 Live from Cambridge, Massachusetts, extracting the signal from the noise, it's theCUBE, covering the MIT Chief Data Officer and Information Quality Symposium. Now your host, Dave Vellante. Hi buddy, this is Dave Vellante. We're back at MIT in Cambridge, Massachusetts. I'm very excited to have a great segment coming up. We're going to talk about the value of information. Courtney Abercrombie is here from IBM. And Doug Laney, Vice President and Distinguished Analyst at Gartner Group is here to talk about a new approach that Gartner has developed. Folks, welcome to theCUBE. Courtney, welcome back. Hey. You can see when Doug, first time on theCUBE. Thanks Dave, I think so first time. Oh wow. Long time listener, first time colleague. Thank you very much for that. So Courtney, let's start with you. I mean, this is a good event for you guys. It's an intimate event. Saw you here last year. Yeah. This year you brought the big guns. Yeah we did. We got Vigiana himself, right? Crushed it yesterday, so congratulations. So how's this going for you? It's been great. We've got CDO 2.0 that we're trying to bring to the table for the CDO's benefit, talking more, a little less about data integration and trying to really move the agenda into market innovation. So pretty exciting talking about things like internet of things and insights as a service and new data sources, you name it. So kind of going from the boring but important to the sexy and important. Well, a little bit of both. We want, you know, you can't just be all potatoes. Sometimes you have to add a little, you know. Meat in the bone. Or chili lime chipotle or whatever. And I think this insights as a service is a really interesting concept. Yeah, that's right. Recognizing the value of exogenous data in addition to just the data that's within an organization's own four walls. So, you know, we think there's been just a lot of naval gazing when it comes to sourcing data for analytic purposes and IVMs, you know, taking a lead and looking externally at data sources. So you know this has been a topic on the mind of the C-suite for a long time. The value of information. How do you quantify the value of information? How do I sort of justify different moves? How do I make choices and decisions? So you've come up with a new framework called Infonomics, is that right? Infonomics. So talk more about that. What is it, where did it come from? So Infonomics is simply the concept that information is an actual enterprise asset, that it has value just like any other asset, physical or financial asset, and that organizations should probably consider not only managing it with the same discipline as other assets, but also valuing it and looking at more specifically the gap between the potential value that any given information asset can generate and its realized value. And organizations will find when they start measuring this that there's a significant gap there that if they were to close it it would generate significant business value. Right, so the point there being that there's a lot of information hanging around that is not unlocking value, but you can take steps to do that. What other steps that you can do? But it's really, it's about, you can't manage what you're not measuring and most companies aren't measuring. Most companies don't even have an inventory of their information assets. When I talk to companies, some of them have told me they have a better accounting of their office furniture or even the toilets in their company more so than the- Well, they're hard assets. Yeah, then they're information assets and when they think about what's actually generating value or what could generate value for their business it just doesn't make sense in today's world to not be quantifying the value of your data in any way. Okay, so how? How do we do it? So there are some companies that we've worked with that want to consider looking at the economic value of data. And for those purposes we've adapted some of the tried and true ways that valuation experts or accountants value other kinds of assets using the cost approach, the market approach or the income approach. But then there are companies that are really just kind of looking for an index of maybe some leading indicators of how one kind of data compares to another in terms of its potential utility, its functionality or how it impacts certain key performance indicators, non-financial key performance indicators. So before you get the CFO involved in establishing at least an internal valuation from an economic standpoint there are ways to measure information's potential and realize benefits using some indexes that we've developed that are non-economic. So the latter is maybe some kind of a scoring, the former is more of a hardcore measurement system. As if you were putting it on a balance sheet. You would use a cost approach, a market approach or an income approach. And so we've adapted those models for some of the nuances of data like when you consume it, you're actually not depleting it. When you sell it, you're actually licensing it. So we've had to make some adjustments to the kind of the typical models. So how are your clients Courtney dealing with this problem? Well, you know, it's interesting, CDOs, well, what we're trying to do right now is really talk to the upper management. We're talking to the senior leaders like CEOs who are trying to figure out maybe not in such technical terms just yet about the actual data assets but they're really trying to understand, look how in general do I get value from data and analytics? I know that data and analytics is important. I get it because I can tell that other companies are doing things out there that are revolutionary and cool but they don't really know how to go about it. And so a lot of them, their answer is, let's just put a CDO in charge and let them figure it out for us. But what happens is they get so mired down in all kinds of different constraints, organizationally, culturally speaking that they can't actually do anything that's visionary just yet. So what we have to encourage CDOs to do is really think about