>> Announcer: Live from New York, it's theCUBE covering Machine Learning Everywhere, Build Your Ladder To AI, brought to you by IBM. (techy music playing) >> 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 rights to the broadcast of SiliconANGLE Media and Dave Vellante joins me here. Dave, good morning once again to you, sir. >> Hey, John, good to see you. >> 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 / ten letter word, governance, that some people just cringe, right, 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 all of a sudden these great possibilities, right, but also on the other side, you've got to 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, right, 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 want to 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 from everywhere, right, and there's records... The data shows there's about 2.5 quintillion bytes of data getting generated every single day, raw data from everywhere. How are we going to 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's the classification of this means in a business. You know, 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's 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 raw formats from 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, that's-- >> John: Like rolling a rag. >> Right, right. >> 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 it's 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, you know, Enron forced a lot of, you know, governance and the Federal Rules of Civil Procedure forced a lot of sort of even some artificial governance, and then I think organization, especially public companies and large organizations said, "You know what, we can't just do "this as a band-aid every time." You know, now GDPR, many companies are not ready for GDPR, we know that. Having said that, because it is, went through governance 1.0, many companies are not panicked. I mean, they're kind of panicking because May is coming, (laughs) but they've been through this before. >> Madhu: Mm-hm. >> Do you agree with that premise, that they've got at least the skillsets and the professionals to, 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 costs, right? >> Dave: Right. >> You're not going to hire more people to sit there and classify the data and say, "Hey, is this data ready for GDPR," or for Basel or for POPI, like in South Africa. I mean, there's just >> Dave: Yeah. >> Tons of things, right, so I do think the 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, >> Dave: Hm. >> 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," and that's the aspect which I think we need to go focus on >> Dave: Mm-hm. >> So the companies, to your point, don't shrug every time they hear regulations, that it's kind of built in-- >> Right. >> 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 the bromide is, but it's true, people and process are the really-- >> Madhu: Mm-hm. >> Hard pieces of it. >> Madhu: Mm-hm. >> Technology comes and goes >> Madhu: Mm-hm. >> 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 >> Yeah, yeah. >> 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 >> Madhu: Yes. >> At the point of use or creation-- >> Madhu: Yes. >> Which has been a real nagging problem for decades, especially after FRCP, Federal Rules of Civil Procedure, where it was like, "Ugh, we can't figure "this out, we'll do email archiving." >> Madhu: Mm-hm. >> You can't do this manually, it's just too much data-- >> Yeah. >> To your point, so I wonder if you could talk a little bit about governance and its contribution to value. >> Yeah, so this is good question. I was just recently visiting some large banks, right, >> Dave: Mm-hm. >> And normally, the governance and compliance has always been an IT job, right? >> Dave: Right. >> And they figure out bunch of products, you know, you can download opensource 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 on data is changing, you know, 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 then, maybe the data needs to be owned by businesses. >> Dave: Hm. >> No longer an IT thing, right? So, governance I feel like, you know, governance and integration I feel like is 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. You have to keep it for seven years, then get rid of it, you know. That started to change, you know, with the big data movement, >> Madhu: Yeah. >> But there was still sort of... It was hard, right, but what I'm hearing now is increasingly, especially of the businesses sort of owning the data, it's becoming viewed as an asset. >> Madhu: Yes. >> You've 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, you know, people... We've been saying data is a natural resource. >> Dave: Mm-hm. >> It's our bloodline, it's this and that. It truly is, you know, 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, drinking our own champagne (laughs). >> John: Not the Kool-Aid. >> You know, yeah, yeah. >> John: Go right to the dog. >> Madhu: Yeah, exactly. >> Dave: No dog smoothie. (laughs) >> Drinking our own champagne, and truly we're seeing transformation in how we're running our own business as well. >> Now what, there are always surprises. There are always some, you know, accidents kind of waiting to happen, but in terms of the IOT, you know, have got these millions, right, of sensors-- >> Madhu: Mm-hm. >> You know, feeding data in, and what, from a governance perspective, is maybe a concern about, you know, an unexpected source or an unexpected problem or something where yeah, 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've expected. So, I mean, just looking down the road from your perspective, you know, is there anything along those lines that you're putting up flags for just to keep an eye on to 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 it's... You know, 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, it's all about that you don't end up in jail. Right, that's the real focus in terms of our technology in terms of the way we're looking at. >> So, maybe help our audience a little bit. So, I described at our open AI is sort of the umbrella and machine learning is the math and the algorithms-- >> Madhu: Yeah. >> That you apply to train