 Hi, I'm Peter Burris, and welcome to another Wikibon Action Item. This was a very interesting week in the tech industry, specifically because IBM's Think Conference was aggregated in a large number of people. Now, theCUBE was there, Dave Vellante, John Furrier and myself, all participated in somewhere in the vicinity of 60 or 70 interviews with thought leaders in the industry, including a number of very senior IBM executives. The reason why this becomes so important is because IBM made a proposal to the industry about how some of the digital disruption that the market faces is likely to unfold. The normal approach or the normal mindset that people have used is that startups, digital native companies were going to change the way that everything was going to operate and the dinosaurs were going to go by the wayside. IBM's interesting proposal is that the dinosaurs actually are going to learn to dance, utilizing or playing on a book title from a number of years ago. And the specific argument was laid out by Ginny Rametti in her keynote when she said that there are a number of factors that are especially important here. Factor number one is that increasingly, businesses are going to recognize that the role that their data plays in competition is on the ascending. It's getting more important. Now, this is something that Wikibon's been arguing for quite some time. In fact, we have said that the whole key to digital disruption and digital business is to acknowledge the difference between business and digital business is the role that data and data assets play in your business. So we have strong agreement there. But on top of that, Ginny Rametti made the observation that 80% of the data that could be accessed and put to work in business has not yet been made available to the new activities and new processes that are essential to changing the way customers are engaged, businesses operate, and overall change and disruption occurs. So her suggestion is that that 80%, that vast amount of data that could be applied that's not being tapped is embedded deep within the incumbents. And so the core argument from IBM is that the incumbent companies, not the digital natives, not the startups, but the incumbent companies are poised to have a significant role in disrupting how markets operate because of the value of their data that hasn't currently been put to work and made available to new types of work. That was the thesis that we heard this week and that's what we're going to talk about today. Are the incumbents really going to strike back? So Dave Vellante, let me start with you. You were at Think, you heard the same type of argument. What did you walk away with? So when I first heard the term incumbent disruptors, I was very skeptical and I still am, but I like the concept and I like it a lot. So let me explain why I like it and why I think there are some real challenges. If I'm a large incumbent global 2000, I'm not going to just roll over because the world is changing and software is eating my world. Rather, what I'm going to do is I'm going to use my considerable assets to compete and so that includes my customers, my employees, my ecosystem, the partnerships that I have there, et cetera. The reason why I'm skeptical is because incumbents aren't organized around their data assets. Their data assets are stovepipe. They're all over the place and the skills to leverage that data value, monetize that data, understand the contribution that data makes toward monetization. Those skills are limited. They're bespoke and they're very narrow. They're within lines of business or divisions. So there's a huge AI gap between the true digital business and an incumbent business. Now I don't think all is lost. I think a lot of strategies can work from M&A to transformation projects, joint ventures, spin-offs, IBM gave some examples. They put up Verizon and American Airlines. I don't see them yet as incumbent disruptors, but then there was another example of IBM Merisk doing some very interesting and disruptive things. Royal Bank of Canada doing some pretty interesting things. But in a joint venture form, Dave, to your point, they specifically set up a joint venture that would be organized around this data, didn't they? Yes, and that's really the point of trying to make. All is not lost. There are certain things that you can do, many things that you can do as an incumbent and it's really game on for the next wave of innovation. So we agree as a general principle that data is really important, David Floyer. And that's been our thesis for quite some time. But Ginny put something out there that said, Ginny Rametti put something out there. My good friend Ginny Rametti put something out there that 80% of the data that could be applied to disruption, better customer engagement, better operations, new markets is not being utilized. What do we think about that? Is that number real? If you look at the data inside any organization, there's a lot of structured data and that has better ability to move through an organization. Equally, there's a huge amount of unstructured data that goes in emails, it goes in voicemails, it goes in shared documents, it goes in diagrams, PowerPoints, et cetera, that also is data which is very much locked up in the way that Dave Vellante was talking about, locked up in a particular process or in a particular area. So is there a large amount of data that could be used inside an organization? Is it private? Is it theirs? Yes, there is. The question is, how do you tap that data? How do you organize around that data to release it? So this is kind of a chicken and egg kind of a problem. Neil, I'm going to turn to you. When we think about this chicken and egg problem, the question is, do we organize in anticipation of creating these assets? Do we establish new processes in anticipation of creating these data assets? Or do we create the data assets first and then re-institutionalize the work? And the reason why it's a chicken and egg kind of a problem is because it takes an enormous amount of leadership will to affect the