Customer Equity Accelerator Podcast

Ep. 81 | Customer Data as an Asset with Doug Laney

"It’s imperative for companies to treat data as an asset – and it meets the definition of an asset – but accountants don’t currently allow it." - Doug Laney

 

This week Doug Laney, former Gartner analyst, author of Infonomics and principal of data & analytics strategy at Caserta joins Allison Hartsoe in the Accelerator. When 9/11 happened it kicked off an unexpected crises as companies who lost all their data discovered that insurance firms believed it had no value. Although data meets the definition of an asset on many levels, traditional accounting and insurance firms do not see it this way. What can a corporation do to recognize and protect this truly valuable asset? Doug Laney has the answers.   

Please help us spread the word about building your business’ customer equity through effective customer analytics. Rate and review the podcast on Apple Podcast, Stitcher, Google Play, Alexa’s TuneIn, iHeartRadio or Spotify. And do tell us what you think by writing Allison at info@ambitiondata.com or ambitiondata.com. Thanks for listening! Tell a friend! See the full transcriptView all episodes.

 

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Ep. 82 | Quantifying CX with Michael Allenson Ep. 80 | Customer Trust Through Privacy

Show Transcript

Allison Hartsoe: 00:01 This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue generating customers, then this is the show for you. I'm your host, Allison Hartsoe, CEO of Ambition Data. Each week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let's go!

Allison Hartsoe: 00:33 Welcome everyone. Today's show is about customer data as an asset and to help me discuss this topic is Doug Laney. Doug is the author of infonomics and also the principle of data and analytics strategy at Caserta, which is a premier data analytics boutique consultancy that specializes in data engineering, architecture, and strategy. Doug, welcome to the show.

Doug Laney: 00:56 Thanks Allison. Great to be with you.

Allison Hartsoe: 00:57 This is an unusual background to be able to talk about customer data as an asset. So can you tell us a little bit more about how you got into the subject and why this topic is of interest to you?

Doug Laney: 01:08 Well, the background on the concept of Infonomics and thinking about information as an asset as an interesting and unfortunate beginning, it actually came to my attention after the nine 11 terror attacks. Yeah. That organizations had actually lost their data. Remember this was in the days before a lot of cloud and offsite backups and so companies kept onsite backups, which of course got destroyed along with the twin towers. And with addition to the tragic loss of life, companies lamented the loss of their data and had to deal with that. So they lost their customer lists, their customer transaction data, and it became quite an existential event for them. And what naturally what these companies did was they submitted claims to their insurers for the value of the data they lost. And the insurers wrongly denied those claims suggesting that data wasn't considered property and therefore wasn't covered by their property and casualty policies.

Doug Laney: 01:58 This kind of rose the hair on my back a little bit and I thought, well goodness, isn't information and asset? I mean, isn't it property? It has the characteristics of an asset. It's something that is owned or controlled. It's exchangeable for cash or generates probable future economic benefits. Those are the definition of an asset. How can the industry, the accounting industry, or the insurance industry not recognize information as an asset? So there are a number of court cases that ensued, and the courts are thoroughly confused on the matter and some courts have found that, well, since information can be represented by you say bubbles on an optical disk, or it can be printed or it can be housed on a hard drive, that it should have sufficient physical manifestation to be considered an asset. Other courts have issued really ridiculous rulings like well since electrons have negligible mass.

Allison Hartsoe: 02:41 Oh, kidding?

Doug Laney: 02:42 Yes. That information shouldn't be considered an asset. So we're in this crazy world where information meets the criteria of an asset, but the accounting profession doesn't recognize it as an asset. In fact, the insurance industry double down on its antiquated notions that information isn't property by updating the commercial general liability policy standard template that's used by insurance companies to explicitly exclude electronic data from PNC policies. And then the accounting industry said, well, listen, if the insurance industry isn't going to recognize information as property, then certainly we're not going to recognize it as an asset. And they updated a key financial standard to prohibit companies from capitalizing information on the balance sheets. So that's the kind of crazy world that we live in right now. Yup. And we're in the midst of the information age, and companies are trying to do more with their data, become data-driven, and actually manage and monetize their information as an actual asset. But like I said, the keepers of the definition of what constitutes an asset or constitutes the property, I've gone kind of retrograde.