their roles differently. Not just an aggregator of data or an incubator of data, but how do I actually first and foremost provide a vision for the company that they can get behind? For example, John Deere. And I hope he doesn't mind me using his name but there is a great enterprising individual there that made a whole marketing video that shows what this new business model is gonna represent to the company. Not just being a tractor solution because what do we always think of? We think of tractors. I think of farmers, tractors. I mean, I'm from Texas so farmers are important. I'm not gonna say they're not but I don't think of tech and I don't think of new innovative things when I think of tractors. But this guy created a vision of being a farming data solution because farmers actually use a ton of data, believe it or not. There's irrigation points that they have to keep track of for water control purposes. There's, I mean, weather data that's streaming into their systems. And so this guy created this video that actually shows that it's kind of a fake it till you make it kind of idea but everybody can get behind that. Then they understand, I'm not just telling you, look, here's this farming data solution and it's got this and this and we've got to integrate that and that and it's gonna take 5G whatever years. Instead I'm showing you and I'm saying, look at this cool thing. It's like a, you know, mission impossible type of thing. This is like a really cool futuristic thing here. I want the company to get behind. There's no question then. You've created a vision that the CEO says, that's my legacy. I'm gonna leave that behind. Not a great single source of the truth which we know is important. Not saying it's not but I'm getting behind a vision and visions are so much more compelling to get behind than ROIs. And examples as well. So that's a great example. You know, at Gardner we've been compiling examples of how organizations are using information in innovative ways. We have hundreds in our library and one of them is a great agricultural example. Company called Tom Farms in Indiana that in the 1970s was farming about 700 acres. They're now farming 20,000 acres with the same number of employees by applying technology, including drones that fly over to look for for crop damage or irrigation problems. And it's just a really, really incredible. Or to deliver their milk from Amazon. Sorry. And they're delivering the same. And they've actually improved their. They're actually improved their. Skeet shooting for prizes they call that in Texas. Exactly. Yeah, they've actually even improved their ROI in that timeframe as well. So it's incredible what information can do if applied properly. So who should be or is asking you at Gardner for this type of information? Who's trying to consume it and what's the outcome? The information innovation examples. Well, very often we get called by enterprise architects. No, I'm sorry. I'm specific about the infonomics. The infonomics. So that's more than directed toward the CDO role itself. It is, okay. Which again is trying to establish some metrics and measurement for the improved use and deployment of information assets, the improved governments, the improved quality of data. So we have a variety of metrics to find to help organizations. Should be people using it for, okay, I should invest in this initiative because it's going to give me a better payback. And you said Courtney, ROI's. Absolutely. ROI's always interesting to financial people, right? Yeah, of course it is. Of course it is. That's what it comes down to. It's never going to go away. Right. Do you have, do you have a vision or, and do you have examples potentially of people who are actually using this type of approach to say invest in this relative to that? So some are. Analytics is going to drive my business more than governance. Some are using our infonomics valuation models to determine which information needs the most attention. Which information has the greatest gap between the realized value that it's generating and its potential value if we were to apply it more readily toward applicable business processes. And the ability to measure that helps you make determinations about which data to surface or to improve from a governance or quality standpoint. Others are just trying to improve the culture around information. There's a large financial services firm, a household name firm whose chief financial officer came to me and said, hey Doug, you know, we stink at managing information around here. But you've given us an idea, which is if I start putting dollar signs on top of data assets, I could radically change the behavior around here. Imagine the difference between whether you tell somebody, hey, you manage our customer database versus you now own our $500 million customer information asset. So that kind of thing can really, and then maybe not double, we're going to track it. We're going to be tracking the appreciation or depreciation of that asset over time. And now you're actually managing information the way you manage other kinds of assets in the organization. Well, so now you're talking about a P and L-like management system, but then you have to have a pretty good framework and more than a framework, you have to have a good system to be able to do that. And we found that the investors are paying attention. Investors are looking at companies that have greater information savvy. Do they have chief data officers? Do they have data science organizations? Do they have enterprise data governance functions? And they're starting to look at those kinds of factors in companies and valuing them for M&A deals or for other reasons. And we actually found that those kinds of companies have a market to book value that's two to three times higher than the norm. We looked at a ratio called, it's called a Tobens Q, which is basically a market to book value. And found that companies that have these information savvy, information-centric characteristics. And again, these are not companies that are selling data, but just regular companies that are more