 Hi, I'm Peter Burris. Welcome to another Wikibon action item. We're once again broadcasting from theCUBE's beautiful Palo Alto, California studio. And I'm joined here in the studio by George Gilbert and David Floyer. And in remote we have Jim Cabela's, David Vellante, Neil Raden and Ralph Finos. Hi guys. Hey. How are you all doing? This is a great, great group of people to talk about the topic we're going to talk about guys. We're going to talk about the notion of de-risking digital business. Now, the reason why this becomes interesting is the Wikibon perspective for quite some time has been that the difference between business and digital business is the role that data assets play in a digital business. Now, if you think about what that means, every business institutionalizes its work around what it regards as its most important assets. A bottling company, for example, organizes around the bottling plant. A financial services company organizes around the regulatory limit or impacts or limitations on how they share information and what is regarded as fair use of data and other resources and assets. The same thing exists in a digital business. There's a difference between, say, Sears and Walmart. Walmart made use of data differently than Sears and had specific assets that are employed and had a significant impact on how the retail business was structured. Along comes Amazon, which is even deeper in the use of data as a basis for how it conducts its business. And Amazon is institutionalizing work in quite different ways and has been incredibly successful. We could go on and on and on with a different, with a number of different examples of this and we'll get into that. But what it means, ultimately, is that the tie between data and what is regarded as valuable in the business is becoming increasingly clear given if it's not perfect. And so, traditional approaches to de-risking data through backup and restore now needs to be rethought so that it's not just de-risking the data, it's de-risking the data assets. And since those data assets are so central to the business operations of many of these digital businesses, what it means to de-risk the whole business. So, David Vellante, give us a starting point. How should folks think about this different approach to envisioning business and digital business and the notion of risk? Okay, thanks, Peter. I mean, I agree with a lot of what you just said. I'm going to pick up on that. I see the future of digital businesses really built around data, sort of agreeing with you, building on what you just said, really where organizations are putting data at the core. And increasingly, I believe that organizations that have traditionally relied on human expertise as the primary differentiator will be disrupted by companies where data is the fundamental value driver. And I think there's some examples of that and I'm sure we'll talk about it. And in this new world, humans have expertise that leverage the organization's data model and create value from that data with augmented machine intelligence. I'm not crazy about the term artificial intelligence. And you hear a lot about data-driven companies. And I think such companies are going to have a technology foundation that is increasingly described as autonomous, aware of the anticipatory, and importantly, in the context of today's discussion, self-healing. So able to withstand failures and recover very quickly. So de-risking a digital business is going to require new ways of thinking about data protection and security and privacy. And specifically as it relates to data protection, I think it's going to be a fundamental component of the so-called data-driven companies' technology fabric. It's going to be designed into applications, into data stores, into file systems, into middleware and into infrastructure as code. And many technology companies are going to try to attack this problem from a lot of different angles, trying to infuse machine intelligence into hardware, software, and automated processes. And the premise is that leading companies will architect their technology foundations, not as a set of remote cloud services that they're calling, but rather as a ubiquitous set of functional capabilities that largely mimic a range of human activities, including storing, backing up, and virtually instantaneous recovery from failure. So let me build on that. So what you're kind of saying, if I can summarize, and we'll get into whether or not it's human expertise or some other approach or notion of business. But you're saying that increasingly, patterns in the data are going to have absolute consequential impacts on how a business ultimately behaves. We got that right? Yeah, absolutely. And how you construct that data model and provide access to that data model is going to be a fundamental determinant of success. Neil Raiden, does that mean that people are no longer important? Well, no, no, I wouldn't say that at all. I was talking with the head of a medical school a couple of weeks ago, and he said something that really, really resonated. He said that there's many doctors who graduated at the bottom of their class as the top of their class. And I think that's true of organizations too. You know what, in 20 years ago, I had the privilege of interviewing Peter Drucker for an hour, and he foresaw this 20 years ago. He said that people who run companies have traditionally had IT departments that provided operational data, but they needed to start to figure out how to get value from that data and not only get value from that data, but get value from data outside the company, not just internal data. So he kind of saw this big data thing happening 20 years