Honored to be here today with Doug Laney of West Monroe. He’s the innovation fellow at West Monroe. Doug Laney is a noted, personality in the data analytics space. He’s the author of Infonomics, which I believe is the kind of the seminal must have, must know piece related to treating data as as a real asset. We give a lot of lip service to that in the data and analytics world.

Doug is also the author of a new book called Data Juice. We’re gonna talk about both of those books today.

Like myself, Doug is also a former Gartner analyst. We may get in a little bit of a of a discussion of our, shared experiences at at Gartner.

But his list of accomplishments is long. Also, a professor at his his alma mater, University of Illinois, Champaign.

Look really forward to diving into these these topics with Doug today.

As far as I’m concerned, in my individual opinion, if you are a CDO and you haven’t read Infonomics by Doug Laney, you need to put it on your must read list. As a matter of fact, the Wall Street Journal has recommended that it’s on your must read list if you’re a CIO at the very least for this, for this summer. So welcome, Doug.

Thanks, Malcolm. Great to great to be with you. Personality, I guess I need some paparazzi now or something.

Well, yeah. Arm arm guards and and everything else. But but but, yeah, I mean, you you are extremely well known in this space, and rightfully so. I can recall vividly being at, a Gartner data and analytics summits in two thousand seventeen, eighteen, where you were speaking, and I had a lot of moments listening to you speak. I think a lot of your receipts with Gartner led to the the publication of of of Infronomics, and it really your perspectives really helped change the way I look at data.

It it was it was foundational to me. It really helped me understand, why data is as important as it is and taking things beyond just lip service and actual into into, you know, implementation and and business value and business outcomes.

So so, Doug, can you take just a few minutes and and and describe the framework that you that you outlined in in in Infonomics and why a CDO should be reading Infonomics?

Yeah. Well, first, I appreciate the kind words.

I really do appreciate it. Thanks.

So, yeah, infonomics was definitely written for CDOs. In fact, I learned a a great trick while while I was at at Gartner. Somebody had one of our colleagues had written a a research publication on how to use personas, and apply personas to what what you’re doing. And I took that approach, and what I did was I created a number of personas who were kind of an amalgam of of, various people that I know know, mostly chief data officers or senior data and analytics leaders.

And I created these personas, and I I printed, you know, photo of the of an individual and and their their background and interests and what moves them and so forth. And I, and I print put these on my, on my wall while I wrote the book, because I think a lot of mistakes book authors make is they write for themselves or they write for, I don’t I don’t know who their English high school teacher. I don’t know. Who actually sent a copy of my book to.

She was very appreciative. Oh. Yeah. Yeah. I found her. She’d reached a long retired, but I found her and and sent her a copy of the book and, with a nice note, because she had really inspired me to raise my game when it came to to writing.

But anyway, so yeah, so I have these personas, various c ds, aspirational c ds, established c ds, and and so they were the ones I was writing to when I wrote wrote the the book as well. I did also intend for the book to be used as a textbook, in MBA classes and, and, the certainly the infonomics class that I teach at Illinois. They had asked me to teach a class while while I was a gardener. And and I said, Well, listen, I I wanna write this book first, and then we’ll use the book as a textbook if you like.

So I actually created a teaching guide alongside or instructor guide alongside the the book so that I could develop a core curriculum for it. But but, yeah, it’s definitely for c ds. Listen, chief data officers, the role is still kinda being, you know, defined, but, you know, we do know that it’s really about how to manage and leverage data as an as an actual asset. It’s really kind of at the core of what chief data officers need to be doing to to drive improved value from data.

But one of the things that I found was that most organizations don’t measure their data in any way. They may maybe they they measure some data quality characteristics, but, they don’t measure it as if it were an asset. So a lot of organizations will will talk about data as an asset, but not really treat it like one. And, again, you know, I I think, you know, there’s the old adage that you can’t manage what you don’t measure, and I think, you you know, there’s a problem there that if we’re not measuring the value of data, it’s cost component.

