 Live from San Francisco, California, it's theCUBE. Covering the IBM Chief Data Officer Summit, brought to you by IBM. Hello everyone, welcome to historic Fisherman's Wharf in San Francisco. We're covering the IBM Chief Data Officer event, hashtag IBM CDO. This is theCUBE's, I think eighth time covering this event. It's the 10th year anniversary of the IBM CDO event. And it's a little different format today. We're here at day one. It's like a half day, they start at noon, and then the keynotes. We're starting a little bit early. We're going to go all day today. My name is Dave Vellante. Steve Eleuk is here. He's the CUBE alum and vice president of deep learning and the global chief data officer at IBM. And Tim Humphrey, the VP and chief, at the chief data office at IBM. Gents, welcome to theCUBE. Welcome, glad to be here. So a couple years ago, Ginny Rametti at a big conference talked about incumbent disruptors. And the whole notion was that you've got established businesses that need to transform into data businesses. Well, that struck me that, well, if IBM's going to sell that to its customers, it has to go through its own transformation, Steve. So let's start there. What is IBM doing to transform into a data company? Well, I've been at IBM for two years now. And luckily I'm Ben Infinity from a lot of that transformation that's taken place over the past three or four years. So internally, getting our data in good order, understanding it, going through various different foundation stones, building those building blocks so that we can gather new insights and traverse through the cognitive journey. One of the nice things, though, is that we have such a wide diverse set of data within the company. So for different types of enterprise use cases that have benefits from AI, we have a lot of data assets that we can pull from. Now, keeping those data assets in good order is a challenging task in itself. And I'm able to pull from a lot of different tools that IBM's building for our customers. I get to use them internally, look at them, evaluate them, give them real practitioners point of view to ultimately get insight for our internal business practices, but also for our customers in turn. Okay, so when you think about a data business, they've got data at the core. I mean, draw the simple conceptual picture and you got people around it, maybe you got processes around it. IBM, 100 plus year old company, you've got different things at the core. It's products, it's people, it's business process. So maybe you could talk to them about how you guys have gone about kind of putting data at the center of the universe. Is that the right way to think about it? It is the right way to think about it. And I like how you were describing it because when you think about IBM, we've been around over 100 years and we do business in roughly over 170 countries. And we have businesses that span hardware, software, services, financing. And along the way, we've also acquired and divested a lot of companies and a lot of businesses. So what that leaves you with is a very fragmented data landscape, right? You know, to support regulations in this country, tax rules in another country, and having all these different types of businesses, some you inherit, some are born from within your company, it just leaves a lot of data silos. And as we see transformation as being so important and data is at the heart of that transformation, it was important for us to really be able to organize ourselves such that access to data is not a problem, such that being able to combine data across disciplines from finance to HR to sales to marketing to procurement, that was the big challenge, right? And to do this in a way that really unlocks the value of the data, right? It's very easy to use somebody like one of my good smart friends here, Stephen Elliott, to develop models within a domain. But when you talk about cross-functional, complex data coming together to enable models, that's like the holy grail of transformation. Then we can deliver real business value. Then you're not waiting to make decisions. Then you can actually be ahead of trends. And so that's what we've been trying to do. And the thought and the journey that we have been on is build a enterprise data platform. So take the concept of a data lake, bring in all your data sources into one place, but on top of that, make it more than just a data lake. Bring the services and capabilities that allow you to deliver insights from data together with the data. So we have a data platform. And our cognitive enterprise data platform sort of enables that transformation and makes people like my good friend here much more productive and much more valuable to the business. This just sounds like just a massive challenge. It's not just a technology challenge, obviously. You've got cultural. I mean, people, this is my data. And I'm referring to him, you're talking like you're largely through this process, right? So first of all, can you talk about some? I will say this, this is a journey. You're never done, right? And one of the reasons why it is a journey is if you're going to have a successful business, your business is going to keep transforming. Things are going to keep changing. And even in our landscape today, regulations are going to come. So there's always going to be some type of challenge. So I like to say we're in a journey. We're not finished. We're well down the path. And we've learned a lot. And one of the things we have learned, you hit on it is culture, right? It's a little hard to say, okay, I'm opening things up. I don't own the data. The company owns the data. There is that sort of cultural change that has to go along with this. And there are technology challenges. I mean, when I first started in this business, you know, AI was a hot concept. But you needed like massive supercomputers to actually make them work. Today, you now see their sort of rebirth. You know, everybody