ago. Unfortunately, he had a prejudice for senior executives. You know, he never really thought about any other people in an organization except the highest people. And I think what we're talking about here is really the whole organization. I think that I have some concerns about the ability of organizations to really implement this without a lot of fumbles. I mean, it's fine to talk about the five digital giants, but there's a lot of companies out there that, you know, the bar isn't really that high for them to stay in business, and they just seem to get along. And I think if we're going to de-risk, we really need to help companies understand the whole process of transformation, not just the technology. Well, take us through it. What is this process of transformation that includes the role of technology but is bigger than the role of technology? Well, it's like anything else, right? There has to be communication, there has to be some element of control. There has to be a lot of flexibility. And most importantly, I think there has to be acceptability by the people who are going to be affected by it, that it's the right thing to do. And I would say you start with assumptions. I call it assumption analysis. In other words, let's all get together and figure out what our assumptions are and see if we can't line them up. Typically, IT is not good at this. So I think it's going to require the help of a lot of practitioners who can guide them. So Dave Vellante, reconcile one point that you made. I want to come back to this notion of how we're moving from businesses built on expertise and people to businesses built on expertise, resident as patterns in the data or data models. Why is it that the most valuable companies in the world seem to be the ones that have the most real hardcore data scientists? Isn't that an expertise in people? Yeah, it is. And it's worth, I think it's worth pointing out. Look, stock market is volatile, but right now the top five companies, Apple, Amazon, Google, Facebook, and Microsoft in terms of market cap account for about 3.5 trillion. And there's a big distance between them. They've clearly surpassed the big banks and the oil companies. Now again, that could change, but I believe that it's because they are data-driven, so-called data-driven. Does that mean they don't need humans? No, but the human expertise surrounds the data as opposed to most companies, human expertise is at the center and the data lives in silos. And I think it's very hard to protect data and leverage data that lives in silos. Yeah, so here's where I'll take exception to that, Dave, and I want to get everybody to build on top of this just very quickly. I think that human expertise has surrounded in other businesses the buildings or the bottling plant or the wealth management or the platoon. So I think that it's that the organization of assets has always been the determining factor of how a business behaved. And we institutionalized work, in other words, where we put people based on the business's understanding of assets. Do you disagree with that? Is that, are we wrong in that regard? I think data scientists are an example of re-institutionalizing work around a very core asset, in this case, data. Yeah, you're saying that the most valuable asset is shifting from some of those physical assets, the bottling plant, et cetera, to data. Yeah, we are, we are, we are, absolutely. All right, David Flare. So I'd like to, I'd like to come in. Let me give you an example from the news. Cygna's acquisition of Express Scripts for $67 billion. Who the hell is Cygna, right? Connecticut General was just a sleepy life insurance company and INA was a second tier property and casualty company. They merged a long time ago. They got into health insurance and suddenly who's Express Scripts? I mean, that's a company that nobody ever even heard of. They're a pharmacy benefit manager. They're an information company period. That's all they do. David Flare, what does this mean from a technology standpoint? So I wanted to emphasize one thing that evolution has always taught us, that you have to be able to come from where you are. You have to be able to evolve from where you are and take the assets that you have. And the assets that people have are their current systems of record, other things like that. They must be able to evolve into the future to better utilize what those systems are. So what are the, and the other thing I want to say. Let me give you an example, just to interrupt you. Because this is a very important point. One of the primary reasons why the telecommunications companies whom so many people believe, analysts believe have this fundamental advantage because so much information is flowing through them is when you're writing assets off for 30 years that kind of locks you into an operational mode, exactly. So, and the other thing I want to emphasize is that the most important thing is sources of data, not the data itself. So for example, real-time data is very, very important. So what is your source of your real-time data? If you've given that away to Google or to your IoT vendor, you have made a fundamental strategic mistake. So understanding the sources of data, making sure that you have access to that data is going to enable you to be able to build the sort of processes and data digitalization. So let's turn that concept into kind of a, a Jeffrey Moore kind of strategy bromide. At the end of the day, you look at your value proposition and then what activities are central to that value proposition and what data is thrown off by those activities or what data is required by those activities. Right, both internal, yeah. The internal and external