It’s, the benefits that it’s delivering the probable future economic benefits of a data asset. It’s market value even if we’re thinking about externalizing it. Then we’re really in a really poor position to justify and get the budgets and resources that we need to manage data as an actual asset. And then I think it follows that if, you know, for any asset that you’re not managing particularly well, whether it’s your physical assets or financial assets or human capital, you’re not gonna be able to deliver value or optimize the value that you’re delivering from those assets.

So that’s kind of how the whole kind of the three m’s of of of infonomics, the, measuring, managing, and monetizing data kind of all fit together. And so for most organizations, it’s kind of a vicious cycle of not measuring and therefore not managing and therefore not monetizing their data as optimally as they could. And so the idea behind, you know, infonomics is kind of goes with cub fans, you know, used to say in Chicago before before we won the world series, is, is to reverse the curse. Right.

There’s the old, you know, curse of the billy goat here in Chicago for the for the Cubs team. And so reverse the curse or flip the script, and so to get companies to start measuring the value of their data first, it’s cost components, it’s market value, it’s impact on revenue, it’s quality characteristics, it’s business relevance. Things like that will drive behaviors in the organization, not only for managing data better, but to kinda light a fire under the business to to do more with their their data.

So what would you say to a CDO, who said, well, okay. Monetize, manage, measure.

The managing part, you know, we seem to be reasonably good at. We we’ve invested in a lot of tool that we’re reasonably good at. Right? We we have some integration software, some NDM software, some data quality software. We spend a lot of money on that.

Some thoughts on that.

What’s that?

I I have some thoughts on that, but we’ll we’ll get there and finish it with that.

To hear your thoughts on that. I I I think the state of data data management is if I were to give it a five out of ten based on my three years at Gartner or a rating out of ten, I’d probably give it a five, maybe a solid six.

We we can certainly talk about that. There’s a lot more work to be done there. Yeah. But getting back to the notion of measuring, what I would hear all the time from CDOs and CIOs that I was talking to is, well, that sounds nice, Malcolm, but that’s really impossible.

Because the impacts here from a data perspective are indirect. Right? If I improve my quality of my customer data, there’s no direct tie to retention or incremental sales. What would you say to that person, Doug?

I would I would say that’s naive.

Yes, it’s difficult, but it’s it’s not impossible. And if we follow the same ways that valuation experts value other kinds of assets, we can apply those same kinds of techniques with some nuances to to data.

You know, a chief data officer at AIG, told me and I wrote about this in the book, told me how he was able to use the valuation models, that I published in the book to, connect the dots between data quality and business process performance. So, yeah, there are a lot of things that go into the performance of any business process, resulting in some ROI number. But, they’re able to to show how improvement in data accuracy or an improvement in data completeness actually drives improvements in business process performance. And we developed an entire, metrics framework for a very large. I can’t name the name, but the very large, retail company, that they can use to empirically track how improvements in various quality metrics and data governance, indicators drive improvements in business process performance leading to revenue, improvements, profit improvements, market share, risk reduction, etcetera.

Yeah. One of my favorite models at, at Gartner, which I know was there when you were there, is the is the value pyramid.

Right? Starting at the top of the pyramid and working your way down into individual fields of data. But still, there is this mysterious, and for many, a challenging jump between the quality metric.

Mhmm.

In this case, could be quality. It doesn’t have to be quality, but the quality metric and the actual business outcome. And one of the ones that that I was favorite of that that I like to use example I like to use is is around, like, cross sell or upsell.

Mhmm. Because everybody has metrics around qualified leads. Right? And if I delivered you one more qualified lead, what would that equal to from a sales perspective?

That that one was often one that worked really well with me. Yeah. When you’re talking about the state of data management and you said you have some opinions, what what what are those opinions? I’d I’d I’d love to hear more.

I I I think the state of management is is is okay, but could be a lot better. What do you think?