talks about the AI winter and now it's like the AI spring. So how are you guys applying machine intelligence to make IBM a better business? Well, ultimately, you know, the technology has really, you know, basically transitioned us from the dark ages, you know, forward. Previously in the supercomputer mentality, it didn't, you know, fit well for a lot of AI tasks. Now with GPUs and accelerators and, you know, FPGAs and things like that, we're definitely able, along with the data and the curated data that we need, to just fast track. You know, the practitioners would spend an amazing amount of time gathering crowdsourcing data, getting in good order. And then the computational challenges were tough. Now IBM came to the market with a very interesting computer. The Power 8 and Power 9 architecture has NVLink, which is a proprietary NVIDIA interconnect directly to the CPU. So we can feed GPUs a lot quicker for certain types of tasks. And for certain types of tasks, that could mean, you know, you get to market quicker or we get insights for, you know, enterprise problems quicker. All right, so technology is a big deal, but it doesn't just center around GPUs. If you're slow to get access to the data, then that's a big problem. So the governance and regulatory aspects are just as important in addition to that, security, privacy, et cetera, also important. The quality of the data, where the data is, you know, so it's an end-to-end system. And if there's any sort of impedance on any of it, it slows down the entire process. And then you have very expensive practitioners who are trying to do their job that are waiting on data or waiting on results. So it's really an end-to-end process. Okay, so let's assume for a second that technology box is checked. And again, as you're saying, Tim, it's a journey and technology is going to continue to evolve, but we're at a point in technology now where this stuff actually can work. But what about data quality? What about compliance and governance? How are you dealing with the natural data quality problem? Because I'm a P&L manager, I'm saying, well, we're making data decisions, but if I don't like the decision, I'm going to attack the quality of the data. So who adjudicates all that? And how have you resolved those challenges? Well, you know, I like to think of, I'm an engineer by studying. I just like to think of simple formula. Garbage in, garbage out. It applies to everything and it definitely applies to data. Your insights, the models, anything that you build is only going to be as good as the data foundation you have. So one of the key things that we've embarked on a journey on is how do we standardize all aspects of data across the company? Now, you might say, hey, that's not a hard challenge, but it's really easy to do standards in a silo. For this organization, this is how we're going to call terms like geography and this is how we'll represent these other terms. But when you do that across functions, it becomes conflict, right? Because people want to do it their own way. So we're on the path of standardizing data across the enterprise. That's going to allow us to have good definitions. And then, as you mentioned earlier, we are trying to use AI to be able to improve our data quality. One of the most important things about data is the metadata, the data that describes the data. And we're trying to use AI to enhance our metadata. I'd love for Stephen to talk a little bit about this because this is sort of his brain child. But it's fascinating to me that we can be on AI transformation. Data can be at the heart of it. And we can use AI to help improve the quality of our data. It's fascinating. Yeah, so the metadata problem is interesting because you're talking about Data Lake before. In this day and age, you're talking schema-less, throw it into a Data Lake and figure out because you have to be agile for your business. So you can't do that with just human categorization. And you know, it's got to take out years. For a company the size of IBM, the market would shift so fast, right? So how do you deal with that problem? That's exactly it. We're not patient enough to do the normative kind of mentality where you just throw a whole bunch of bodies at it. We're definitely moving from that non-extensible man count, full-time employee type situation to looking for ways that we can utilize automation. So around the metadata, quality and understanding of that data was incredibly problematic. And we were just hiring people left, right, and center and then it's a really tough job that they have. Dealing with so many different business silos, et cetera. So looking for ways that we could automate that process, we finally found a way to do it. So there's a lot of curated data. Now we're looking at data quality in addition to looking at regulatory and governance issues, in addition to automating the labeling of business metadata. And the business metadata is the taxonomy that everything is linked together. We understand it under the same normative umbrella. So then when one of the enterprise use cases says, hey, we're looking for additional data assets. Oh, it's in the cloud here, or it's in a private instance here. But we know it's there and you can grab it, right? So we're definitely at probably the tail end of that curve now. And it started off really hard, but it's getting easier. So that's- We got to leave it there. It's an awesome discussion. I hope we can pick it up in the future. Maybe we have more metadata than data. Metadata is becoming more and more valuable. But thank you so much for sharing a little bit about IBM's transformation. It was great having you guys on. All right, keep it right there, everybody. We'll be back with our next guest right after this short break. We'll be watching theCUBE at IBM CDO in San Francisco. Right back. All right, long and clear. Thank you, sir. All right, thank you guys. Appreciate it. I wish we had more time. No worries. So.