 From around the globe, it's theCUBE with digital coverage of MIT Chief Data Officer and Information Quality Symposium brought to you by SiliconANGLE Media. Hi everybody, this is Dave Vellante and welcome back to theCUBE's coverage of the MIT CDOIQ 2020 event. Of course, it's gone virtual. We wish we were all together in Cambridge. They were going to move into a new building this year for years. They've done this event at the tank center, moving into a new facility, but unfortunately it's going to have to wait at least a year, we'll see. But we got a great guest, nonetheless, Doug Laney is here. He's a business value strategist, a best-selling author, an analyst, a consultant, and a longtime CUBE friend. Doug, great to see you again. Thanks so much for coming on. Dave, great to be with you again as well. So I got to ask you, I mean, you have been an advocate for obviously measuring the value of data, the CDO role. I don't take this the wrong way, but I feel like the last 150 days have done more to sort of accelerate people's attention on the importance of data and the value of data, than all the great work that you've done. What do you think? It's always great when organizations actually take advantage of some of these concepts of data value. You may be speaking specifically about the situation with the United Airlines and American Airlines, where they have basically collateralized their customer loyalty data, their customer loyalty programs, to the tunes of several billion dollars each. And one of the things that's very interesting about that is that the third-party valuations of their customer loyalty data resulted in numbers that were larger than the companies themselves. So basically the value of their data, which is as we've discussed previously, off balance sheet, is more valuable than the market cap of those companies themselves, which is just incredibly fascinating. Well, and of course all you have to do is look to the Trillionaires Club, and now of course Apple pushing two trillion to really see sort of the value that the market places on data. But the other thing is of course COVID. I mean, everybody talks about the COVID acceleration. How have you seen it impact the awareness of the importance of data, whether it applies to business resiliency or even new monetization models? I mean, if you're not digital, you can't do business and digital is all about data. I think the major challenge that most organizations are seeing from kind of a data and analytics perspective due to COVID is that their traditional trend-based forecast models are broken. If you're a company that's only forecasting based on your own historical data and not taking into consideration or even identifying what are the leading indicators of your business, then COVID and the economic shutdown or slowdown have entirely broken those models. So it's raised the awareness of companies to say, hey, how can we predict our business now? We can't do it based on our own historical data. We need to look externally at what are those, external maybe global indicators or other kinds of markets that precede our own forecast, our own activity. And so the conversion from trend-based forecast models to what we call driver-based forecast models isn't easy for a lot of organizations to do. And one of the more difficult parts is identifying what are those external data factors from suppliers, from customers, from partners, from competitors, from complimentary products and services that are leading indicators of your business, and then recasting those models and executing on them. And that's a great point. If you think about COVID and how it's changed things, I mean, everything's changed, right? The ideal customer profile has changed. Your value proposition to those customers has completely changed. You got to rethink that. And of course, it's very hard to predict even when this thing eventually comes back some kind of hybrid mode. You used to be selling to people in an office environment. That's obviously changed. There's a lot that's permanent there. And data is potentially at least the forward indicator, the canary and the coal mine. Right. It also is the product and service. So not only can it help you and improve your forecasting models, but it can become a product or a service that you're offering. I mean, look at us right now. We would generally be face-to-face in person to person, but we're using video technology to transfer this content. And then one of the things that it took me a while to realize, but a couple of months after the COVID shutdown, it occurred to me that even as a consulting organization, CUSERDA focuses on North America, but the reality is that every consultancy is now a global consultancy because we're all doing business remotely. So there are no particular, or certain, there are no particular or real strong localization issues for doing consulting today. So we talked a lot over the years about the role of the CDO, how it's evolved, how it's changed. Of course, the early, the kind of pre-titled days it was coming out of a data quality world. And that's still vital. Of course, as we heard today from the keynote, it's much more public, much more exposed, different public data sources, but the role has certainly evolved initially into regulated industries like financial health care and government, but now many, many more organizations have a CDO. My understanding is that you're giving a talk in the business case for the CDO. Help us understand that. Yeah, so one of the things that we've been doing here for the last couple of years is running an ongoing study of how organizations are kind of impacted by the role of the CDO. And really it's more of kind of a correlation and looking at what are some of the qualities of organizations that have a CDO or don't have a CDO. So some of the things we found is that organizations with a CDO nearly twice as often mention the importance of data and analytics in their annual report. Organizations with a C-level CDO, meaning a true executive are four times more often to be likely to be using data to transform the business. And when we're talking about using data and advanced analytics, we found that organizations with a C-I-O, not a CDO responsible for their data assets are only half as likely to be doing advanced analytics in any way. So there are a number of interesting things that we found about companies