It it could be a lot better. I I just think, the the we data professionals and I I’m as much to blame as anyone, you know, over my thirty plus year career.

We’ve done just a crap job of of applying asset management principles and practices to data. So asset management practices have been around for centuries. Right? And, and and we just have not really applied them to to data.

We haven’t talked about, things like like data condition, supply chains, you know, pick pick any, you know, manage asset management kind of approach, and we just we just have not done a a good job of applying the principles and practices. So one of the things I did and I wrote about in Infonomics is I looked at a variety of different kinds of asset management, practices like, like a material asset management, financial asset management, human capital management, IP asset management, library science, records management, and there’s just a lot that we can borrow should be borrowing from those disciplines that we’re just not.

And so one of the things that I did was aggregate those into a set of, what I call generally, accepted information principles, on how to how to manage data, the kinda eight eighteen principles on how to manage data by applying best practices in asset management.

So I think maybe that’s more strategic, but when it comes to, you know, technology, yeah, the technology is always improving improving for integrating data, for handling master data management, metadata management, data cleansing, data quality. One of the things that we we’re not doing very well, though, is is dealing with the, the increase in the variety of data. So, you know, I talk about the three m’s of of infonomics. I’m also the the I don’t know if you know the guy who came up with the three v’s of big data.

So the volume, velocity, and variety. And so, yes, we’ve been able to scale infrastructure and architecture and technology to handle increasing volume and, velocity of data, but not so much variety. We haven’t really figured out a way to automate kind of the integration of disparate data sources to align them semantically and, and, you know, integrate them physically or in real time very, very well. Yes.

There are data integration products out there, but to make them work, it’s still very much a manual effort of mapping data elements and trying to rationalize their the differences in their in their, in their semantics. So I think there’s a big opportunity for vendors, some vendor out there to figure out how to really automate the integration of of data much much better of disparate data sources.

I I think there’s a lot of promise out there Mhmm. Around the notion of, I will loosely say, a data fabric where you could use metadata to help inform or augment or even potentially automate integration patterns that that where you could use things like feedback from whether transaction was successful or not successful as a means to understand should there be an integration pattern where there isn’t one today? An example would be between a piece of RevRec software potentially and a data management software. I think that’s kinda where you’re going. Yeah?

Yeah. Yeah. I mean, not just the software, but the data itself. Right. So, you know, you’ve got a lot of customer data around the organization. Any company I go into has twelve different definitions of what a customer is from different, databases and applications.

I just talked to a a client yesterday. So we have multiple instances of Salesforce, and each one defines a customer differently. Right? And so how do we integrate that that data into getting a complete picture of, of a customer? And so, you know, that that’s very much a still manual architecture coding model modeling and coding kind of effort to to do that.

Yeah. Well, that that what you described is a classic MDM problem, but I think what we’re seeing now is that, you know, context matters. Cost context always mattered, but I think it’s gonna matter more because it will help whittle down the volume of data if you understand the context. Meaning, customer in the context of a marketing interaction versus a compliance or a legal interaction.

Right.

So so I I think when you you couple all those things together plus the kind of the semantic consistency that you were talking about, yeah, I I think we’re on to something.

But getting back to the notion of of of kind of asset valuation practices, what what what are, you know, kind of tangible actions that a CDO could take after watching this video? Would that would that be like sitting down with the CFO to to understand how to build some of these models? Or what what what are one of the one or two things to to try to take more of the kind of the asset valuation approach here?

Absolutely. We’re doing that with a manufacturer right now, and we just we just at West Monroe did it with a, with a couple pharmaceutical companies to help them value their data for different purposes.

Yep.

And then depending on why you want to value the data, whether you’re trying to get investments, whether you’re trying to, prove or justify the benefits of an analytic use case or just, value the enterprise data perhaps for an m and a, you know, type transaction.

There there are variety of reasons why you would want to to to value data.

And then therefore, you know, different methods. Kind of the standard methods of valuing any asset are the cost approach, the market approach, and the income approach. The cost approach, what did that asset cost to generate or or create or or purchase?

