 Live from New York, it's theCUBE, covering machine learning everywhere. Build your ladder to AI, brought to you by IBM. Welcome back to New York City. As we continue here at IBM's machine learning everywhere, build your ladder to AI, bringing it to you here on theCUBE, of course, the flagship broadcast of SiliconANGLE Media. And Dave Vellante joins me here. Dave, good morning once again to you, sir. And we're joined by Madhu Kochar, who is the Vice President of Analytics Development and Client Success at IBM. I like that, Client Success. Good to see you this morning. Thanks for joining us. Yeah, thank you. Yeah, so let's bring up a four letter slash 10 letter word, governance, that some people just cringe right away, but that's very much in your wheelhouse. Let's talk about that in terms of what you're having to be aware of today with data and this awesome, these great possibilities, right? But also on the other side, you gotta be careful. And I know there's some clouds over in Europe as well, but let's just talk about your perspective on governance and how it's important to get it all under one umbrella. Yeah, so I lead product development for IBM Analytics, governance and integration. And like you said, governance has, every time you talk that people cringe and you think it's a dirty word, but it's not anymore, right? Especially when you wanna tie your AI ladder story, right? There is no AI without information architecture, no AI without IA. And if you think about IA, what does that really mean? It means the foundation of that is data and analytics. Now let's look deeper. What does that really mean? What is data analytics? Data is coming at us like from everywhere, right? And there's records, the data shows there's about 2.5 billion bytes of data getting generated every single day. Raw data from everywhere. How are we gonna make sense out of it, right? And from that perspective, it is just so important that you understand this type of data. What is the type of data? What is the classification of this means in a business? When you are running your business, there's a lot of cryptic fields out there. What is the business terms assigned to it? And what is the lineage of it? Where did it come from? If you do have to do any analytics, if data scientists have to do any analytics on it, they need to understand where did it actually originated from? Can I even trust this data? Trust is really, really important here, right? And is the data clean? What is the quality of this data? The data is coming at us all rough formats from very IoT sensors and such. What is the quality of this data? To me, that is the real definition of governance, right? It's not just about what we used to think about compliance, yes. But it's all about being appropriate with all the data you have coming in. Exactly, I call it governance 2.0 or governance for insights because that's what it needs to be all about, right? Compliance, yes indeed with GDPR and other things coming at us is important. But I think the most critical is that we have to change the term of governance into like, this is that foundation for your AI ladder that is going to help us really drive the right insights. That's my perspective. I want to double click on that because you're right. I mean, it is kind of governance 2.0. It used to be Enron forced a lot of governance and the federal rules of civil procedure forced a lot of sort of, even some artificial governance. And then I think organizations, especially public companies and large organizations said, you know what? We can't just do this as a band aid every time. Now GDPR, many companies are not ready for GDPR. We know that. Having said that, because it is, we went through governance 1.0, you know, many companies are not panicked. I mean, they're kind of panicking because it's May is coming. But they've been through this before. Do you agree with that premise that they've got at least the skill sets and the professionals too, if they focus, they can get there pretty quickly. Yeah, no, I agree with that. But I think our technology and tools needs to change big time here, right? Because regulations are coming at us from all different angles. Everybody's looking to cut cost, right? You're not gonna hire more people to sit there and classify the data and say, hey, is this data already for GDPR or for Basil or for Poppy like in South Africa? I mean, there's just tons of things, right? So I do think that technology needs to change. And that's why, you know, in our governance portfolio in IBM Information Server, we have infused machine learning in it, right? Where it's automatically, you have machine learning algorithms and models understanding your data, classifying the data, you know? You don't need humans to sit there and assign terms, the business terms to it. We have compliance built into our, it's running actually on machine learning. You can feed in taxonomy for GDPR. It would automatically tag your data in your catalog and say, hey, this is personal data. This is sensitive data or this data is needed for these type of compliance. I mean, that's the aspect which I think we need to go focus on. So the companies, to your point, don't shrug every time they hear regulations that it's kind of built in in the DNA. But technologies have to change. The tools have to change. So to me, that's good news. If you're saying the technology and the tools is the gap, you know, we always talk about people process and technology and the bromide is, but it's true, people and process are the really hard pieces of it. Technology comes and goes and people kind of generally get used to that. So I'm inferring from your comments that you feel as though governance, there's a value component of governance now. It's not just a negative risk avoidance. It can be a contributor to value. You mentioned the example of classification, which I presume is auto-classification at the point of use or creation, which has been a real nagging problem for decades, especially after FRCP. Federal was a civil procedure where it was like, ah, we can't figure this out. We'll do email archiving. You can't do this manually. There's just too much data to your point. So I wonder if you could talk a little bit about governance and its contribution to value. Yeah, so this is a good question. I was just recently visiting some large banks, right? And normally, the governance and compliance has always been an IT job, right? And they figure out a bunch of products. You can download open