way a business works before the assets in place. But it's unclear that we're going to get the asset that we want unless we affect the reorganization and institutionalization. Neil, is it going to be a chicken? Is it going to be the egg? Or is this one of the biggest problems that these guys are going to have? Well, I'm a little skeptical about this 80% number and I need some convincing before I would comment on it. I would rather see, and when David mentioned PowerPoint slides or email or that sort of thing, I would rather see that information curated by the application itself rather than dragged out as raw data and reinterpreted something. I think it's very dangerous. I think, you know, we saw that in data warehousing but it's easier because when you look at building data lakes, you throw all this stuff into a data lake and then after the fact somebody has to say, well, what is this data mean? So I find that, I find it that tough. Now, so Jim Cabela, a couple of weeks ago, Microsoft actually introduced a technology or a toolkit that could in fact be applied to move this kind of advanced processing for dragging value out of a PowerPoint or a Word document or something else close and proximate to the application. Is that, I mean, what Neil just suggested, I think is a very, very good point. Are we going to see these kinds of new technologies directly embedded within applications to help users narrowly but businesses more broadly lift that information out of these applications so that it can be freed up for other uses? I think, yeah, at some level, Peter, this is a topic called dark data. It's been discussed in the data management circles for a long time. The vast majority, I think 75 to 80% is the numbers that I've seen the research bear out is locked up in terms of it's not searchable, it's not easily discoverable. It's not mashup-able, I mean, I'm making up a word, but the term mashup hasn't been used in years, but I think it's a good one. What it's all about is if we want to make the most of our incumbents data, then we need to give the business, the business people, the tools to find the data where it is to mash it up into new forms and analytics and so forth in order to monetize it and sell it, make money off of it. So there are a wide range of data discovery and other tools that support really self-service combination and composition of composite data object. I don't know that, however, that the culture of monetizing existing data sets and pulling dark data into productized forms, I don't think that's taken root in any organization anywhere. I think that's just something that consultants talk about as something that, gee, should be done, but I don't think it's happening in the real world. I think you're probably correct about that, but I still think Neil Ray's a great point and I would expect, and I think we all believe that increasingly this is not going to come as a result of massive changes in adoption of new data science like practices everywhere, but in embedding of these technologies, machine learning algorithms, approaches to finding patterns with an application data in the applications themselves, which is exactly what Neil was saying. So I think that what we're going to see, and I wanted some validation from you guys about this, is a increasingly tools being used by application providers to reveal data that's in applications and not open source, independent tool chains that then expose facto get applied to all kinds of different data sources in an attempt for the organization to pull this stuff out. David Furrier, what do you think? I agree with you. I think there's a great opportunity for the IT industry in this area to put together solutions which can go and fit in on the basis of existing applications. There's a huge amount of potential, for example, of ERP systems to link in with IoT systems, for example, and provide a data across an organization. Rather than designing your own IoT system, I think people are going to buy in pre-made ones. They're going to put the devices in, the data's going to come in, and the AI work will be done as part of that, as part of implementing that. And right across the board, there is tremendous opportunity to improve the applications that currently exist or put in new versions of applications to address this question of data sharing across an organization. Yeah, I think that's going to be a big piece of what happens. And it also says, Neil Raden, something about whether or not, you know, enormous machine learning deities in the sky, some of which might start with the letter W, are going to be the best and only way to unlock this data. Is this going to be something that, we're suggesting now that it's something that's going to be increasingly distributed closer to applications, less invasive and disruptive to people, more invasive and disruptive to the applications and the systems that are in place. And what do you think, Neil? Is that a better way of thinking about this? Yeah, let me give you an example. Data science, the way it's been practiced, is a mess. You have one person who's trying to find the data, trying to understand the data, the infectious selection, designing experiments, doing runs and so forth, coming up with formulas and then putting them in the cluster with funny names so they can try to remember which one was which. And now what you have are a number of software companies with some brilliant ways of managing that process, really helping the data science create a work process and curating the data and so forth. But if you want to know something about this particular model, you don't have to go to the person and say, when did you do that model? And what exactly were you thinking about it? That information would be available right there in the work bench. And I think that's a good model for frankly everything. So let's- It's because we built into AI development pipeline toolkits. Yeah, that's a hot theme. Yeah, it's a very hot theme. But Jim, I don't think you think, but I'm going to test it. I don't think we're going to see AI pipeline