systems to do things maybe better than, maybe better than humans can do and then there's deep learning, which is, you know, neural nets and so forth, but 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. >> Madho: Yes. >> And so I wonder if you could comment on that and then describe from the client's 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 our client, people don't understand actually how powerful that is, because your data, your catalog, is learning. It's 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 them 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 get data. (laughs) So, by now doing machine learning in our software, we can do this in days, even in 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 folks and go focus on some, you know, 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. >> Madhu: Yeah. >> I mean, taxonomies are like cement in data classification, and while we can't, you know, move the business forward because we have this classification, can your machines adapt, you know, 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. >> Dave: Mm-hm. >> They don't, and you can't put humans to 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 kind. >> Broader question, one of the good things about things like GDPR is it forces, it puts a deadline on there and we all know, "Give me a deadline and I'll hit it," so it sort of forces action. >> Madhu: Mm-hm. >> And that's good, we've talked about the value that you can bring to an organization from a data perspective, but there's a whole non-governance component of data orientation. How do you see that going, can the governance initiatives catalyze sort of what I would call a... You know, people talk about a data driven organization. Most companies, they may say they are data driven but they're really not foundational. >> Mm-hm. >> Can governance initiatives catalyze that transformation to a data driven organization, and if so, how? >> Yeah, no, absolutely, right. So, the example I was sharing earlier with talking to some of the large financial institutes, where the business guys, you know, outside of IT are talking about how important it is for them to get the data really real time, right, 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. >> Dave: Right. >> We don't live in that world anymore, right. It's online, it's real time, it's all, you know, 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 in governance. The way I explained it earlier, it's 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-- >> Madhu: Yeah. >> It's embedded, it's aware, it's anticipatory. Am I overstating that? >> Madhu: No. >> I mean, is the data going to find me? >> Yeah, you know, (laughs) that's a good way to put it, you know, so no, you're at the, 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 that they'll win. >> So, if you have a partner that maybe, if it is contextual, I mean... >> Dave: Yeah. >> So, also make it relevant-- >> Madhu: Yes. >> To me and help me understand its relevance-- >> Madhu: Yes. >> Because maybe as a, I hate to say as a human-- >> Madhu: Yes. >> That maybe just don't have that kind of prism, but can that, does that happen as well, too? >> Madhu: Yeah, no. >> John: It can put up these white flags and say, "Yeah, this is what you need." >> Yeah, no, 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 is 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 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 skillsets 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. >> Yeah. >> 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, my personal opinion is the way the technology's 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 (laughs) 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? >> Mm-hm. >> Will governance, you know, in the next, let's say decade, become essentially automated? >> That's my desire, and with the product-- >> Dave: That's your job. >> That's 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, right. (laughs) 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 our products and more automation we can do, more smarts we can build in. I coined this term like, hey, we've got to build smarter metadata, right? >> Dave: 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." System already knows it, they understand certain behaviors, they know what your applications is going to do and it kind of automatically does it for you. No more science fake, I think it can happen. (laughs) >> Do you think we'll ever have more metadata than data... (laughs) >> Actually, somebody did ask me that question, will we be figuring out here we're building data lakes, what do we do about metadata. No, I think we will not have that problem for a while, we'll make it smarter. >> Dave: Going too fast, right. >> You're right. >> But it is, it's like working within your workforce and you're telling people, you know, "You're a treasure hunter and we're going to give you a better map." >> Madhu: Yeah. >> So, governance is your better map, so trust me. >> Madhu: Hey, I like that, maybe I'll use it next time. >> Yeah, but it's true, it's like are you saying governance is your friend here-- >> Madhu: Yes. >> And we're going to fine-tune your search, we're going to make you a more efficient employee, we'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 foe. >> Yes, yeah, be your differentiator, right. >> But my takeaway is it's fundamental, it's embedded. You know, you're doing this now with less thinking. Security's got to get to the same play, but for years security, "Ugh, it slows me down," but now people are like, "Help me," right, >> Madhu: Mm-hm. >> And I think 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. >> Madhu: Yeah, absolutely. >> Well, Madhu, thank you for the time. We mentioned on the outset by the interview 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 Mohed, Kepa, I love you so much. (laughs) >> All right. >> Thank you. >> So, they know where mom is. (laughs) New York City at IBM's Machine Learning Everywhere, Build Your Ladder To AI. Thank you for joining us, Madhu Kochar. >> Thank you, thank you. >> Back with more here from New York in just a bit, you're watching theCUBE. (techy music playing)