source and do other things to quickly deliver data or insights to their business groups, right? And for business to further figure out new business models and such, right? So recently, what has happened is by doing machine learning into governance, you're making your IT guys the heroes because now they can deliver stuff very quickly and the business guys are starting to get those insights and their thoughts and data is changing. And recently I was talking with these banks where they're like, can you come and talk to our CFOs because I think the policies, the cultural change you referred to them is maybe the data needs to be owned by businesses, no longer an IT thing, right? So governance, I feel like, governance and integration, I feel like it's a glue which is helping us drive that cultural change in the organizations, bringing IT and the business groups together to further drive the insights. So for years, we've been talking about information as a liability or an asset. And for decades, it was really viewed as a liability, get rid of it if you can, if you have to keep it for seven years, and then get rid of it. That started to change with the big data movement but there was still sort of, it was hard, right? But what I'm hearing now is increasingly, especially if the business is sort of owning the data, it's becoming viewed as an asset. You got to manage the liabilities, we got that. But now, how do we use it to drive business value? Yeah, yeah, no, exactly. And that's where I think our focus in IBM analytics with machine learning and automation and truly driving that insights out of the data. I mean, people, we've been saying data is a natural resource, it's our bloodline, it's this and that, it truly is. And talking to the large enterprises, everybody is in their mode of digital transformation or transforming, right? We in IBM are doing the same things, right? We're eating our own, we're drinking our own champagne and, you know, yeah, yeah, exactly. No dogs for a year. No dogs for a year. Drinking our own champagne. And truly we are seeing transformation in how we are running our own business as well. Yeah, they're always surprises. There are always some accidents kind of waiting to happen, but in terms of the IoT, I've gotten these millions right of sensors, feeding data in and what from a governance perspective is maybe a concern about an unexpected source or an unexpected problem or something where you have great capabilities, but with those capabilities might come a surprise or two in terms of protecting data and a machine might provide perhaps a little more insight than you might have expected. So I mean, just looking down the road from your perspective, is there anything along those lines that you're putting up flags for just to keep an eye on and see what new inputs might create new problems for you? Yeah, no, for sure. I mean, we're always looking at how do we further do innovation? How do we disrupt ourselves and make sure that data doesn't become our enemy, right? I mean, as we are talking about AI, people are starting to ask a lot of questions about ethics and other things too, right? So very critical. So obviously when you focus on governance, the point of that is let's take the manual stuff out, make it much faster, but part of the governance is that we're protecting you, right? That's part of that security and understanding of the data is all about that you don't end up in jail, right? That's the real focus in terms of our technology and tools that we are looking at. So maybe help our audience a little bit. So I described at our open AI as sort of the umbrella and machine learning is the math and the algorithms that you apply to train systems, to do things maybe better than humans can do and then there's deep learning, which is neural nets and so forth. Am I understanding that you've essentially, first of all, is that sort of, I know it's rudimentary, but is it reasonable? And then it sounds like you've infused ML into your software. Yes. And so I wonder if you could comment on that and then describe from the client standpoint what skills they need to take advantage of that, if any. Oh yeah, no. So embedding ML into a software, like a packaged software, which gets delivered to a client, people don't understand actually how powerful that is because your data, your catalog is learning, is continuously learning from the system itself, from the data itself, right? And that's very exciting. The value to the clients really is, it cuts down their cost big time. Let me give you an example. In a large organization today, for example, if they have like maybe 22,000 some terms, normally it would take them close to six months for one application with a team of 20 to sit there and assign the terms, the right business glossary for their business to their data. So by now doing machine learning in our software, we can do this in days, even even hours. Obviously depending on what's the quantity of the data in the organization, that's the value. So the value to the clients is cutting down that, they can take those books and go focus on some, bigger value add applications and others and take advantage of that data. The other huge value that I see is as the business changes, the machine can help you adapt. I mean, taxonomies are like cement data classification. And that's why we can't move the business forward because we have this classification. Can your machines adapt in real time? And can they change at the speed of my business, is my question. Right, right, no it is, right? And clients are not able to move on their transformation journey because they don't have data classified done right. And you can put humans through it. You're going to need the technology. You're going to need the machine learning algorithms and the AI built into your software to get that. And that will lead to really success of every client. So I'm going to do, I have a broader question. One of the good things about things like GDPR, is it forces, it puts a deadline on there and we all, give me a deadline and I'll hit it. So it sort of forces action. And that's good. We've talked about the value that you can bring to an organization from a data perspective, but there's this whole non-governance component of data orientation. How do you see that going? Can the governance initiatives catalyze sort of what I would call, people talk about a data-driven organization. Most companies, they may say they are data driven but they're really not foundational. Can