toolkits be immediately or be accessed by your average end user who's putting together a contract so that that toolkit or so that data is automatically munged and ingested or ingested and munged by some AI pipeline. This is going to happen in an application. So the person's going to continue to do their work and then the tooling will or will not grab that information and then combine it with other things through the application itself into the pipeline. You got that right? Yeah, but I think this is all being, everything you described is being embedded in applications that are making calls to back end cloud services that have to have themselves built by data scientists and exposed through REST APIs. Steve, Peter, everything you're describing is coming to, is coming to applications fairly rapidly. Yeah, I think that's a good point, but I want to test it. But I want to test that. So Ralph Finos, you've been paying a lot of attention during reporting season to what some of the big guys are saying on some of their calls and in some of their public statements. One company in particular, Oracle, has been finessing a transformation, shall we say. What are they saying about how this is going as we think about their customer base, the transformation of their customer base and the degree to which applications are or are not playing a role in those transformations? Yeah, I think in their last earnings call a couple of days ago, the point that they were making around the decline in the... And again, this is Oracle. So in Oracle's last earnings call, yeah. Yeah, I'm sorry, yeah. And the decline in the revenue growth rate in the public cloud, the SaaS end of their business was a function really of a slowdown of the original acquisitions they made to kind of show up as being a transformative cloud vendor and that are basically beginning to run out of gas. And I think if you're looking at marketing applications and sales related applications and content type of applications, those are kind of hitting a natural high of growth. And I think what they were saying is that from a migration perspective on ERP that that's gonna take a while to get done. They were saying something like 10 or 15% of their customer base had just begun doing some sort of migration. And that's data around ERP and those kinds of applications. So it's a long slog ahead of them, but I'd rather be in their shoes I think for the long run than trying to kind of jazz up in the near term some kind of pseudo SaaS cloud growth based on acquisition and low lying fruit. Yeah, because they have a public cloud, right? I mean, at least they're in the game. Yeah, and they have to show they're in the game. Yeah, and specifically they're talking about their applications as clouds themselves. So they're just not saying, here's a set of resources that you can build to. They're saying, here's a set of SaaS based applications that you can build around. Right, and I think, go ahead Ralph, sorry. Yeah, yeah, and I think the notion there is the migration to their ERP and their systems of record applications that they're saying is this going to take a long time for people to do that migration because of complexity and process. So the last point, Dave Flante, did you have a point you want to make before I jump into a new thought here? I just compare and contrast IBM and Oracle, they have public clouds, they have SaaS, many others don't. I think there's a major point of differentiation. All right, so we've talked about whether or not this notion of data as a source of value is important, we agree it is. We still don't know whether or not 80% is the right number, but it is some large number that's currently not being utilized and applied to work differently than the data currently is, and that likely creates some significant opportunities for transformation. Do we ultimately think that the incumbents, again I mentioned the chicken and egg problem, do we ultimately think that the incumbents are, is this going to be a test of whether or not the incumbents are going to be around in 10 years, the degree to which they enact the types of transformation we thought about. Dave Vellante, you said you were skeptical, you heard the story, we've had the conversation, will incumbents who do this in fact be in a better position? Well, incumbents that do take action, absolutely will be in a better position, but I think that's the real question. I personally believe that every industry is going to get disrupted by digital, and I think a lot of companies are not prepared for this and are going to be in deep trouble. All right, so I have one more thought, because we're talking about industries overall. We have, there's so many elements we haven't gotten to, but there's one absolute thing I want to talk about, specifically the difference between B2C and B2B companies. Clearly the B2C industries have been disrupted, many of them, pretty significantly over the last few years. Not too long ago, I mean I have multiple, not necessarily good memories, of running the aisles of toys or us, sometime after 10 o'clock at night, right around December 24th, I can't do that anymore. It's not because my kids are grown, or I won't be able to do that soon anymore. So B2C industries seem to have been moved faster because the digital natives are able to take advantage of the fact that a lot of these B2C industries did not have direct and strong relationships with their customers. I would posit that a lot of the B2B industries are really where the action's going to take, and the right kind of way I would think about it, and David Foley, I'll turn to you first. The way I would think about it is that it's, in the B2C world, it's new markets and new ways of doing things, which is where the disruption's going to take place. So more of a substitution as opposed to a churn, but in the B2B markets, it's disrupting, getting greater efficiencies, greater automation, greater engagement with existing customers, as well as finding new businesses and opportunities. What do you think about that? I think the B2B market is much