information savvy have this market to book value ratio that's significantly higher than the norm. So you talked about before the sort of unrealized value of data and then unlocking the potential for the business process. Presumably that goes through an application somewhere through some kind of IT infrastructure. Is that a fair assessment? Is that the process itself? No, the data. The data itself. Obviously you need to, you're delivering it. So I'm just thinking about, okay, so how do I get to, from theory, to actually implementing something that I can apply in a P&L-like sense, the example that you described through the application. And I have to look at cost, I have to look at value, and I have to be able to monitor and manage that on an ongoing basis. Obviously the cost side of information management is significant and we look at, in our models, look at the cost to acquire the data, or generate or capture the data, the cost to manage or administer the data, and then the cost to deliver data. And that's tempered against the value expectation, or the upside, the top line upside of that data. So part of me feels like, okay, so going through that process could be really painful to actually develop a system that is reliable and works in either spreadsheet mail or maybe I develop some software, I call IBM and they have some stuff off the shelf, I don't know. I mean, to be fair, companies already do this with other kinds of intangible assets. They go through the same kind of exercise with their patents. I'm sure IBM, the top producer of patents in the history of the world, it has some process for valuing the patents. Even if they're not used, they have some book of value, and so do copyrights and so do trademarks, but it's ridiculous in today's world. We're well into the information age, and the thing that's generating the most value for these kinds of companies is something that's not accounted for. Right, right, and so, but part of me says, well, maybe this scoring approach that you described earlier, that kind of relative is good enough, or maybe even better, combined with the marketing video that can change through an organization, rather than trying to boil the old data. You still need ideas, right? Once you determine that this data has some potential value, or is lacking a delivered value, you still need some approaches to coming up with ideas, and so that's why we have this library of examples. Many of them are IBM examples as well, on how organizations are doing really transformative things with data, and we use them to inspire our clients to move forward. You really have to use a combination of both a visionary approach where you're getting everybody in the team involved, and the senior leadership team, so that you don't run into obstacles as you go forward, and then you have to also approach it with the CDO at the same time. How are you going to measure your assets? How are you going to do this? So it's like, I consider his Infonomics idea to be more about putting another arrow in your quiver if you're a CDO, another way to prove value. Bring the vision first, get everybody behind you, and then continue to look at the cost structures of all the different data elements that you have, because data is only going to get more expensive, by the way, because people are going to want more of it, and augment, I mean, we're going to want Twitter, shoot, forget that, pretty soon it's going to be patient data, pretty soon it's going to be farming data, who knows what other kind of data, as farmers get more sophisticated, as patient care gets more sophisticated, we're going to want our own data. We may hold our patient care for ourselves on our cell phones in the future, and go to our doctors and our specialists, and show them our own records on our phones, just saying. So we may be the big, and then data's going to really have a great value, and then you'll wish you had talked to Doug. Well, for individual consumers, right, you can see that you'd much rather have what you just described than know you can't see your records because of HIPAA. Right, right, but if you opt in, and you can do that with your individual doctor, you can opt in to your own patient care, right? Get it off. So in thinking about the organizations, I can see this getting really political. Oh yeah. How'd you get the value of that data? My data's more valuable than her data, come on. So what have you seen in terms of that tension? We've seen someone at an executive level get involved in that, and make the determination, here's how we're going to apply this model, and really any valuation expert will tell you that applying a valuation model is as much art as it is science, but as long as you apply it consistently, then it's usable. And really it's not the relative value of data that's as important as measuring its gap and measuring its improvement or degradation over time. It's really the delta that's important. Yeah, because you're talking about unlocking value that drops right to the bottom line, or to the valuation. Do you have examples of that? I mean, you said you're building up this library. So I gave you one example. Oh, examples of information innovation. Oh my goodness, there's hundreds of examples, so. We got tons of them. We'll talk about them. Delaney, what are some of your favorites? What am I? Patient care apps. Oh no, you go first, go ahead. You know, one of my favorites is Coca-Cola, and they're public about this, and they now have the orange juice brand MinuteMade. So that's a Coke brand now. And they wanted to achieve a consistent flavor and texture of orange juice that's specific to different regions in the world and in the US. But the problem is that there are variances in the supply chain, so sometimes you're sourcing oranges from California, sometimes from Mexico, sometimes from Florida, and then there are disruptions in the supply chain due to variations in the weather. So what Coke does, MinuteMade does, is they've identified 600 discrete flavors of an orange, and all of the factors that go into that flavor, including weather and supply chain and economic