data, what are those sources that you require? Yes, that's exactly right. And then you need to put together a plan which takes you from where you are as the sources of data and then focuses on how you can use that data to either improve revenue or to reduce costs or a combination of those two things as a series of specific exercises. And in particular, using that data to automate in real time as much as possible. That to me is the fundamental requirement to actually be able to do this and make money from it. If you look at every example, it's all real time. It's real time bidding at Google. It's real time allocation of resources by Uber. That is where people need to focus on. So it's those steps, practical steps that organizations need to take that I think we should be giving a lot of focus on. You mentioned Uber. David Vellante, we're just not talking about the, once again, talking about the Uberization of things, are we? Or is that what we mean here? So what we'll do is we'll turn the conversation very quickly over to you, George. And there are existing today a number of different domains where we're starting to see a new emphasis on how we start pricing some of this risk. Because we think about de-risking as it relates to data. Give us an example of one. Well, we were talking earlier in financial services, risk itself is priced just the way time is priced in terms of what premium you'll pay in terms of interest rates. But there's also something that's softer that's come into sort of much more widely held consciousness recently which is reputational risk, which is different from operational risk. Reputational risk is about, are you a trusted steward for data? Some of that could be personal information. And a use case that's very prominent now with the European GDPR regulation is, if I ask you as a consumer or an individual to erase my data, can you say with extreme confidence that you have? That's just one example. Well, I'll give you a specific number on that. We've mentioned it here on Action Item 4. I had a conversation with Chief Privacy Officer a few months ago who told me that they had priced out what the fines to Equifax would have been had the problem occurred after GDPR fines were being enacted and was $160 billion was the estimate. There's not a lot of companies on the planet that could deal with $160 billion liability like that. Okay, so we have a price now that might have been kind of sort of mushy before but the notion of trust hasn't really changed over time but it's changed as the technical implementations that support it. And in the old world with systems of record we basically collected from our operational applications as much data as we could put it in the data warehouse and its data mart satellites and we tried to govern it within that perimeter but now we know that data basically originates and goes just about anywhere. There's no well-defined perimeter. It's much more porous, far more distributed. You might think of it as a distributed data fabric and the only way you can be a trusted steward of that is if you now across the silos without trying to centralize all the data that's in silos across them you can enforce who's allowed to access it, what they're allowed to do, audit who's done what type of data, when and where and then there's a variety of approaches just to pick two. One is where it's discovery oriented to figure out what's going on with the data estate using machine learning. This is elation as an example. Then there's another example which is where you try and get everyone to plug into what's essentially a new system catalog that's not in a DBMS but that acts like the fabric for your data fabric DBMS and that's atlas. That's an example of one of the companies that are looking at coming at this. But when we think, Dave Vellante, coming back to you for a second, when we think about the conversation there's been a lot of presumption or a lot of, another bromide, analysts like to talk about don't get uberized. We're not just talking about getting uberized. We're talking about something a little bit different, aren't we? Well, yeah, absolutely. I think uber is going to get uberized personally. And so, but I think there's a lot of evidence. I mentioned the big five, but if you look at Spotify, Waze, Airbnb, yes Uber, yes Twitter, Netflix, Bitcoin is an example, 23 and me. These are all examples of companies that go back to what I said before of putting data at the core and building human expertise around that core to leverage that expertise. And I think it's easy to sit back, companies for some companies to sit back and say, well, I'm going to wait and see what happens. But to me anyway, there's a big gap between kind of the haves and the have nots. And I think that that gap is around applying machine intelligence to data and applying cloud economics, zero marginal economics and API economy always on sort of mentality, et cetera, et cetera, and that's what the economy is, my view anyway, is going to look like in the future. So let me put out a challenge. Jim, I'm going to come to you in a second on very quickly on some of the things that start looking like data assets. But today, when we talk about data protection, we're talking about simply a whole bunch of applications and a bunch of devices, just spinning that data off. So we have it at the third site and then we can, and it takes some career of time. And then if there's a catastrophe or we have, you know, large or small, being able to restore it often in hours or days. So we're talking about an improvement on RPO and RTO. But when we talk about data assets, and I'm going to come to you in a second with that, David, for, but when we