>> Live from Boston, Massachusetts, it's theCube, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to theCube's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We are here with Joseph Selle, he is the Cognitive Transformation Lead at IBM. Thanks so much for joining us, Joe. >> Hi, Rebecca, thank you. Hi, Dave. >> Good to see you, Joe. >> You, too. >> So, your job is to help drive the internal transformation of IBM. Tell our viewers what that means and then talk about your approach. >> Right, it a very exciting, frankly, it's one of the best jobs I've ever had personally. It's wonderful. We're transforming the company from the inside out. We're engaging with all of the functional areas within IBM's operations, and we're challenging those functional teams to breakdown their business process and reinvent it using some new tooling. And in this case, it's cognitive approaches to data analysis, and to crowd sourcing information, and systems that learn. We've talked a lot about at this conference, machine learning and deep learning. We're providing all of these tools to these functional teams so they can go reinvent HR and procurement, and even our M&A process, everything is fair game. So, it's very exciting and it really allows us to reinvent IBM. >> So, reinventing all of these individual functions, I mean, where to do you start? How do you begin to build the blueprint? >> Well, in our case, where we started was we had to get the whole company thinking about a large-scale enterprise, cultural transformation. We have a company of 300-some odd thousand people, employees, speaking all languages, all over the globe. So, how do you move that mass? So, we had cognitive jam, that's basically a technology enabled brainstorm session that spreads across the entire globe. And, by engaging about 300,000 IBM'ers, we were able to call and bring together all kinds of very disruptive, interesting ideas to remake all these business processes. We culled those ideas, and through some prioritization, almost a shark tank-like process, we ended up with a few that were really worthy, we felt, of investment. We've put money in, and our cognitive reinvention was born. Just like that. >> That's a lot of brain power. (laughs) >> Well, that's why it's wonderful to be at IBM, 'cause we have hundreds of thousands of brainy people working for us. >> You have talked about, when he was a controller during the Gerstner transformation, I don't know were you there back then? >> Yes, I was. >> Okay, so you guys were young pups back then, still young pups, I guess. But, he talked about, as the controller, he was an unhappy customer because he didn't have the data. So, can you talk about, sort of, what's different today? I mean, it's a lot different, obviously, the state of the industry, the technology, the amount of the data, et cetera. But, maybe talk about data as the starting point and how that was different from, maybe, the Gerstner transformation. >> The early days. >> Which was epic, by the way. You know, took IBM to new levels and be part of what the company is today. >> And this story that I'm going to tell you, is generally applicable to most any company that's global in nature. The data are not visible and they're not easy to see and discern any value from in the early stages of your transformation. So, when Jim was controller, he had data that was one, hard to get, and two, he had no tools to organize it except for, maybe, some smart people with Excel and, whatever it was back then, LotusPro, or something, I can't remember the name of that. (laughter) >> Something that ran on OS/2. >> There was no tooling, no approach. And, the whole idea of big data was not even around at that point. Because the data was organized and disorganized in little towers and databases all around, but there wasn't a flood of data. So, what's different between those days and this time period that we're in is, you can see data now and data are everywhere. And they're coming at us in high, high volumes and at high speeds. If you think about The Weather Company, one of the acquisitions we made two years ago, that is a stream of huge, big data, coming at us very fast. You can think about The Weather Company as a giant internet of things, device, which is pulling data from the sky and from people interacting with the environment, and bringing that all together. And now, what can we do with that data? Well, we can use it to help predict when we're going to have a supply chain disruption, or, I mean in an almost obvious sense, or we can use it when we're trying to respond to some sort of operational disturbance. If we're looking at where we can reroute things, or if we're trying to anticipate some sort of blockage on our supply chain, incoming supply chain, or outgoing supply chain of products. Very important, and we just see much more now then Jim ever could when he was a controller. >> In the scope of your data initiative, is everything, I mean, he's mentioned supply chain, you got customer data? >> It is, it is. But, I'll say that, you know, if a company's going to embark down this