pressures and whatnot, and they've developed a model that quantifies how to formulate orange juice using, they say, a quintillion different data points. And then Watson does the genomic frequency for you. And so when there's a disruption in the supply chain, say there's a hurricane or a freeze, they can replay on the entire business in five to 10 minutes. And maintain that consistency and quality. As best as possible. And so I think about it now, when I'm drinking orange juice, I'm actually drinking an algorithm. Yeah. You're drinking an algorithm. And there are tons of IBMs provided us with a number of really great examples as well. Our clients are phenomenal, I'll just say. You want to give us another one? Well, there's a lot, I mean, there's everyday cases too. I mean, if you want to think about any given coffee, you know, retailer, I can't use their name, but that has these cool reward apps on the phone, they can provide you with real-time promotions, you know, that you may go in and buy. Like myself, I love a good latte deal and I will be the first one in there to get my freebie when I've hit my 12. Real-time, real-time promotions, right? Real-time promotions, but right there on your phone, it's fast and easy. I mean, I think we were just having this conversation last time actually. One of our other examples from IBM is Watson is helping the largest Peruvian insurer, Remak. Yes. Remak in standardizing policies. So Remak has the challenge that they customize every policy and so it's difficult for claims adjusters to process those claims when they come in. So what they're using IBM Watson to do is to standardize those policies to make it quicker for adjusters to process them and they've gone from being able to process fewer than 1% of the claims, I think, to fully, you know, 25% of the claims and really, really, you know, help that insurer. So where are you going with Infonomics? It sounds like it's a relatively new concept that you've created. Well, it actually dates back to 9-11. Actually, it's an interesting story. After the 9-11 terrorist attacks, some clients started calling us lamenting not only, of course, the tragic loss of life, but also the loss of their data. So naturally, what they did was they contacted their insurers to process a claim and the insurers said, well, hold on a second. We don't think information constitutes property and what the insurance industry did was change the commercial general liability policy standard to exclude electronic data from P&C policies. The month after 9-11, literally, they didn't wait for the dust to settle before covering their, you know what, not to be outdone, the accounting industry backpedaled on it as well and now prohibits the capitalization of information, electronic data on balance sheets. And so there are a number of court cases now that have gone through the system and the courts are confused on this as well. So we're really in the Wild West as to whether information constitutes property, constitutes an asset. We argue it meets all the criteria of an asset. It's owned and controlled, it's exchangeable for cash, it generates what accountants call probable future economic benefit. And so I don't think there's any argument that meets the criteria of an asset. Now it's time to actually start treating it like one. Okay, and so this has been around for a while, certainly conceptually, and you're playing a role in terms of advancing the thinking around there. Where do you want to take in phonomics? Well, some people say it wouldn't be great if accounting standards actually changed. And I don't foresee that in their term, but I think organizations ought to, perhaps prepare for that, it's going to come at some point. And creating an internal balance sheet for the value of your information can help you in terms of monetizing it better, managing it better, deploying it better, recognizing challenges with the data better. Right now governance is a very touchy-feely kind of thing, there aren't a lot of metrics behind it. And I think there's a need for improved metrics. All right, Courtney, give you the last word. What's new for you? I mean, you're always in the go. Yeah, in about three weeks I'll be in Australia doing another Chief Data Officer event. We've got a lot of different regions that are popping up now with Chief Data Officers over in Singapore area. I mean, Australia, Latin America, as you talked to the two guys before. It's just everywhere. Everybody recognizes that data and analytics is the way to get at a competitive advantage. And that's really brand new thinking. I mean, because data's always been the lifeblood of a business in a transactional operational sense, fulfilling orders and things like that. But we haven't ever really tried to tackle it like this before. And the unstructured data, we have so many new emerging roles, which is my job at IBM. So we're really starting to define even brand new roles like data engineer. You know, of course data scientist has been around, but how well defined has it been? Has it had a career path? Have we really done everything that we could do with that as a role? No, we haven't. So those are all new areas that I'm focused on and trying to really set them and define them in the marketplace and really get some oomph, you know, as only IBM can do, just the same way it did with CIO role back in the day. It backs it and it just happens somehow, you know? Right, you make it happen. I'm going to will it. And you have symposium coming up. We have symposium coming up in the fall. Thank you in sunny Orlando, as always, and then point, the stations are on the world. Right, and you got, that's the insight, you're going to be an insight? I'll be an insight, and we also have a CDO forum that's coming up in November as well, and check out the IBM CDO website too. A lot of action, filling up the conference schedule with data. There you go. Thanks, David. Doug and Courtney, thanks very much for coming with you. Thanks. It was great to have you both. Take care. Next guest, right after this, this is theCUBE, we're live from MIT in Cambridge. Right back.