talk about data assets, we're talking about not only the data, the bits, we're talking about the relationships and the organization and the metadata as being a key element of that. So David, so I'm sorry, Jim Cabela's, just really quickly, 30 seconds. Models, what do they look like? What are the new nature of some of these assets look like? Well, the new nature of these assets are the machine learning models that are driving so many business processes right now. And so really the core assets there are the data, obviously, from which they're developed and also from which they're trained, but also very much the knowledge of the data scientists and engineers who build and tune this stuff. And so really what you need to do is you need to protect that knowledge and grow that knowledge base of data science professionals in your organization in a way that builds on it. And hopefully you keep the smartest people in house and they can encode more of their knowledge and automated programs to manage the entire pipeline of development. But we're not just, we're not talking about files. We're not even talking about databases, we're talking about algorithms. Algorithms and models are today's technologies really, really set up to do a good job of protecting the full organization of those data assets. I would say that they're not even being thought about yet. And going back on what Jim was saying, those data scientists are the only people who understand that in the same way as in the year 2000, the COBOL programmers were the only people who understood what was going on inside those applications. And we as an industry have to allow organizations to be able to protect the assets inside their applications and use AI, if you like, to actually understand what is in those applications and how are they working. And I think that's an incredibly important de-risking is ensuring that you're not dependent on a few experts who could leave at any moment in the same way as the COBOL programmers could have left. But it's not just the data, and it's not just the metadata. It really is the data structures and the models and everything else. It's a whole way that this has been put together and the reason why. And the ability to continue to upgrade that and change that over time. So those assets are incredibly important, but at the moment there isn't a way that you can, there isn't technology available for you to actually protect those assets. So if I combine what you just said with what Neil Raden was talking about, David Vellante has put forward a good vision of what's required. Neil Raden's made the observation that this is going to be much more than technology. There's a lot of change, not change management at a low level inside the IT, but business change. And the technology companies also have to step up and be able to support this. We're seeing this come, we're seeing a number of different vendor types start to enter into this space. Certainly the storage guys, Dell EMC are talking about doing a better job of data protection. We're seeing middleware companies, TIPGO, WANDISCO talk about doing this differently. We're seeing file systems, Scality, Weka IO talk about doing this differently. Backup and restore companies, Veeam, Veritas. I mean, everybody's looking at this and they're all coming at it just really quickly, David. Where's the inside track at this point? For me, it is so much white spaces to be unbelievable. So nobody has an inside track yet? Nobody has an inside track. Just to start with a few things. It's clear that you should keep data where it is. The cost of moving data around an organization from inside to out is crazy. So companies that keep data in place or technologies can keep data in place are going to have an advantage. Much, much greater advantage. Sure there must be backup somewhere, but you need to keep the working copies of data where they are because it's the real-time access. Usually it's important. So if it originates in the cloud, keep it in the cloud. If it originates in a data provider in another cloud, that's where you should keep it. If it originates on your premise, keep it where it originated. Unless you need to combine it and then put that at the new origination point. Then you're taking subsets of that data and then combining that up itself. So that's my first, will be my first point. So organizations are going to need to put together what George was talking about is this metadata of all the data, how it interconnects, how it's being used, the flow of data through the organization. It's amazing to me that when you go to an IT shop, they cannot define for you how the data flows through that data center or that organization. That's the requirement that you have to have and AI is going to be part of that solution of looking of all of the applications and the data and telling you where it's going and how it's working together. So a second thing would be companies that are able to build or conceive of networks as data. We'll also have an advantage. And I think I had a third one, companies that demonstrate pernil's observations, a real understanding of the unbelievable change that's required. You can't just say, oh, Facebook wants this, therefore everybody's going to want it. There's going to be a lot of push marketing that goes on in the technology side. All right, so let's get to some action items. David Vellante, I'll start with you. Action item. Well, the future is going to be one where systems see, they talk, they sense, they recognize, they control, they optimize. It may be tempting to say, you know what? I'm going to wait. I'm going to sit back and wait to figure out how I'm going to close that machine intelligence