The market approach is what is the, value of that asset on an open, you know, arms length marketplace.

There’s some nuances with data again that when we sell data, we’re actually licensing it. Usually, we’re not transferring ownership of it. So and because data is a non rival risk, non depleting asset, we can license it over and over again. And so the market value, method requires a bit of optimism price price optimization.

And then the income approach is what is this asset’s contribution to income streams? And and increasingly, we’re helping clients not only look at current income streams, but if we remember account or accounting, a accountants will value an asset based on its probable future economic benefits. So those probable future economic benefits for an asset may include, use cases which we’re conceiving of or are in planning or some stage of of implementation.

So, we wanna kinda look at what are the revenue streams or value streams that are going to be generated by those, the the cash flows and so forth over over a over a time horizon.

So, yeah, it can be pretty sophisticated, but this is all kind of standard stuff that accountants have been doing for years. So Right. Yes, Malcolm. Just as you said, we should be engaging the the CFOs or the the financial organization, which is exactly, you know, what we’ve done pretty much in every every valuation initiative that that we’ve worked on at at West Monroe for clients. We’ve involved someone from from the the client’s financial team. At at least to kinda bless the methods that we’re we’re using.

Right. Right. So I love I love this notion of generally accepted information principles.

You know, when when I when I hear g a and then p, I I usually think of GAAP accounting principles.

Right. So I borrowed from that. Right? Yeah.

Yeah. It’s a riff. Yeah. I get it. And and for a long time, I think I was kind of doing one of these kind of fist shaking at at at at the accountants of the world and and the the kind of the gap principles because I’ve always thought that the data should be considered a balance sheet item. But one of the things that I found interesting in Infonomics was that you actually say that it may be a good thing that data is not considered, a balance sheet asset.

Tell us more about why why potentially could be a good thing.

I don’t have a horse in the race, so I I really don’t care whether data’s a balance sheet asset or not.

I I think it would help some organizations and it would hurt others.

The the the the negative aspect of of of reporting on the value of your data as well. Like, imagine you’re a CFO or a CEO. Now, you know, raise your hand if you wanna report on more publicly than you already do. Right? Probably not. Right? There’s certain things that you like to keep, proprietary, and out of the prying eyes of, you know, investors and competitors.

So how what data you have and how you’re deploying it and how you’re valuing it, is a closely held secret for many organizations, particularly, digital organizations and and, those that are are, relying heavily on on data or or data derivatives to drive business value. So, yeah, that’s that’s one reason. The other, the the flip side is, you companies that could report, if you could report on the value of your data, it might augment the the valuation of your company because, data and digital service companies get valued very differently than your traditional, you know, companies with with traditional, physical or financial products or or services.

And so if you could value your data as part of that business, then your overall valuation might might get a a pretty good bump. In fact, we’re working with some companies to take the data part of their biz the data in their business, Excuse me, and move it into a separate, entity, a separate corporate entity, and then that entity gets valued at some premium because it’s a database, part of the business. And then the parent company can include the value of that subsidiary on their balance sheet and get a get a bump from from that. So, it’s, it’s something we’re working on. It’s called assetizing the data because data is not a balance sheet asset according to accounting standards, for most cases. But what you can do is move the data part of your business into another company and then value that and then include that on your balance sheet. So it’s a bit of accounting shenanigans, but it’s entirely, legit.

Well, that I I find that fascinating. Right? Because on on day one, if I’m a CDO, how can I make a massive, massive impact here?

Maybe that massive impact is talking about, do we need a separate business here that more aligns to Mhmm.

A data driven valuation than a traditional valuation.

I think Right.

Yeah.

The classic example that we heard in the news recently, was the whole American Airlines thing in getting a loan, or at least a line of credit, I believe, against their, loyalty database that put their valuation two or three x their airline.

Mhmm. Yeah. I published a article about that in in Forbes a couple summers ago. Yeah.