more stable. Relationships, business relationships, very, very important. They take a long time to change. But much of that isn't digital? A lot of that is not digital, I agree with that. However, I think that the underlying change that's happening is one of automation. B2B are struggling to put into place automation with robots, automation everywhere. What you see, for example, in Amazon is a dedication to automation, to making things more efficient. And I think that, to me, the biggest challenge is owning up to the fact that they have to change their automation, get themselves far more efficient. And if they don't succeed in doing that, then their ability to survive, or their likelihood of being taken over with a reverse takeover, it becomes higher and higher and higher. So how do you go about that level, huge increase in automation that is needed to survive, I think is the biggest question for B2B. And when we think about automation, David, we're not talking about the manufacturing arms, or only talking about manufacturing arms. We're talking about a lot of new software automation. David Vonte, Jim Cabellus, RPA is kind of a new thing. Dave, we saw some interesting things, I think, bring us up to speed quickly on what the community I think was talking about with RPA. Well, I tell you, there were a lot of people in financial services, which is IBM Stronghold, and they're using software robots to automate a lot of the backend stuff that humans are doing. That's a major, major use case. I would say 25 to 30% of the financial services organizations that I talked to had active RPA projects ongoing at the moment. I don't know, Jim, what are your thoughts? Yeah, I think backend automation is where B2B disruption is happening. As the organizations are able to automate, more of their backend, and digitize more of their backend functions, and accelerate them and improve the throughput of transactions, are those that will clean up. I think for the B2C space, it's the front end automation of the digitalization of the engagement channels. But RPA is essentially a key that's unlocking backend automation for everybody because it allows more of the front end business analysts, and those who are not traditional like BPM or business process re-engineering professionals, to begin to take standard administrative processes and begin to automate them from, as it were, the outside in a greater way. So I think RPA is a secret key for that. And I think we'll see some of the more disruptive organizations, businesses, take RPA and use it to essentially just reverse engineer, as it were, existing processes, but in an automated fashion, and drive the backend by AI. Yeah, I just love the term software robots. I just think that that's, I think that's so strongly evokes what's going to happen here. If I could add, I think there's a huge need to simplify that space. The other thing I witnessed that IBM think is it's still pretty complicated. It's still a heavy lift. There's a lot of big services component to this, which is probably why IBM loves it. But there's a massive market, I think, to simplify the adoption of it. I completely agree. We have to open the aperture as well. Again, the goal is not to train people new things, new data science, new automation stuff, but to provide tools and increasingly embed those tools into stuff that people are already using so that the disruption and the changes happen more as a consequence of continuing to do what the people do. All right, so let's hit the action item around, guys. It's been a great conversation. Again, we haven't talked about GDPR. We haven't talked about a wide array of different factors that are going to be an issue. I think this is something we're going to talk about. But on the narrow issue of can the disruptors strike back, Neil Raden, let's start with you. Neil Raden, action item. I've been saying since 1975 that I should be hanging around with a better class of people, but I do spend a lot of time in the insurance industry. And I have been getting a consensus that in the next five to 10 years, there will no longer be underwriters for claims adjustments. That business is ready for massive, massive change. And those are disruptors, largely. Jim Kobielus, action item. Action item, in terms of business disruption, is just not to imagine that you're, because you're the incumbent in the past era in some solution category that's declining, that that automatically guarantees you, that makes your data fit for seizing opportunities in the future. As we've learned from Blockbuster video, the fact that they had all this customer data that didn't give them any defenses against Netflix coming along and cleaning their clock and putting them out of business. So the next generation of disruptor will not have any legacy data to work from, and they'll be able to work miracles because they made a strategic bet on some front end digital channel that made all the difference. Ralph Finos, action item. Yeah, I think there's a notion here of siege mentality. And I think the incumbents are in, you know, with the castle walls and the disruptors are outside the castle walls. And sometimes the disruptors, you know, scale the walls, sometimes they don't. And that I think being inside the walls is a long run tougher thing to be at. Dave Vellante, action item. Yeah, I want to pick up on something Neil said. I think it's alluring for some of these industries like insurance and financial services and healthcare, even parts of government that really haven't been disrupted in a huge way yet to say, well, I'll wait and I'll wait and I'll see what happens. I think that's a huge mistake. I think you have to start immediately thinking about strategies, particularly around your data, as we talked about earlier, maybe it's M&A, maybe it's joint ventures, maybe it's spinning out new companies, but, you know, the time has passed where you should be acting. David Foyer, action item. I think that it's easier to focus on something that you can actually do. So my action item is that the focus of most B2B companies