path, you don't want to try to boil the ocean at the start. You want to try to go after some selective business challenges, that are persistent challenges that you wish you had a way to solve because a lot of value's at play. So, you go in there and you solve a few problems. You deal with a data integrity and access problem, on a, sort of a, confined basis. And you do this, maybe, several times across different parts of your company. Then, once you've done that four or five times, or some small number of times, you begin to learn how to handle the problem more generally, and you can distill approaches and tools that can then be applied broadly. And where we are in our evolution, is that Inderpal and Jim, and the internal workings of IBM, were building a cognitive enterprise data platform. So, we're taking all of these point solutions that I just referred to, bringing them together onto a platform, and applying some common tooling to all of these common types of problems around data organization, and governance, and meta-data tagging, and all this geeky stuff that you have to be able to do if you're going to make any value. You know, if you're going to make an important, valuable business decision, based on a stream of data. >> So, where has it had tangible, measurable, business impact, this sort of cognitive initiative? >> Well, a couple of the areas where we're most mature, one would be in supply chain and procurement. We've been able to take jobs that, frankly, involve a lot of churning analysis, and be able to say to a procurement specialist, okay, what used to take you six hours, or an hour, or what ever the task was, we can shrink that down using a cognitive tool, down to just a few minutes. So, procurement, we've been able to get staffing efficiencies, and we've been able, even more importantly, to make sure that we're buying things at the best possible price. Because those same analysts want to know what's happening in the market, where's the market sentiment going? Is this market tightening or loosening? Is it a buyer or a seller market? If we're trolling the web, bringing back information on the micro-movements of all the regional markets in various electronics commodities, we know an aggregate, whether we should be hard bargainers or easy bargainers, essentially. So, that's procurement. But, you could talk about human resources, where the Watson tool can recommend a game plan for how you would manage the career of a person. You don't want to lose your star people. And it's wonderful that deep, subject matter experts in HR know how to anticipate what you're thinking, and those are the people you want in charge of HR. But, there's a lot of other people who aren't, maybe, as good as that one person at HR, now the system can help you by giving you a playbook, making you a better HR manager. So, that's HR, but I got one more that's really exciting that I'm working on right now in the area of M&A. So, IBM and any large company that has multiple offerings and geographies is involved in M&A. We're using cognition and big data to speed up our M&A process. Now, we have a small team of M&A, so we're not going to make millions of dollars of staffing efficiencies, but, if we can capture a company, if we can be the first one to make an offer on a company, rather than the third one, then we're going to get the best company. And if you can bring the best company in, like The Weather Company as an example in that space, or like any other type of data-mining company or something, you want the best company. And if you can use cognition to enhance your process to move very quickly, that's going to really help you. >> So, this is a huge transformation of the business model, but then you've also talked about the cultural transformation of IBM. How would you describe this new IBM, going through this transformation? How would you describe the culture and collaboration? >> So, luckily, we're pretty far along in the transformation and we're at a stage where we actually have a data platform that's been deployed internally. And, people know about the potential of cognition to redefine and remake their business processing, create all this value. So, now we're getting people to come on to the platform as citizen analysts, if you want to call them that, they're not operations PhD's, they're not necessarily data scientists, they're regular business analysts. They're coming onto the platform and they're finding data and they're finding tools to manipulate that data. They're coming in on a self-service model and being able to gain insights to bring back into their business decisions without the CIO office being involved. >> So that's a workbench on the Cloud, essentially, is that right? >> Yes, that it a good way to put it, yep. >> Workbench, we out of trademark that. (laughs) >> Let's do that. >> Good descriptor, I think. >> Well, Joe, thanks so much for joining us, it's been a pleasure talking to you. >> My pleasure, thank you. >> Thanks, thanks a lot. >> I'm Rebecca Knight, for Dave Vellante, we will have more from IBM CDO Summit just after this.