So the airlines, in order to stay aloft, pun intended, you know, at the beginning of the pandemic, needed to take out loans, and so they, the loans, they got against their customer loyal loyalty programs, basically comprised of just data. Right? We’re we’re a a two or three x multiple, both United and American. And so now there are some airlines that have and are looking into lopping off part of the data business.

Their loyalty programs into separate corporate entities to get that premium valuation.

So Fantastic.

So the last m here, monetization. We’ve been kind of flirting with this. And and one of the posts that that you had on LinkedIn recently that I found really thought provoking, I I’m not sure you intended it that way, but it was to me, was was that one of your clients was laying on a beach in Puerto Rico reading Infonomics, and and you had made a point about how that client was working to I’m I’m gonna put words in your mouth probably here. Collateralize. That’s what the word I will use. Collateralize, potentially leverage, their data, but in you flirted at the idea of potentially alternative, value drivers here.

I kind of went to the crypto space when I heard Puerto Rico. When I don’t know why. I just put two and two together. It’s like, oh, Puerto Rico, crypto alternative data. But Yeah. I’d love your thought on, you know, the whole crypto world and the notion potentially of tokenizing data or using digital assets as a way to kind of work around, for lack of a better word, kind of traditional ways of leveraging data or or or or or, you know, feedback debt.

What do you have any thoughts here?

At the core crypto assets are data. Right?

Yeah.

But then we can also use the underlying technology, the blockchain technology to track and certify, the the provenance and lineage of data assets.

So I think that technology will have some initial impact in the data management space in terms of metadata management, in terms of data licensing as well. So data brokers and so forth will be able to track who has their data and how it’s being used and so forth. Because right now, data brokers, they’ll publish a data set to you, and they have no idea what you’re doing with it. You could be repackaged. You could be reselling it for all they know.

So, you know, unless it’s customer data and they insert seeds in it, which are are fake names and addresses that will basically watermarks.

Yeah.

Yeah. They’ll basically phone home if if the the data set’s being used improperly. I mean, that’s been done in mailing lists for for decades. But there really are are no, you know, digital ways to to to do this, with other kinds of data assets. And so, you know, blockchain might be a way to to to handle that.

Yep.

Tying off on infinomics, one of the things that I liked the most about the book is that you have a healthy smattering of real world use cases.

Right? That that I assume that that, you know, through through years at Gartner and other interactions with with with companies, you were actually able to say, okay. CDO at company x was able to do y using z practices. I I love that. It’s it’s it’s throughout the book. Is that really kind of the genesis for Data Juice?

And and maybe you just tell us a little bit about date Data Juice and and and and and and and why it should be on my radar.

Yeah. You know, my editor at Gartner, advised that, you know, I include a lot of stories to make my my cases. And so, you know, I researched these stories. And and then also, you know, when I first, returned to Gartner, so I returned to Gartner, a decade or more ago, for my my my second time, they said, listen, you’re the big data guy, research big data and and write about it and advise clients on big data.

Well, the the questions quickly turned from, you know, what is big data to how do we do big data to what do we do with all this big data? And I was like, I I I can give you some generic answers, but, you know, let me go collect some stories. So a dozen stories turned into fifty stories, turned into nearly five hundred stories. I’ve now got about seven hundred stories that I’ve compiled over the years.

And so, yes, I included a bunch of stories, maybe forty or fifty in in data in in infonomics to kinda make the points. But people were asking for more. Like, we we wanna be inspired. Tell us stories in the manufacturing industry or where companies are doing predictive analytics, and we just need stories in order to kinda light a fire under our board of directors or our business, business people. And so, finally, I I compiled them all into this into the new book that was just published, earlier this month called, Data Juice about it’s a hundred and one stories about how organizations can squeeze value from their, actually, hundred one stories of how organizations are actually squeezing value from their data.