Allison Hartsoe: 03:38 Can you go back to what constitutes an asset? Because I think their definition oftentimes spins on the idea of it has to be physical to be an asset. And you mentioned it goes all the way down to the electrons and which just seems like on the verge of absurdity. So are they pinning everything about information to the idea it has to be physical?

Doug Laney: 03:59 Well, there are different kinds of assets. The're, of course, there are physical assets. The're financial assets. There are intangible assets like copyrights and brand and trademark and things like that, so information just clearly meets the criteria of an asset. As I mentioned, an asset is something that has three criteria according to the accounting profession. An asset is something that is owned or controlled. Okay. If you can demonstrate exclusive control over it, then that qualifies. It has to be something that is exchangeable for cash, and it's pretty clear that the companies can sell their data for cash or other considerations and it generates probable future economic value, and it's pretty clear that the data of any variety customer data and especially is used by companies to generate economic value. Yet again, the accountants have not yet agreed to allow information to be represented or reported on the balance sheet.

Allison Hartsoe: 04:43 Oh, when you say information, are you really talking about data and maybe specifically customer data or is it all in.

Doug Laney: 04:48 yeah, any kind of data. Okay, so unstructured data, emails, transaction data, customer data, process data. None of that can be represented as actual property. Even companies like Facebook and TripAdvisor and others that all they do is traffic and information. The value of that information is nowhere to be found on their balance sheet.

Allison Hartsoe: 05:06 Wow. That's almost mind-boggling, right? Because here are these companies have this incredible valuation, right. So with Facebook and all these great info companies having so much value and with this problem on the other side where property isn't really recognized as an asset, should companies care?

Doug Laney: 05:22 Well yeah, they should definitely care because it clearly meets the criteria of an asset. So kind of you'll forget what the accountants say, and I think it's incumbent, it's an imperative today for organizations to manage their information as an actual asset to treat it as an actual asset. The components of infonomics are really the three main aspects to it. One is to measure your information assets. The second is to manage them, and the third is to monetize them. We've all heard the adage that you can't manage what you don't measure well because organizations don't measure their information, they don't value it. They barely measure its data quality. Most companies don't even have an inventory what they have. So you can't manage what you don't measure. And because information is not something companies measure, they're in a poor position to manage it well, and I think it follows that if for assets that you're not managing well, you're in a poor position to monetize them or to generate economic benefits from them. And so for many companies, this is quite a vicious cycle of failing to measure their information. Therefore they're not managing it well enough. Therefore they're unable to generate economic benefits from it. The idea behind infonomics is to turn this into more of a virtuous cycle of vicious one.

Allison Hartsoe: 06:25 I see. Are there examples where companies have been doing this right, or maybe not within the US, maybe in other places? Is anybody doing this?

Doug Laney: 06:34 Yeah. There were several examples of companies that have touched on some of these concepts. I can't really speak to one that is doing all three of these facets in, in a truly integrated fashion. But there are some great examples that I can speak about publicly. One is Boeing. Boeing came to us and said, listen, we have so much data. You can imagine how much data they have on their customers, their flights, and their airplanes, their processes that we can't manage all of this data equally. We need to be disciplined about prioritizing what we're managing and how we're managing it and what we're focusing on, and we need to come up with ways to budget for information management based on the potential economic value of the data. So they used some of the data valuation models that I've published in my book and research to better understand which data is the highest priority for them based on it's what data has management issues or data quality issues, but has a high business relevancy, meaning it's highly relevant across a range of business processes.

Doug Laney: 07:27 And so they were able to prioritize that way. Other companies, like the manufacturer of vivid, realized that they had a lot of data that was underutilized, vivid makes security systems, and they realized that they had a lot of data that was going underutilized. And so they measured this using the models that I've published to understand where data was providing a low economic value versus having a high potential value. And they said that's the data we need to be innovating around. And so they came up with 20 or so different ways to better innovate around this data by, for example, using customer support data better in the manufacturing process or a sales data better in the marketing process and marketing data better in the customer support process and so forth. And once they executed on all of these initiatives, they realized that they had added $300 million of market value. Wow. On a $2 billion business. Wow. Yep. So there are plenty of other examples like that.