that have a CDO and how they operate a bit differently. You know, I want to ask you about that. You mentioned the C-I-O and we've seen, we're increasingly seeing, you know, lines of reporting and peer reporting alter shift. The sands are shifting a little bit. We've seen, you know, in the early days, the CDO and still predominantly, I think, is sort of an independent organization. We've seen a few cases and increasingly number where they're reporting into the C-I-O. We've seen the same thing, by the way, with the chief information security officer, which used to be considered the Fox watching the henhouse. So we're seeing those shifts. We've also seen the CDO become, you know, more aligned with a technical role and sometimes even emerging out of that technical role. Yeah, I think the, I don't know. What I've seen more is that the CDOs are emerging from the business. Companies are realizing that data is a business asset. It's not an IT asset. There was a time when data was tightly coupled with applications and technologies, but today, you know, data is very easily decoupled from those applications and usable in a wider variety of contexts. And for that reason, you know, as data gets recognized as a business, not an IT asset, you want somebody from the business responsible for overseeing that asset. Yes, a lot of CDOs still report to the C-I-O, but increasingly more CDOs are, you're seeing, and I think you'll see some other surveys from other organizations this week where the CDOs are more frequently reporting up to the CEO level, meaning they're true, they are true executives. You know, I long advocated for the bifurcation of the IT organization into separate I and T organizations. Again, there's no reason other than for historical purposes to keep the data and technology sides of the organizations, you know, so intertwined. Well, it certainly makes sense that the Chief Data Officer would have an affinity with the lines of business, and you're seeing a lot of organizations really trying to streamline their data pipeline, their data life cycles, bring that together, you know, infuse intelligence into that, but also have a system, take a systems view, and really have the business be intimately involved, if not even own, you know, the data. You see a lot of emphasis on self-serve. What are you seeing in terms of that sort of data pipeline or the data life cycle, if you will, that used to be kind of, you know, wonky, hardcore techies, but now really involving a lot more constituents. Yeah, well, the data life cycle used to be so much short. When we look at the data life cycles, they're longer and they're more kind of data networks than a life cycle or a fly chain. And the reason is that companies are finding alternative uses for their data, not just using it for a single operational purpose or perhaps a reporting purpose, but finding that there are new value streams that can be generated from data. There are value streams that can be generated internally. There are a variety of value streams that can be generated externally. So we work with companies to identify what are those variety of value streams and then kind of test their feasibility. Are they ethically feasible? Are they legally feasible? Are they economically feasible? Can they scale? Do you have the technology capabilities? And so we'll run through a process of assessing the ideas that are generated. And the bottom line is that companies are realizing that data is an asset. It needs to be not just measured as one and managed as one, but also monetized as an asset. And as we talked about previously, data has these unique qualities that it can be used over and over again and it can be generate more data when you use it and it can be used simultaneously for multiple purposes. So companies like you mentioned like Apple and others have built business models based on these unique qualities of data, but I think it's really incumbent upon any organization today to do so as well. Well, when you observe those companies that we talk about all the time, the sort of data is at the center of their organization and they maybe put people around that data. That's got to be one of the challenge for many of the incumbents. If it talks about the data silos, the different standards, different data quality, that's got to be a fairly major blocker for people becoming a quote unquote data-driven organization. It is because some organizations were developed as people driven or product driven or brand driven or other things to try to convert to becoming data driven takes a high degree of data literacy or fluency. And I think there'll be a lot of talk about that this week. I'll certainly mention it as well. And so getting the organization to become data fluent and appreciate data as an asset and understand its possibilities and the art of the possible with data, it's a long road. And yeah, so the culture change that goes along with it is really difficult. Listen, we're working with a 150 year old consumer brand right now that wants to become more data driven and they're very product driven. And we hear the CIO say, we want people to understand that we're a data company that just happens to produce this product. We're not a product company that generates data. And once we realize that and start behaving in that fashion then we'll be able to really win and thrive in our marketplace. So one of the key roles of a chief data officer is to understand how data affects the monetization of an organization. Obviously there are for-profit companies of your healthcare organization. It's saving lives, obviously being profitable as well or at least staying within the budget depending upon the structure of the organization. But a lot of people I think oftentimes misunderstand that it's like, okay, do I have to become a data broker? Am I selling data directly? But I think you kind of pointed out many times and you just did that data is unlike oil. That's why we don't like that data in the oil analogy because it's so much more valuable and can be used. It doesn't follow the laws of scarcity. But what are you finding just in terms of people's application of that notion of monetization, cutting costs, increasing revenue. What are you seeing in the field? What's that spectrum look like? So one of the things I've done over the years is compile a kind of a library of hundreds and hundreds of examples of how