should be on looking at all of their processes and incrementally automating them. Or taking out the people costs, taking out the costs, other costs, automating as processes as much as possible. That, in my opinion, is the most likely path to being in a position that you can continue to be competitive. Without that focus, it's likely that you're going to be disrupted. All right, so the one thing I'll say about that, David, is when I think you say people costs, I think you say the administrative costs associated with people. And people doing things, automating jobs. All right, so we have been talking here on today's Wikibon Action Item about the question, will the incumbents be able to strike back? The argument we heard at IBM Think this past week, and this is the third week of March, was that data is an asset that can be applied to significantly disrupt industries, and that incumbents have a lot of data that hasn't been bought into play in the disruptive flow. And IBM's argument is that we're going to see a lot of incumbents start putting their data into play, more of their data assets into play, and that's going to have a significant impact ultimately on industry structure, customer engagement, the nature of the products and services that are available over the course of next decade. We agree, we generally agree. We might nitpick about whether it's 80%, whether it's 60%, but in general, the observation is an enormous amount of data that exists within large companies related to how they conduct business is siloed and locked away and is used once and is not made available, is dark and is not made available for derivative uses that could in fact lead to significant consequential improvements in how a business's transaction costs are ultimately distributed. Automation's going to be a big deal. David Foyer's mentioned this in the past. I'm also of the opinion that there's going to be a lot of new opportunities for revenue enhancement and products, I think that's going to be as big, but it's very clear that to start, it makes an enormous amount of sense to take a look at where your existing transaction costs are, where existing information asymmetries exist and see what you can do to unlock that data, make it available to other processes and start to do a better job of automating local and specific to those activities. And we generally ask our clients to take a look at what is your value proposition, what are the outcomes necessary for that value proposition, what activities are most important to creating those outcomes and then find those that by doing a better job of unlocking new data, you can better automate those activities. In general, our belief is that there's a significant difference between B2C and B2B businesses. Why? Because a lot of B2C businesses never really had that direct connection, therefore never really had as much of the market and customer data about what was going on, a lot of point of sale perhaps, but not a lot of other types of data. And then the disruptors stepped in and created direct relationships, gathered that data and were able to rapidly innovate products and services that served consumers differently. Where a lot of that new opportunity exists is in the B2B world. And here's where the real incumbents are going to start flexing their muscles over the course of the next decade as they find those opportunities to engage differently, to automate existing practices and activities, change their cost model and introduce new approaches to operating that are cloud-based, blockchain-based, data-based, based on data, and find new ways to utilize their people. If there's one big caution we have about this, it's this, ultimately, the tooling is not broadly mature, the people necessary to build a lot of these tools are increasingly moving into the traditional disruptors, the legacy disruptors if we will, AWS, Netflix, Microsoft, companies more along those lines. That talent is very dear still in the industry and is going to require an enormous effort to bring those new types of technologies that can in fact liberate some of this data. We look to things like RPA, robot process automation, we look at the big application providers to increasingly imbue their products and services with some of these new technologies, and ultimately, paradoxically, perhaps, we look for the incumbent disruptors to find ways to disrupt without disrupting their own employees and customers. So embedding more of these new technologies in an ethical way directly into the systems and applications that serve people so that the people have faced minimal changes to learning new tricks because the systems themselves have gotten much more automated and much more, are able to learn and evolve and adjust much more rapidly in a way that still corresponds to the way people do work. So our auction item, any company in the B2B space that is waiting for data to emerge as an asset in their business so that they can then do all the institutional, the re-institutionalizing of work and reorganizing of work and new types of investment is not going to be in business in 10 years or is going to have a very tough time with it. The big challenge for the board and the CIO, and it's not successfully been done in the past, at least not too often, is to start the process today without necessarily having access to the data of starting to think about how the work's going to change, think about the way their organization is going to have to be set up. This is not business process re-engineering, this is organizing around future value of data, the options that data can create and employ that approach to start doing local automation, serve customers and change way partnerships work and ultimately plan out for an extended period of time how their digital business is going to evolve. Once again, I want to thank David Foyer here in the studio with me, Neil Raiden, Dave Vellante, Ralph Finoz, Jim Cabela's, remote, thanks very much guys for all of our clients. Once again, this has been a Wikibon Action Item. We'll talk to you again. Thanks for watching.