Allison Hartsoe: 08:20 So this is just fascinating. So let's talk a little bit about the models, because oftentimes when we look at data, we're looking at a very specific customer lens. But I've often wondered there are operational efficiencies, there's what I call the grease in the wheels around brand. There are other things that make data valuable. So maybe could you talk a little bit about how you model this raw set of data and into understand your value it has.

Doug Laney: 08:45 Yeah, because data is not a balance sheet asset. There are some companies that really can't like cross that chasm to thinking about financially valuing their data. So we also came up with a set of foundational valuation models one that measures information is what we call it intrinsic value. So the intrinsic value of information looks at how correct how complete and how scarce the data is. Because data that has higher quality characteristics and is more proprietary to your own organization, like specific data that you're capturing about a customer that nobody else has, that data has a higher potential value than other kinds of data. So the second model we have is called the business value of information. It also considers key quality characteristics, but also the relevancy of data sets across a range of business processes to get a full picture of how important that data is actually or potentially to the organization. And then the third model is the performance value of information. Where we measure how does having or not having data affect key performance indicators in the organization, like the ability to sell faster or deliver products faster.

Allison Hartsoe: 09:43 Oh, this is fascinating.

Doug Laney: 09:45 Right? And then now on the financial side, for those that really do want to measure information as a, in economic terms, all we've done is really borrow from the way that valuation experts and accountants value any kind of asset using the cost approach, the market approach, and the income approach. We've adapted the models a little bit to accommodate some of the interesting nuances of information that we haven't yet discussed.

Allison Hartsoe: 10:04 So let me circle back to these three models. The foundational valuation model, the business value of information, and the performance value of information, right? The foundational ones seems pretty obvious like, okay, I have data, I need to understand that it has value. Is it correct? Is it complete, is it scarce? That makes just intuitive sense, but when we look at the business value of information, the piece that strikes me there is with the presence of let's say AI, I've got all these different workflows that are rapidly changing and dynamically moving. Maybe the business value of information, how good it is, how relevant it is for business processes is more, it's almost like a cash flow statement. It's constantly maneuvering, constantly changing in terms of how important it is to the business. Would that be correct?

Doug Laney: 10:48 Yeah, no doubt. And data is very contextual, so you can use it in a variety of ways. We haven't talked about this yet, but data has these really unique characteristics. There are a lot of people who talk about data as the new oil, and while that certainly represents that the recognized data has value and importance, it misses the point that data has these unique characteristics like that. And you can only consume a drop of oil once and then it's gone, right? You can burn that all at once and then it's gone. Data you can use over and over again, and you can use it simultaneously for multiple purposes. It's what economists would call a non-rivalrous non-depleting asset. And so I think it's important to be able to consider the variety of ways to use information simultaneously for different business purposes and to use that same data again and again to drive business value. So it's kind of a horizontal and vertical look at using information. And so that makes it very complex as you suggest to consider all of the variety of ways to leverage an information asset. And this is one of the things that I help organizations with is their data monetization strategy. It's an approach that we have for exploring the variety of ways to use data to generate business value inside and outside the organization.

Allison Hartsoe: 11:52 This is really a neat area because I would imagine that the value of data increases the tighter, that ability to use it, again and again, is streamlined or is tightened across the organization. So let's say for example, in marketing I've got a definition of channel that is different in sales and different in marketing and different across the board. I tighten up those definitions, and now the asset flows a little better. Would that be reflected in the model?

Doug Laney: 12:18 Yeah. And in fact, defining information in value terms in quantified value terms helps to solidify some of those definitional inconsistency across the organization. If you can put things in financial terms in money, money talks, right? So if you can put the value of your data in monetary terms, it's a common way to discuss and think about information throughout the organization, both its actual and its potential value.

Allison Hartsoe: 12:41 Okay. So I know there's a lot of people out there who get into data and love the idea of governance, but governance is probably one of the least sexy areas of data. Could this be a way to make governance more sexy by tying it to the bottom line?