gap. I think that's a mistake. I think you have to start now and you have to start with your data model. George Gilbert, action item. I think you have to keep in mind the guardrails related to governance and trust when you're building out applications on this sort of new data fabric and you can take the approach of a platform-oriented one where you're plugging into an API like Apache Atlas that Hortonworks is driving or discovery-oriented one. As David was talking about, which would be something like Elation using machine learning. But if, let's say the use case starts out as an IoT edge analytics and cloud inferencing, that data science pipeline itself has to now be part of this fabric, including the output of the design time, meaning the models themselves. They can be managed. Excellent. Jim Capil, has she been pretty quiet? But I know you got a lot to offer, action item. Jim. I'll be very brief. What you need to do is protect your data science knowledge base. That's the way to de-risk this entire process. And that involves more than just a data catalog. You need a data science expertise registry within your distributed value chain. And you need to manage that as a very human asset that needs to grow. That is your number one asset going forward. Ralph Finoce, you've also been pretty quiet. Action item, Ralph. Yeah, I think you've got to be careful about what you're trying to get done. Whether, and it depends on your industry, whether it's finance or whether it's an attainment business, there are different requirements about data in those different environments. And you need to be cautious about that. And you need leadership on the executive business side of things. The last thing in the world you want to do is depend on data scientists to figure this stuff out. And I'll give you the second or last answer for action item, Neil Raiden action item. I think there's been a lot of progress lately in creating tools for data scientists to be more efficient. And they need to be because the big digital giants are draining them from other companies. So that's very encouraging. But in general, I think becoming a data driven digital transformation company for most companies is a big job. And I think they need to do it in peace parts because if they try to do it all at once, they're going to be in trouble. All right, so that's great conversation guys. Oh, David Floyer, action item. David's looking at me saying, what about me? David Floyer, action item. They are him. So my action item comes from an Irish proverb, which if you ask the directions, they will always answer you. I wouldn't start from here. So the action item that I have is if somebody is coming in saying you have to redo all of your applications and rewrite them from scratch and start in a completely different direction, that is going to be a 20 year job and you're not going to ever get it done. So you have to start from what you have, the digital assets that you have and you have to focus on improving those with additional applications, additional data, using that as the foundation for how you build that business with a clear long term view. And if you look at some of the examples that were given early, particularly in the insurance industries, that's what they did. Thank you very much guys. So let's do an overall action item. We've been talking today about the challenges of de-risking digital business, which ties directly to the overall understanding of the role that data assets play in businesses and the technology's ability to move from just protecting data, restoring data, to actually restoring the relationships in the data, the structures in the data, and very importantly the models that are resident in the data. This is going to be a significant journey. There's clear evidence that this is driving new valuation within the business. Folks talk about data as a new oil. We don't necessarily see things that way because data, quite frankly, is a very, very different kind of asset because it can be shared, because it doesn't suffer the same limits on scarcity. So as a consequence, what has to happen is you have to start with where you are. What is your current value proposition and what data do you have in support of that value proposition? And then whiteboard it, clean slate it and say, what data would we like to have in support of the activities that we perform, figure out what those gaps are, find ways to get access to that data through piecemeal, piecepart investments that provide a roadmap of priorities looking forward. Out of that will come a better understanding of the fundamental data assets that are being created, new models of how you engage customers, new models of how operations works in a shop floor, new models of how financial services are being employed and utilized. And use that as a basis for then starting to put forward plans for bringing technologies in that are capable of not just supporting the data and protecting the data, but protecting the overall organization of data in the form of these models, in the form of these relationships, so that the business can, as it creates these, as it throws off these new assets, treat them as the special resource that the business requires. Once that is in place, we'll start seeing businesses more successfully reorganize, re-institutionalize the work around data, and it won't just be the big technology companies who have, who people call digital native, that are well down this path. I want to thank George Gilbert, David Floyer here in the studio with me, David Vellante, Ralph Finos, Neil Raiden and Jim Cabellos on the phone. Thanks very much guys. Great conversation. And that's been another Wikibon Action Item.