And then each story includes, most stories have a have a, you know, why were they doing this and and kind of what was involved, and then, an actual outcome. So you you talk about outcomes earlier, Malcolm. So outcomes are, you know, very important. And I guess there are a lot of vendors out there who tell stories like, you know, by using our technology, we increase the number of users by two hundred x, or the speed of queries was a thousand percent faster.

Like, well, that’s, you know, interesting, but how did that actually deliver business benefits? And so these stories are all business benefits oriented. Most of them are financial benefits. Some are social benefits or or others.

But met they’re pretty much all measurable benefits.

And some of them are just purely, you know, really innovative and inspirational ways to use data. But then each story I started kind of writing a, an analysis of each story. Like, what would I what did I see? What did jump out at me? What was, what would I recommend for this company or organization to do going forward to take this idea further?

And and they the the all the recommendations started to sound kinda homogeneous.

So, I reached out to, nearly a hundred friends in the industry and and said, listen. I want you to write, you know, an analysis. So I basically I kinda crowdsource the entire book, which is good because my wife told me if I ever wrote another book, it would have to be titled how I used, big data to find my next wife.

So so I was like, Honey, how about how about if I crowdsource this book and, like, have compile these stories, and then, and then have, other people kinda write the analyses. And so I got got the green light for that. But yeah, the book’s been really well received. Thank you, and, great accolades from business leaders and data leaders and technology leaders and executives, you know, around around the world.

So, yeah, I, I’d say it’s meant to be, you know, and it’s not. Infonomics is more of a kind of a a how to book, and and providing really some some best practices and a ways to apply asset management and measurement practices to data, and and more a book that you might sit down and read. I think data juice, although people tell me they love flipping from one story to the next, it’s really meant to be more of a reference book. So each story is tagged by the type of industry, the type of data, the, type of analytics that was used, geography.

And so you can actually look at the index and thumb to whatever stories, you know, might be most meaningful. But at the end, it’s the book is meant to be, it’s, you know, it’s meant to inspire organizations to do more with their data, or or maybe shame them, you know, into doing more with their data as well.

I I get the shame angle. That can be a a very, very powerful motivator.

It is powerful. Yeah.

When when when I was tasked to implement an MDM, one of the first things we did was was was build a report that showed how bad the data really was.

Yeah. Yeah. Yeah.

And that was heavily that was that was heavily motivating. But but all else being equal, of course, you know, you the best disinfectant.

Right?

Right. Yeah. All those being equal, of course, if you if you if you can find carrot and, and and not so much stick, it’s probably a little bit better. But before before we tie up here, I would love to to understand your perspective of of the future and where where things are going, what you see some of the to the degree that we are prognosticators at all and that we can see the future at all. I mean, I I I see a few kind of normal things evolving, but would would love your perspective on, you know, if if I’m a CDO and I’m building a a longer term road map, what are the things that I should be thinking about?

I think you’ve gotta have your eye on blockchain. You’ve gotta have your eye on quantum. You know, I don’t know if, either of those, but maybe blockchain, but not quantum requires some some planning right now. But, but, keep it in kind of the background of the strategy, I would say. Quantum’s gonna radically change, analytic capabilities and and privacy and security. So definitely have an eye on that.

Organizationally, I’m starting to see more and more as I’ve been recommending for years, the the bifurcation of the IT organization into separate I and T organizations. I I think, the day when, information and technology were tightly coupled, you know, our our were decades ago, and there’s no reason that data technology need to be even discussed in the same, you know, breath anymore, let alone manage, simultaneously.

And so unfortunately, as I I wrote about and I’ve said, and others have said this as well, a lot of CIOs are really chief infrastructure officers and haven’t really been focused on managing data as an actual asset. So maybe it makes sense to to separate IT into separate I and T organizations.

In some cases, where where some clients have kind of taken my my my advice on on that.

They’ve even dispensed with the, CIO role altogether, you know, and they now have a chief technology officer and a chief data officer, and are managing those assets separately. Of course, they’re very interrelated and of course they’re interrelated with other business assets, but from a management, and strategy standpoint, they really can be, largely, Man managed separately.