Doug Laney: 12:54 Damn. I thought you wanted to talk about data science, which is considered the most sexy job in the 21st century.

Allison Hartsoe: 12:59 It's governance.

Doug Laney: 13:00 Yeah. Data governance is very important, especially with customer data. With all the regulations out there now, companies really need to be circumspect and buttoned-up when it comes to understanding all of the regulatory issues, whether they are national issues, international compliance issues like GDPR or industry-specific issues like there's a layer cake of compliance considerations that need to be included in any kind of data governance program. My thing about data governance is that the mistake that many companies make is they start with policies, right? And all these compliance regulations kind of compel you to do so. But I think it's really important to think about the principles. What is the vision for data in the organization and what are the principles for the way that you're going to collect and use and manage and store and share information and to develop those principles in concert with not only your chief privacy officer and your chief information security officer but also the business because it is a bit of a push and pull and once you have those principles established, then you're in a position to establish guidelines and then policies from that. But starting with policies is really kind of a recipe for failure from what I've seen.

Allison Hartsoe: 14:02 Yeah. I guess that makes sense because in many cases, when we talk about making a change within an organization, it really goes back to the use cases and tightening everything around. How does the business operate, and in this case you're not just talking about how the business operates, but you're talking about how does business operate within the management of risk around privacy or data protection.

Doug Laney: 14:23 Yeah. Where things get really challenging too is that there are these information ecosystems emerging. It's no longer four walls around our data. We're sharing data, we're collaborating on the same data sets with our partners, with our suppliers and sometimes with our customers. And so governance needs to expand beyond the four walls of our organization to incorporate the larger ecosystem in which we preach. We participate.

Allison Hartsoe: 14:43 Well now there is a great solution because governance, it has already been peg as the least sexy title out there, but maybe the information ecosystem manager is the sexy new title of the future.

Doug Laney: 14:55 Yeah, I love it. In fact, there's a chapter in my book about applying classic ecosystem concepts to information, so I encourage your listeners to check that out.

Allison Hartsoe: 15:02 Oh, excellent. And I do want to call out again, the book is called Infonomics and where can they find it? Is it available on Amazon? We could certainly include a link.

Doug Laney: 15:11 Yeah. Infonomics is available on Amazon and other booksellers. It's available in audio and hardcover and in electronic format. We're really excited that it was selected by CIO magazine as the must-read book of the year and received some other nice accolades.

Allison Hartsoe: 15:25 Fantastic. I love it. Well, let's talk a little bit about companies that are maybe not doing it right, or we talked a little bit about some classic mistakes. Are there places where, and you don't necessarily have to call anybody on the corporate, but I'm very interested in where the train wrecks are.

Doug Laney: 15:40 Well, here's an interesting scenario that I've seen kind of, again and again, I'll just highlight a particular example. So one of the things I do is I help organizations develop and review and refine their data strategies. A lot of companies will come up with a data strategy, which encompasses data governance and data management and data usage and all that. And so I review these data strategy documents for organizations and I'll review hundreds of them a year. And so I'm meeting with the client, a utility company, and they show me their data strategy document, and it starts out like many of them do by articulating that data is our most important asset or it's our most critical asset, or we want to manage data as an asset. And then I go through the document, and I see there's nothing in there about maintaining an inventory of other information assets.

Doug Laney: 16:21 Now imagine a retailer with no inventory, what's on their store shelves or a CFO with no chart of accounts or an HR manager with no employee directory. Those would be dismissible offenses. Yet here we are, we're CIOs, and chief data officers don't have a complete inventory of their information assets. So anyway, I mentioned this to the client, and they say, yeah, well, you know, the company, we really only inventory our major assets, like our trucks and our transformers and our generators. And I said, well, that's great. Well, why not apply that same kind of discipline to your data? And they said, well, yeah, maybe that makes sense. But anyway, after the meeting, I go into the men's room, and I noticed that they have inventory tagged all of the toilets and urinals and sinks in the bathroom.

Allison Hartsoe: 16:57 Oh.

