chief Digital Officer or maybe more of a senior executive that also has a Chief Data Officer and Technology Officer under them. There's no real right way to go about it, other than having a distinct executive, who's responsible for the enterprise data assets.
[00:29:02] Loris Marini: Absolutely. A little bird told me that you’re writing another book. Can you tell me what it’s about?
[00:29:14] Doug Laney: A little background on that. When I re-joined Gartner over 10 years ago, they said, “Listen, you're the big data guy, you're gonna research big data. The questions from clients quickly evolved from 'how do we do big data' to 'what do we do with all this big data?'” So, I was like, “oh, well, I started collecting use cases of how organizations are using data and analytics in innovative and high-value ways.” The use cases turned into fifty, then they turned into a hundred. I now have over 500 in my collection and every industry, geography, type of data, and type of analytics are reflected in it.
I've been giving presentations on that, but you can only present so many of them in 50 minutes. I used to give a session called 50 Shades of Data where I present 50 examples in 50 minutes. So that was kind of fun, but right now I use these examples to inspire clients.
When we run data monetization workshops, part of that workshop involves going through a set of examples of what others have done inside their industry. Maybe even outside their industry to kind of inspire or sometimes shame them into action or generate ideas. There's been a lot of calls from people saying that they want access to this library and I don't share it readily, so I'm putting it into a book.
I've taken a hundred of my favorite stories, representative ones, and written them up. I was going to do an analysis of each one, and then I said, “well, why does anybody wanna read a hundred analyses by me? That would be pretty homogeneous. I know a bunch of people in the industry. So I conscripted a hundred data and analytics leaders and thought leaders and practitioners around the world. Each one of them commenting on a different story. And so, each story in the book includes the story and then an analysis of that story. I'm really excited to publish it sometime this year.
[00:31:28] Loris Marini: That's going to be another gem for sure. And I can’t wait to read it.
[00:31:30] Doug Laney: Yeah, it's going to be fun. It's not going to be as prescriptive as Infonomics. It's certainly not going to be as theoretical. It’s much more toward the practical and more of a reference guide for organizations that are looking to be inspired or shamed into doing more with their data.
[00:31:48] Loris Marini: Yeah. And I didn't mention this at the beginning, but I know that you're pretty strong on education as well. You have a course; you teach at university. Well, how did that come together?
[00:31:57] Doug Laney: I'd met the dean of the University of Illinois business school, one of the top 30 global business schools, and I told them what I was working on and he got pretty interested and he said, "would you like to teach a course on that? We could be the only university in the world teaching a course on Infonomics.” And I said, “well, let me write the book first, and then we'll use the book as the basis of the course.” And so, I've been teaching it now for a few years. And the first cohort had 12 students. The next one had 60 and I just finished up the third year of teaching it. And that class had 400 students in it. I don't know, I don't know what to expect next time, but it's getting kind of unruly.
[00:32:42] Loris Marini: Growing by X, year by year.
[00:32:43] Doug Laney: Why do I like teaching? Not only to give back to my university, to my Alma mater, but I learn from the students.
Infonomics is just the tip of the iceberg here. There's so much more to get into in terms of how to monetize, manage and measure data as an actual asset or how to apply economic models to data. And that's one of my favorite assignments that I give the students. We continue to build and expand the thinking on this topic, which is pretty nascent, right? And so, some of the assignments I have them do, one is to identify any economic concept, macro, micro, whatever it is, whether it's externalities or marginal utility or monopolies or government influence or whatever the case might be, and then apply them. Or think about how that economic model applies to data? Does it work? Does it break down entirely? Could we reconfigure it in some way to make it useful with data? Almost all economic principles were designed with traditional goods and services in mind. These models have never been applied to data before. There's one chapter in the book where I look at about six or seven different economic models and how they apply. But I challenged the students to look at some others and I'm really excited to be presenting some of them next week at the 15th annual MIT Chief Data Officer symposium.
It's going to be fun. I'm going to try to do justice articulating some of these ideas that my students have come up with. Another fun assignment that they do is identify any data-related regulation. In light of that regulation that tends to prohibit doing things with data, find a way so that they can do something with data. Find a loophole or a way around that regulation. Whether it’s a healthcare regulation like HIPAA here in the US or the GDPR or some kind of industry regulation. Some of the students have been really creative at identifying ways to still monetize data or generate value streams from data, even in light of these regulations. And in some cases, they found some real gaping holes in these regulations. So that's a fun assignment too. I'd love to publish these as well. And I'm trying to get approval from the university first, to publish the adaptive, economic ideas, because I have 60, 70 papers from students on those.