organizations are using data and analytics in innovative ways. And I have kind of a book in process that hopefully will be out this fall. Sharing a number of those inspirational examples. So that's the kind of thing that organizations need to understand is that there are a variety of great examples out there and they shouldn't just necessarily look to their own industry. There are inspirational examples from other industries as well. Many clients come to me and they ask, what are others in my industry doing? And my flippant response to that is, why do you want to be in second place or third place? Well, why not take an idea from another industry, perhaps a digital product company and apply that to your own business? But yeah, like you mentioned, there are a variety of ways to monetize data. It doesn't involve necessarily selling it. You can deliver analytics. You can report on it. You can use it internally to generate, improve business process performance. And as long as you're measuring how data is being applied and what its impact is, then you're in a position to claim that you're monetizing it. But if you're not measuring the impact of data on business processes or on customer relationships or partner supply relationships or anything else, then it's difficult to claim that you're monetizing it. But one of the more interesting ways that we've been working with organizations to monetize their data certainly in light of GDPR and the California Consumer Privacy Act where I can't sell you my data anymore, but we've identified ways to monetize your customer data in a couple of ways. One is to synthesize the data, create synthetic data sets that retain the original statistical kind of anomalies in the data or features of the data, but don't share actually any PII. But another interesting way that we've been working with organizations to monetize their data is what I call inverted data monetization, where again, I can't share my customer data with you, but I can share information about your products and services with my customers and take a referral fee or a commission based on that. So let's say I'm a hospital and I can't sell you my patient data, of course, due to a variety of regulations, but I know who my diabetes patients are and I can introduce them to your healthy meal plans, to your gym memberships, to your at home glucose monitoring kits. And again, take a kind of a referral fee or a cut of that action. So we're working with customers in the financial services firm industry and in the healthcare industry on just those kinds of examples. And we've identified tens, hundreds of millions of dollars of incremental value for organizations from their data that they, we're just kind of sitting on. Interesting, I don't call you a business value strategist at the top. Where do you see, where in the S curve do you see you're able to have the biggest impact? I mean, I doubt that you enter organizations where you say, oh, they've got it all figured out, they can't use my advice, but as well, sometimes in the early stages, you may not be able to have as big of an impact because there's not top down support or whatever. There's too much, you know, technical debt, et cetera. Where are you finding you can have the biggest impact though? Generally, we don't come in and run those kinds of data monetization or information innovation exercises unless there's some degree of executive support. I've never done that at a lower level, but certainly there are lower level, more kind of immediate and vocational opportunities for data to deliver value through simply analytics. One of the simple examples I give is I sold a home recently and when you put your house on the market, everybody comes out of the woodwork, the fly-by-night mortgage companies, the moving companies, the box companies, the painters, the landscapers all know you're moving because your data's in the US and the MLS directory. And it was interesting, the only company that didn't reach out to me was my own bank. And so they lost kind of the opportunity to introduce me to a mortgage, to retain me as a client, to introduce me to my new branch, print me new checks, move the stuff in my safe deposit box, all of that. They missed a simple opportunity. And I'm thinking, this doesn't require rocket science to figure out which of your customers are moving. The MLS database, or you can harvest it from Zillow or other sites, is basically public domain data. And I was just thinking, how stupid simple would it have been for them to hire a high school programmer, give them a can of Red Bull and say, listen, match our customer database to the MLS database to let us know who's moving on a daily or weekly basis. Some of these solutions are pretty simple. So is that part of what you do come in with just like hardcore tactical ideas like that? You're also doing strategy. I mean, tell me more about how you're spending your time. I try to take more of a broader approach where we look at the data itself. And again, people have said, if you tortured enough, what would it tell us? It's going to take that angle. We look at examples of how other organizations have monetized data and think about how to apply those and adapt those ideas to the company's own business. We look at key business drivers internally and externally. We look at edge cases for their customers' businesses. We run through some hypothesis-generating activities. There are a variety of different kinds of activities that we do to generate ideas. And most of the time when we run these workshops, which last a week or two, we'll end up generating anywhere from 35 to 50 pretty solid ideas for generating new value streams from data. So when we talk about monetizing data, that's what we mean, generating new value streams. But like I said, then the next step is to go through kind of that feasibility assessment and determine which of these ideas you actually want to pursue. So you're, of course, a longtime industry watcher as well. As a former gardener analyst, you kind of have to be. My question is, if I think back, I've been around a while, if I think back at like the peak of Microsoft's prominence in the PC era, it was like 1990. Windows 95 and you felt like, wow, Microsoft is so strong. And then, of course, Linux comes along and a lot of open source changes and lo and behold, a whole new set of leaders emerges. And you kind of see the same