Doug Laney: 16:58 Because they're physical assets, right. And so they're on the balance sheet in effect, right? As part of the physical plan. So this is a company that claims they manage their major assets, like their trucks and their transformers, and Oh, by the way, their toilets, but not their data. And that's the kind of disconnect and just dichotomy that I see among organizations that we're trying to solve.

Allison Hartsoe: 17:16 Yeah, it's old legacy thinking. And I think sometimes people who think only about driving a lot of brands, and I'll drive a lot of customers through the door, and then I'll just magically have sales that they kind of think of that old fashion mentality too where it's almost like 1960s kind of shouting and legacy thinking.

Doug Laney: 17:34 Another type of legacy thinking is what you're doing with your data. You know, many organizations all use a type of data, like customer data for a single purpose. They will use it to drive maybe promotional campaigns, right? And then they'll report on it. They'll do some generate some pretty pie charts and bouncy bar charts, and dashing dashboards and not too much else with it. But the opportunities for leveraging data broadly within the organization and sometimes even externally, although there are of course are restrictions with customer data are really broad, and organizations shouldn't get pigeonholed into just thinking about using data a single way and then reporting on it. There's a lot more you can do with it.

Allison Hartsoe: 18:06 All right, and this is where the privacy issues start to come in. Our companies well-advised to maybe do their own navel-gazing first and clean up their own house before they start pulling in external data sets or sharing externally.

Doug Laney: 18:20 I wouldn't delay, I think there's incredible value in external data assets. There are 10 million data sets published by government organizations worldwide open data. There are at least 5,000 data brokers out there selling data. There are a trillion websites with data that can be harvested. There's data you can gather from partners, from suppliers, from customers. And so another old way of thinking is that most companies have a procurement department that's procuring physical assets like staplers and computer paper and laptops, but they don't have anybody who is out there procuring external data assets. And so I think one of the pot roles I'm looking forward is this role of a data curator. And I've seen a few companies that actually have a full-time data curator now who has their eyes on external data assets and how to get access to them.

Allison Hartsoe: 19:04 Oh, that's a great role. We're finding all kinds of new careers in this conversation.

Doug Laney: 19:10 I published a piece a while ago called the fresh hot roles for the Information Savvy Organization, which identified 30 or so new roles that we're either seeing or anticipating.

Allison Hartsoe: 19:19 Can we link to that article?

Doug Laney: 19:20 It's a piece of Gartner research so you can link to it, but only Gartner clients could get access to it. It's up to you.

Allison Hartsoe: 19:25 Okay. Well, at least we'll link to it. And then if your organization has a Gartner account, then you can certainly get hold of it, and if it doesn't, then you can read the summary.

Doug Laney: 19:35 Yeah, I mentioned some of these roles in my book as well. So for those who don't have access to Gartner.

Allison Hartsoe: 19:39 Yeah, and we actually didn't say at the beginning of the show you have been an analyst with Gartner, which is why you went this direction, and you've seen this unique stuff happening.

Doug Laney: 19:47 Right? Yeah. In fact, I wanted to actually execute on these infonomics ideas and help clients more than I was able to in an advisory capacity at Gartner. So that's why I moved back into the consulting world.

Allison Hartsoe: 19:57 Great. Are there other examples you'd like to share about either train wrecks or really successful ways people should be thinking about their data?

Doug Laney: 20:05 Well, there are so many great stories of companies monetizing their data. In fact, I've maintained a library of nearly 500 examples of how companies are using data and analytics in innovative ways. And there are just so many great examples.

Allison Hartsoe: 20:16 Are there any related to the customer, particularly customer lifetime value, customer equity? You know, we always loved that.