Yeah. So that that’s, I think, another trend. Again, I would love to see some kind of, some vendor, some technology being able to automatically integrate data sources based on understanding their cement semantic consistencies and inconsistencies, or automatically fill in gaps in data quality, correctness, completeness, etcetera, by using external data sources. I think external data sources are really the the next horizon of of company data.

Most organizations really just spend a lot of time kinda staring at their own navel when it comes to, you know, analyzing the the data that they have, and really don’t have a a very good awareness of the full range of external data sources available from, you know, millions of data sets published by government organizations and NGOs worldwide from thousands of companies selling data, data brokers, and data aggregators, and data marketplaces, to hundreds of partners that you can exchange data with and return not necessarily for cash, but maybe for goods and services, and and social media data, and, billions of websites that can have content that can be harvested. So, you know, I think, unfortunately, many many organizations have, you know, entire department dedicated to procuring office supplies, but, you know, no one dedicated to procuring data supplies.

Some research that I did recently kinda bore that out, and it’s really, you know, quite quite unfortunate, and and I think a big mistake. So I think the future will will involve better integration of external data assets. And and even from a data management standpoint, I’m expecting, extended, ecosystems to start to emerge where trading partners will co manage data, Rather than having be my data and your data, we’re managing data in the common environment in the cloud, together. And we agree on rules of the road and governance and access and, and and share the costs of that. So I think, we’ll we’ll start to see that as well. I think the cloud certainly enables that. So, those are just a few ideas.

I love the last one. Yeah. Right. Well, I love it all.

You know, have having done multiple tours of duty at a third party data provider done in Bradstreet. Obviously, I’ve kind of got that pounded into my, DNA about the value of of third party data, external data. But all the things you touched on could couldn’t agree more.

The last one, it really intriguing to me, this notion of kind of shared governance.

I think you could you could loosely use the phrase of consortium there.

You could. You could. Yeah. Consortium has some some negative connotations as well Yeah. Because they tend to not work very well.

But, yeah, I’m I’m talking about a really shared management and, data management environment. I call it an extended, information ecosystem or extended data ecosystem. Yep.

So yeah. And I think there are companies maybe like Walmart that sorta do this for some of their suppliers. Well yeah. And Amazon, right, handles data for a lot of, you know, product providers.

But yeah.

Yep. It it’s it’s to me, the the killer use case there is supply chain. Right? Com companies need more visibility on the supply chain, and that shared management approach, I think, is gonna be key to that.

I also happen to think that that blockchain can play a role here in this notion of of DAOs, decentralized autonomous organizations, where those DAOs exist purely as governance entities to maintain the semantic consistency, to maintain the definitions, to maintain kind of the common area where data is shared across organizations. I think this is really interesting. Totally agree with you.

Good. Listen, Doug. Thank you so much for taking the time to talk to our listeners today. It’s been a pleasure having you on CDO Matters.

Look forward to continue to interact with you. I just joined the the Forbes Technology Council. So, you you’ll be seeing me and, more in Forbes. But, really, really appreciate Yeah.

You you you coming on. I I couldn’t couldn’t recommend more Infonomics. If you haven’t already read it Thank you. Please, I couldn’t recommend it more.

And Data Juice, I’m excited to read. So, Jagrani, thank you so much for your time.

Thanks. Thank you, Malcolm. And and, to return the favor, for any of your CDO listeners, if they’d like to reach out to me for a free copy of of Infonomics and or data juice, inscribed copies maybe for them or for their their ELT, their executive leadership team.

Oh, wow.

Reach out to me, directly. That That’s fantastic.

I mean, it’s it’s eighteen bucks on Kindle, twenty four bucks hard copy. So there’s some real value. That’s wonderful.

Thank you so much. Reach out to me, at, d laney at west monroe dot com or or find me on LinkedIn.

Awesome. Thank you so much.

My pleasure. Thanks, Malcolm.