governance initiatives catalyze that transformation to a data-driven organization and if so, how? Yeah, no, absolutely. So the example I was sharing earlier with talking to some of the large financial institutes where the business guys outside of IT are talking about how important it is for them to get the data, literally real time and self-service. They don't want to be dependent on either opening a work ticket for somebody in IT to produce data for them and God forbid, if somebody's out on vacation, they can never get that. We don't live in that world anymore. It's online, it's real time. It's all self-service type of aspects which the business, the data scientists, building new analytic models are looking for that. So for that data is the key, key core foundation and governance, the way I explained it earlier is not just about compliance, that is going to lead to that transformation for every client, it's the core. They will not be successful without that. And the attributes are changing. Not only is it self-service, it's pervasive, it's embedded, it's aware, it's anticipatory, am I overstating that? I mean, is the data going to find me? Yeah, that's a good way to put it. So no, you are out there, I think you got it. This is absolutely the right focus and the companies and the enterprises who understand this and use the right technology to fix it, they'll win. And the other part of that, I don't know if it is, contextual, I mean, so also make it relevant to me and help me understand it's relevance because maybe as a human, you maybe just don't have that kind of prism. But does that happen as well too? It can put up these white flag and say, yeah, this is what you need. Yeah, absolutely. So like the focus we have on our natural language processing, for example, right, if you're looking for something, you don't have to always know what your SQL going to be for a query to do it. You just type in, hey, I'm looking for some customer retention data, you know, and it will go out and figure it out and say, hey, are you going to looking for churn analysis or are you looking to do some more promotions? It will learn, you know, and that's where this whole aspect of machine learning and natural language processing is going to give you that contextual aspect of it because that's how the self-service models will work. Right, what about skills? John asked me at the open about skill sets and I want to ask a general question but then specifically about governance. I would make the assertion that most employees don't have the multidimensional digital skills and domain expertise skills today. Some companies they do, the big data companies. But in governance, because it's 2.0, do you feel like the skills are largely there to take advantage of the innovations that IBM is coming out with? I think I generally, in my personal opinion, is the way the technology is moving, the way we are getting driven by a lot of disruptions which are happening around us, I think we don't have the right skills out there, right? We all have to retool. I'm sure all of us in our career have done this all the time, you know? So to me, I don't think we have it. So building the right tools, the right technologies and enabling the resources that the teams out there to retool themselves so they can actually focus on innovation in their own enterprises is going to be critical. And that's why I really think more burn I can take off from the IT groups, more we can make them smarter and have them do their work faster. It will help give that time to go see, hey, what's their next big disruption in their organization? Is it fair to say that traditionally, governance has been a very people-intensive activity? Will governance, you know, in the next, let's say decade, become essentially automated? That's the hope, that's my desire. And with the product which is my job and I'm actually really proud of what we have done thus far and where we are heading. So next time when we meet, we will be talking maybe governance 3.0, I don't know. Yeah, that's the thing, right? I mean, I think you hit it on the nail that this is, we got to take a lot of human-intensive stuff out of these other products and more automation we can do, more smarts we can build in. I coined this term like, hey, we got to build smarter metadata, right? Data needs to, metadata is all about data of your data, right? That needs to become smarter. Think about having a universe where you don't have to sit there and connect the dots and say, I want to move from here to there. The system already knows it. They understand your behaviors. They know what your applications is going to do and kind of automatically does it for you. No more science fake, I think it can make it happen. You think we'll ever have more metadata than data? I wonder. Actually somebody did ask me that question, will we be figuring out here, we're building data lakes, would we have to, what do we do about metadata? No, I think we will not have that problem for a while. We'll make it smarter. It's like working within your workforce and you're telling people, you're a treasure hunter. And we're going to give you a better map. So governance is your better map. Hey, I like that, maybe we'll use it next time. But it's true, it's like, are you saying governance is your friend here? And we're going to fine tune your search. We're going to make you a more efficient employee. You're going to make you a smarter person and you're going to be able to contribute in a much better way. But it's almost enforced. But let it be your friend, not your fellow. Yeah, be your differentiator, right? But my takeaway is it's fundamental, it's embedded. You're doing this now with less thinking. Security's got to get to the same play. For your security, it slows me down. But now people are like, hey, help me. The same dynamic is true here. Embedded governance in my business. Not a bolt-on, not an afterthought. It's fundamental and foundational to my organization. Yeah, absolutely. Well, dude, thank you for the time. We mentioned on the outside by the interview that if you want to say hi to your kids, that's your camera right there. Do you want to say hi to your kids real quick? Yeah. Hi, Mohit, Kipa, I love you so much. There you go. All right. Thank you. So they know where mom is, New York City. IBM's machine learning everywhere. Build your ladder to AI. Thank you for joining us. Thank you. Thank you. Back with more here from New York in just a bit. You're watching theCUBE.