Doug Laney: 20:21 All right, so one example is there's a small insurance company called Infinity Insurance. We haven't really talked about dark data. But dark data is data that your organization has that it's sitting on and not really used for any purpose. Maybe you've archived it or something like that. So infinity realized that they were sitting on an archive of 10 years of adjuster reports. And so when somebody submits a claim, a claims adjuster goes out and investigates the claim and then writes up a report. And what they realize is that they can text mine that could use machine learning and text mining to analyze the content of those adjuster reports for certain kinds of words and terminology that were indicative of fraudulent claims. So they were able to identify the kinds of individuals, the kinds of customers and the kinds of situations that lend themselves to fraudulent claims and specifically identify them based on the language and the words and the content of those reports. So they were able to almost instantly identify tens of millions of dollars of previously paid out fraudulent claims that they subsequently subrogated and then big that model into their claims processing system so that when a customer submitted a claim, they could very quickly identify whether it was fraudulent or not.

Allison Hartsoe: 21:28 Wow. And did they have a crosscheck on that? Do you know like if you identify it as fraudulent, is there also a crosscheck that said, oh, that one was wrongly flag? In other words, is there a human that actually makes the final call?

Doug Laney: 21:39 Absolutely. But humans can't look at every claim, so it's a system that helps them identify them much more quickly. Another one is Walmart. Walmart has great search engine, right? Which helps people find the items that they're looking for online. They took into consideration 45 million searches per month. What would they realize is one month people were searching for a term, and it was leading to a high degree of shopping cart abandonment. The term that people were searching for was the word, house. And the search engine was taking them to housing goods and housewares and dog houses. And that wasn't at all what people were looking for. It happened to coincide with the week that the television show, the medical drama house premiered. And of course, what people were looking for was the box DVD set of the previous seasons. And so what Walmart realized was that their search engine was, as you say, staring at its own navel, right? And not considering what was happening in the world at the time. So they upgraded the search engine to take into consideration trends from social media and other sources. And when they did, they found that they were able to improve or reduce shopping cart abandonment by 10 to 15% across the board. Wow. In Walmart terms, that's like, I dunno, $1 billion a year.

Allison Hartsoe: 22:45 That is quite high.

Doug Laney: 22:47 Added value. Yeah.

Allison Hartsoe: 22:48 Yeah. That is one of those critical features where you expect the search engine to interpret the context of what you want. You type in house, and you want the TV show instead of the dog house, and I personally love that TV show. I've actually been, I didn't watch it when it came out, but we'd been watching it.

Doug Laney: 23:07 Great. I watched it for years, and I didn't realize Hugh Laurie was British. He's doing such a great American accent that I was easily fooled. Anyway, speaking of foreign companies, Westpac is a large bank in Australia, and they like many companies implemented this 360-degree view of their customer because they didn't understand all the channels that their customers were participating on. They couldn't get a complete picture of a customer, and they were only able to produce targeted offerings for 1% of their customers based on their understanding of them. And so they created that 360-degree view of the customer by integrating data across all of the channels, the ATM and the banks and other banking products that they had, credit cards and so forth. And then within a matter of months, they were able to target and market 25% of their customers leading to an increase of, I think it was about $22 million of additional revenue they were able to bring in from that.

Allison Hartsoe: 23:54 Nice. So it sounds like, Doug, if I'm hearing these examples right, and I'm understanding what people are doing wrong, it seems like in all cases the very first step is to understand what you have and is part of that understanding landing it into one data lake or is there a step before that's around governance of just understanding how you're going to think about data.

Doug Laney: 24:16 I think there's a step before that in understanding what data you have, creating an inventory directory, a metadata directory or a data catalog of your data. And that's part of a data governance process. So I would start with that with understanding what you have before trying to architect an integrated data solution, and whether it's a data lake or a data warehouse in series of data marts or a more of a hybrid, we call a logical data warehouse or a virtual data warehouse where data is virtually integrated. There's a variety of architectural considerations that will be dependent upon what data you have, where it is, and what your vision for monetizing it or generating economic benefits from it is.

Allison Hartsoe: 24:51 But it starts with your vision of what do you want to get out of it, right? Yeah, absolutely. Okay, so let's go into what would I do first, second, third. So I think the first thing is I would get that vision of what do I want to get out of it? Is it the targeting that I want to be able to do better, or is it the search engine performance? What is the area that my business will respond to most? I don't want to say aggressively, but we'll respond to best.