thing today with the Trillionaires Club. And you feel like, wow, even COVID has been a tailwind for them. But you think about, okay, where could the disruption come to these large players that own huge clouds? They have all the data. Is data potentially a disruptor for these, what appear to be insurmountable odds against the newbies? There's always people coming up with new ways to leverage data or new sources of data to capture. So yeah, they're certainly not going to be around forever. But it's been really fascinating to see the transformation of some companies. Like, I think nobody really kind of exemplifies it more than IBM, where they emerged from, originally selling meat slicers, right? The Dayton meat slicer was their original product, right? And then they evolved into manual business machines and then electronic business machines. And then they dominated that, then they dominated the kind of the software early, mainframe software industry, then they dominated the PC industry, then they dominated the services industry to some degree. And so, they're starting to get into data. And I think following that kind of trajectory is something that really any organization should be looking at. When do you actually become a data company? Not just a product company or a service company or, you know, so. Yeah, yeah, I mean, we have, Interpol Bandari is one of our new guests here. He's the Chief Data Officer of IBM, you know him well. And he talks about the journey that he's undertaken to transform the company into a data company. I think a lot of people don't really realize what's actually going on behind the scenes. Whether it's, you know, financially oriented or revenue opportunities, but with, you know, one of the things he stressed to me in our interview was that on average, they're reducing the end to end cycle time from raw data to insights by 70%. That's on average. And that's just an enormous, you know, for a company that size, it's just enormous cost savings or revenue generating opportunity. Yeah, there's no doubt that the technology behind data pipelines is improving. And the process from moving data to those, from those pipelines directly into, you know, predictive or diagnostic or prescriptive output is a lot more accelerated than the early days of data warehousing. Is the skills barrier is acute? I mean, it seems like it's lessened somewhat, you know, the early Hadoop days you needed, you know, the even data scientists is, is there still just a massive skills shortage where we started to attack that? Well, I think companies are figuring out a way around the skills shortage by doing things like self-service analytics and focusing on more kind of easy to use mainstream type, you know, AI or advanced analytics technologies. But there's still very much a need for data scientists and organizations and a difficulty in finding people that are true data scientists, you know, there's no real kind of certification. And so really anybody can call themselves a data scientist, but I think companies are getting good at interviewing and determining whether somebody's got the goods or not. But then there are other types of skills that we don't really focus on, like the data engineering skills. You're still a huge need for data engineering. You know, data doesn't self-organize. You know, you can, there are some augmented analytics technologies that will automatically generate analytic output, but there really aren't technologies that automatically kind of self-organize data. And so there's a huge need for data engineers. And then as we talked about, there's a large interest in external data and harvesting that and ingesting it and even identifying what external data is out there. So one of the emerging roles that we're seeing, if not the sexiest role of the 21st century is the role of the data curator, somebody who kind of acts as a librarian, identifying external data assets that are potentially valuable, testing them, evaluating them, negotiating, and then figuring out how to ingest that data. So I think that's a really important role for an organization to have. You know, most companies have an entire department that procures office supplies, but they don't have anybody who's procuring data supplies. And when you think about, you know, which is more valuable to an organization, how to not have somebody who's dedicated to identifying the world of external data assets that are out there. You know, there are 10 million data sets published by government organizations and NGOs. There are thousands and thousands of data brokers aggregating and sharing data. There's web content that can be harvested. There's data from your partners and suppliers. There's data from social media. So to not have somebody who's on top of all that is I think a gross, it demonstrates gross negligence by the organization. That is such an enlightening point, Doug. My last question is, I wonder how, if you can share with us how the pandemic has affected your business personally. I mean, as a consultant, you're on the road a lot, obviously not on the road so much. You're doing a lot of chalk talks, et cetera. How have you managed through this and how have you been able to maintain your efficacy with your clients? You know, most of our clients, given that they're kind of in the digital world a bit already, made the switch pretty quick. Some of them took a month or two, some things went on hold, but we're still seeing the same level of enthusiasm for data and doing things with data. In fact, some companies have kind of taken our advice that data could be their best defense in a crisis like this. It's affected our business and it's enabled us to do much more international work more easily than we used to. And I probably spent a lot less time on planes, so it gives me more time for kind of writing and speaking and actually doing consulting. So that's been nice as well. Yeah, there is that bonus, you know, obviously theCUBE is not doing physical events anymore, but hey, we've got two studios operating and Doug Laney, really appreciate your coming on. Always a great guest and sharing your insights and have a great MIT CDOIQ. Thanks, you too Dave, take care, cheers. Thanks Doug. And thank you everybody for watching. This is Dave Vellante for theCUBE, our continuous coverage of the MIT Chief Data Officer Conference MIT CDOIQ. We're back right after this short break.