[00:35:36] Loris Marini: Okay. That’s plenty.
[00:35:37] Doug Laney: I mean, they're not terribly academically rigorous. These are MBA students. They had one week to work on it together, but they came up with some really interesting ideas that I think could form the basis of some real deep research. My goal is for one of my students to go on and really study this stuff at a Ph.D. level and win a Nobel prize.
[00:36:00] Loris Marini: There you go. Just a small ambition.
[00:36:02] Doug Laney: Maybe one of my students.
[00:36:04] Loris Marini: Yeah. Speaking of tapping into your wisdom and knowledge, are there workshops online or in-person only?
[00:36:13] Doug Laney: Yes. The University of Illinois has a relationship with Coursera, so all courses in the MBA program are also available on Coursera and can be taken for a certificate instead of a degree.
[00:36:31] Loris Marini: Right. What about the hands-on workshops?
[00:36:34] Doug Laney: They're going to involve live sessions but they've recorded lectures that we give as well as automatically graded tests and some peer-graded assignments. So yes, somebody can take a class on Infonomics actually, two classes: Infonomics I and II.
There's also a class that I created, An Executive Introduction to Analytics. It's kind of a data literacy-type course for executives. It's a four-week course. And then we have a capstone course on data monetization where the students actually go through a project to monetize a company’s data.
[00:37:12] Loris Marini: Sounds like I'm going to be one of your next students, Doug because this stuff is absolutely interesting. My background is mostly technical.
[00:37:19] Doug Laney: I think to date there've been about 20,000 students worldwide who have taken these courses.
[00:37:24] Loris Marini: That's really amazing.
[00:37:26] Doug Laney: One of them recognized me on the golf course last week.
[00:37:29] Loris Marini: Yes, I saw your post on LinkedIn. Speaking of golf, you say. It's not really competitive, but you play golf quite a lot. You must be good.
[00:37:42] Doug Laney: We're trying to get out. It was a good way to socially distance last summer. I enjoy it.
[00:37:53] Loris Marini: What’s your vision for humanity, in general, when it comes to information?
[00:38:03] Doug Laney: Humanity. Well, I'm going to stick to what I know and that is, I see opportunities for self-organizing data. We spend a lot of time trying to move data around: organizing, integrating, cleaning, securing, tagging, classifying, and defining metadata for it. It’s a major effort, especially as the variety of data grows. Systems that will automate that to some degree are on the horizon.
For humans, we've been enhancing ourselves physically for decades. Artificial limbs and joints, and so forth, glasses and contact lenses and LASIK. But we really haven't figured out how to augment ourselves intellectually other than at arm's length. We have the internet at arm's length, but we really haven't figured out a way to improve our memories or our processing capacity in any way. So, I think what the future holds is a way to enhance our own cognitive abilities in some integrated way.
[00:39:09] Loris Marini: Boom. There you go. Lots of really interesting ideas. Again, the book is Infonomics: How to Monetize, Manage, and Measure Information for a Competitive Advantage. If you haven't had a chance to read it, I cannot stress enough how transformative it is.
[00:39:27] Doug Laney: I heard it was available in a bookstore in Sydney, but I couldn't find it.
[00:39:33] Loris Marini: I just go with my Kindle straight away.
[00:39:35] Doug Laney: Yeah, it's available as an eBook, printed hardcover, and also an audiobook.
[00:39:40] Loris Marini: Yeah, it's interesting. One thing I didn't mention is that I got to know the founder of dataleaders.org, James Price, through your book. At some point, you mentioned James Price in Australia. I'm like, I need to check this guy out. And that's how I met the rest of the leaders of the organization, which has been a fantastic journey so far. So double thanks.
[00:40:03] Doug Laney: James is definitely a partner in crime on this topic. That article he wrote 10, 12 years ago on the barriers to managing information as an asset was ground-breaking. And that's how I found him in my research for the book, I came across him and his partner, Nina Evans, at the University of South Australia and ended up becoming friends with them both during my travels to Australia.
[00:40:29] Loris Marini: Wow, a fantastic story. And I'll make sure to add the links to the book and as well as the article by James Price and the Coursera course as well.
Doug, thank you very much for your time. We could go on and on for hours but I’m just being conscious of the time you have.
[00:40:47] Doug Laney: Maybe another time when the next book comes out.
[00:40:49] Loris Marini: Exactly. Definitely. It’ll be my pleasure to have you again as a guest on the show.
[00:40:53] Doug Laney: Yep. Thank you. Loris. Real pleasure.