Doug Laney: 25:15 Right? And it doesn't have to be enterprise vision or a vision for enterprise analytics. Right? Most of these examples that I have in this library are very vocational, very functionally specific. They're based on a single hypothesis within a particular business unit, and yet they're generating millions of dollars of opportunities. So I would encourage organizations to think very specifically about particular business functions, business problems, what data exists, what data is not being used, what data could be harvested externally to drive improved value for that business process and not necessarily take an enterprise perspective. Now it's great to have a vision for the overall enterprise, which could start with, yeah, we want to manage and leverage our information more as an actual asset. I'm going to certainly encourage that.

Allison Hartsoe: 25:56 And is it important that that first example be a cross-department or Cross business unit or does it not matter?

Doug Laney: 26:03 I don't think it necessarily needs to be cross-business unit but very often involves data, a variety of data assets from the inside and outside the organization. So it's difficult to coordinate across business units until you have achieved some level of success. We still see a lot of data hoarding in businesses and I think that this notion of, you know one of the things that companies try to set up as part of their data governance process is the notion of a data owner, which is a great idea in concept to have somebody who is responsible and accountable for an information asset. But the vernacular, the concept of the moniker data owner, is one that carries a lot of baggage I think and leads to data hoarding and data silos within an organization. So as I started to look around at the way that other assets are managed, I came across the concept of a fiduciary or a trustee. And I think that's a much better model and moniker for a data owner, one that carries the same sort of responsibilities and accountabilities with it, even legal and ethical ones, but not the baggage of somebody who actually owns the data.

Allison Hartsoe: 26:58 And is that fiduciary trustee generally seen as the let's say a chief data officer or chief analytics officer or is that more of a,

Doug Laney: 27:07 no, no, it's somebody from the business.

Allison Hartsoe: 27:08 It is. So it's more of a,

Doug Laney: 27:10 Typically whoever is producing that data, whoever, whatever business leaders part of the organization is responsible for producing that data should be considered accountable and responsible for it. Now, the chief data officer may be the overall chief steward or trustee of data in the organization. And I guess that would be fine. But as far as describing particular trustees to data, I think it needs to be in the business. Data is a business asset at the end of the day. It's not an ITSN yet.

Allison Hartsoe: 27:33 Yes, this is true. So they start with an example and then what is the second step that they should think about?

Doug Laney: 27:37 So the first step was starting with an inventory, right? Or a vision and then an inventory

Allison Hartsoe: 27:42 start with a specific business problem function of how your data could support it.

Doug Laney: 27:46 Okay. And then I think the next thing you want to do is to gather support for it. It's fairly easy to come up with ideas for using data, but if the business isn't ready to act on that, then it doesn't go much further than a prototype. So the business needs to be prepared to change processes, to change organizational structures, to change the way that perhaps they're engaging customers to act on these ideas before actually executing them. And we have a process of feasibility tests or ideas that are generated to determine whether they're scalable, whether they're feasible from a managerial perspective, a technical perspective, an ethical perspective, a legal perspective before economic perspective to determine whether they're entirely feasible before moving forward with.

Allison Hartsoe: 28:26 There's nothing like putting a great data strategy together, and then you realize, oh, I can't get this passed legal or I can't get this out the door. And there's nothing that will cause your data scientists to leave faster than all of their models getting deep-sixed.

Doug Laney: 28:39 Absolutely right. In fact, another public store I can talk about is all states, so I met with the insurance company, all-state in their chief data scientist said, listen, the automobile companies want access to our claims data and they just, yeah, we were the good hands company, right? That's their motto, the Good Hands Company. So we can't be seen as selling our customer data. Even if we're de-identifying it, aggregating it, masking it, redacting it, whatever. We just can't be seen reputationally. It's too much of a reputational risk for us. So I said, well, obviously there's a lot of money here at stake, and they said, yeah, we know it. And so I suggested taking it off-brand. I said if brand and reputation is the issue, why did you start a separate company to do this? And that's exactly what they did. Seven months later, they launched Arity, A. R. I. T. Y., if anyone wants to check it out, and it's a platform for not only all-state but for any insurance company to monetize its data.

Allison Hartsoe: 29:29 Wow. That's a great suggestion that, and I don't know if I'm more stunned that they took the suggestion or that they executed on it, that they actually said, oh yeah, this is exactly what we want to do.

Doug Laney: 29:40 It's nice once in a while, it's an industry expert for people.

Allison Hartsoe: 29:44 So I'm going to say that that's probably the third step is once you figure out where are your business problem is, and your and what you wanted to address and you gather support for it, then the next thing is to start to execute on it. Execute and learn from that.

Doug Laney: 29:56 Yeah. And then there's the whole issue of managing information as an asset by applying asset management principles and practices. We as information professionals and I'm as much to blame as anybody over the last 2030 plus years, I'm have been making this stuff up on our own on how to manage information, and still, there are no ISO standards for how to do it properly. Yet there are ISO standards and industry standards for how to manage other kinds of assets, whether it's physical assets or financial assets or there's library science, there's records management standards. All that I think can be easily adopted and adapted for managing information as an actual asset. And so that's something that I'm working on with clients and in my research.

Allison Hartsoe: 30:32 Oh, fantastic. So Doug if people want to reach you, how can they get in touch if they have extra questions or want to talk to you?

Doug Laney: 30:39 Yeah, I'm easily found on Linkedin, Doug Laney on Linkedin or Doug_Laney on Twitter as well. It was a great way to reach me or contact me directly at Caserta, Doug.Laney@Caserta, c a s e r t a.com and meet me at the various conferences, or I'm frequently attending or speaking

Allison Hartsoe: 30:57 Are you speaking anything coming up soon?

Doug Laney: 30:59 I'm speaking at the chief data and analytics officer conference in Chicago coming up on a couple of weeks and also in two weeks at the MIT chief data officer summit in Cambridge, Massachusetts.

Allison Hartsoe: 31:11 Oh, excellent. I think I was just at the show, this chief data analytics officer conference in San Diego. I know that's a good one to go to. So wonderful. All right, Doug. Well, this has been just fantastic. I love everything we've discussed. There are so many new ideas and concepts you really on the forefront of an interesting field. You probably have been here for years, but I think this is just fantastic. So as always, everything we discussed is at ambition data.com/podcast, and I will include the links that we talked about earlier. And Doug, I really want to thank you for joining us today. I have totally enjoyed geeking out with our discussion.

Doug Laney: 31:45 My pleasure. Me Too.

Allison Hartsoe: 31:46 And I think, am I right that you also teach a course?

Doug Laney: 31:49 That's right. I was asked to teach a course on infonomics at the University of Illinois, the business school and the course is available actually online for students, for graduate students and also available via Coursera. So you can check that out.

Allison Hartsoe: 32:01 Excellent. Well, we will do that for sure. Remember everyone, when you use your data effectively, you can build customer equity. It's not magic. It's just a very specific journey that you can follow to get results.

Allison Hartsoe: 32:16 Thank you for joining today's show. This is your host, Allison Hartsoe, and I have two gifts for you. First, I've written a guide for the customer centric Cmo, which contains some of the best ideas from this podcast, and you can receive it right now. Simply text, ambitiondata, one word to, three, one, nine, nine, six, (31996) and after you get that white paper, you'll have the option for the second gift, which is to receive The Signal. Once a month. I put together a list of three to five things I've seen that represent customer equity signal not noise, and believe me, there's a lot of noise out there. Things I include could be smart tools. I've run across, articles I've shared cool statistics, or people and companies I think are making amazing progress as they build customer equity. I hope you enjoy the CMO guide and The Signal. See you next week on the Customer Equity Accelerator.

 

Key Concepts: Customer Lifetime Value, Marketing, Digital Data, Customer Centricity, Long-Term Customer Value, Marketing Leaders, Analytics, Creativity, Product Development, Audience Research

Who Should Listen: CAOs, CCOs, CSOs, CDOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, eCommerce, Data Scientists, Analysts, CMOs, Customer Insights Leaders, CX Analysts, Data Services Leaders, Data Insights Leaders, SVPs or VPs of Marketing or Digital Marketing, SVPs or VPs of Customer Success, Customer Advocates, Product Managers, Product Developers

 

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