>> >> Announcer: From around the globe, it's theCUBE, with digital coverage of IBM Think2021, brought to you by IBM. >> Hey, welcome back to theCUBE's coverage of IBM Think2021 virtual. I'm John Furrier, host of theCUBE. We're here with Madhu Kochar who's the vice president, product management for IBM Data and AI, also CUBE alumni. Great to see you, Madhu, thanks for coming on theCUBE remotely soon to be in person, I hope soon. Great to see you. >> Thanks, John. Obviously very, very happy to be here and yeah, like you said, hopefully next time face-to-face. >> Yeah, I can't, we've had many conversations in the past on theCUBE about data, machine learning. Now more than ever it's prime time. And as companies put the AI to work, they're facing more and more challenges, especially with the growing data complexity and obviously quality, what are they doing to solve it, solve these problems today? I mean, as cloud scales here, it's transformation, innovation cloud scale, still complexity. What are they doing to solve this? >> Yeah, you're right, John, right? The data complexity is just becoming overwhelming and it's threatening what I would say progress. And you would agree to that, right? As organizations are struggling to turn these complex data landscapes and to get some sustained value out of it and it's becoming costly. And I believe the worst of it is because of the rapid pace of digitization is happening right now generate, the data is just getting generated at higher velocity, all different types of data and all different touch points. And traditionally, trying to move replicate data, integrate data and bring it all into the big data store. It's just not working out, right? It's becoming costly and the stats shows that 97% of enterprise data is still not trusted or analyzed. And with all the movements happening on to the migrating of data to multiclouds is just adding a lot of complexity. Our point of view, all of us has been, we've been talking a lot about hybrid. And to me, hybrid approach is the one that truly accepts the reality, that data will be everywhere and is going to be changing by the minute. And it turned and we have to figure out how do we turn that problem into an advantage. To me, automation is going to be inevitable. And what we truly believe in is like you got to leave the data where it is, bring AI to your data and that means what's AI for business here, right? So, that truly means that you have to be able to understand the language of the business, automates workflows and experiences and truly deliver the trust and predictability in these outcomes. That is where John I firmly believe where we need to be going. >> Madhu you know that's so right on. And I think the business case is well understood, what's interesting is, is that we were talking to some other IBMers about autonomous shipping, about ships that are being powered by automation and getting all that data and integrating in all kinds of diverse sources, is also a business challenge. So autonomous vehicles, autonomous everything these days requires massive amounts of data ingestion and processing and insights and operationalizing and decision-making, all kind of coming in. This is like the Holy Grail of automation. So I have to ask you what is the IBM's perspective on managing data that exists in different forms and on across different environments in an organization? Because this is where the diversity comes in and it's actually better for the data, because AI loves diverse data 'cause the better the data, the better the AI, right? So, but it's still complicated. How are you guys looking at this perspective of managing data that exists in different forms? >> Yeah, I know, that's a great question. And I think this is where we need to be thinking about, what we are talking about here is that you need what I call an intelligent data fabric, data fabric it's a term which is just picking up in the industry and the definition, which I would say an intelligent data fabric is what weaves together and automates data and AI lifecycle over anything. Over anything means any data, any cloud anywhere, right? And what do you get with this? What you get with this is that you're able to then unlock totally new insights from unified data. You're able to democratize your trusted data usage across more people. You unleash truly productivity, right? And you reduce cost and risk and you make AI for business easier. Like I was talking about you bring your AI to the data, it's all about AI for business. You make AI for business easier, faster and more trusted. And underneath the covers, automation is what's going to help us to scale all this. So by truly bringing the intelligent data fabric together which helps us automate and view all these things is going to be the answer. >> I love noises like democratizing, trusted data usage and unifying data and getting all those insights 'cause that tries the value. I'm sold on that and I love it. And I understand it, the question I have for you and I heard this term and I'd love to get you to help me define it for the audience, the notion of distributed data life cycles. Can you describe and define what does that mean for an organization? >> So truly that would just mean, right? Like I was talking about what is intelligent data fabric. This is all about data is everywhere, right? You're, it is in your operational data stores, your data warehouses and now with spans of multiple clouds, people are moving their workloads to various clouds, it's everywhere. When you have to make a decision, you have to be able to have access to all that data, right? You have to keep in mind what are your data privacy rules? What is your data residency rules around it, right? And how do I make, how do I analyze that data? How do I categorize that data to have some business outcomes, right? And anything what you're doing right now, it's going to require AI to it. How do I apply AI to where data lives? That is where the whole aspect of the intelligent data fabric needs to come into the picture. >> Yeah, and my next question is around some of the new announcements that you guys have here at Think and I want to get this new upgrade, and new data pack announcement, because I think, I mean, Cloud Pak for Data because having data become operationalized is much more sensitive than it was in the Wild West in the early days. Like, Hey, the data is everywhere. People are concerned and there's compliance, there's risk and getting sued and different sovereignty issues. So, you guys have had this IBM Cloud Pak for Data for a few years now, helping clients with the chance, some of the data challenges. I think we've talked about it. You guys are announcing more here, the next generation. Could you explain this announcement? >> Yes, yes and yeah, you're right. Cloud Pak for Data was announced about three years ago, we launched it then, many customers in production with that, so very proud. And it really, Cloud Pak for Data in simple terms is all about your data platform and analytics platform, right? So at this think, we are announcing what I call the next generation of Cloud Pak for Data. A lot of enhancements going in but I would like to focus on three top key capabilities which we are bringing in. And it's all about how we are weaving together as part of the intelligent data fabric I was talking about earlier. So the three capabilities which I want the audience to walk away is, number one, auto privacy. What does that mean? It means how do we automate how you enforce universal data and usage policies across hybrid data and cloud ecosystems of various sources and users and how to express that to users in business terms and why? Because this is going to further simplify risk mitigation across an organization of self-serve data consumers. So that's all about auto privacy. The second key capabilities will be around auto catalog. Very, very critical. I call it the brain of it, right? This is where, how we automate, how it is, the data is discovered, how is cataloged and enriched for users, relevance of maintenance of knowledge for business ready data, right? Which is spread again across hybrid sources and multiple cloud landscapes. The third thing, very critical is what we call AutoSQL. This is how you're going to automate how you access, update and unify data spread across distributed data and cloud landscapes without the need of actually doing any data movements or replication if it's needed. So, and part of that data access is also needs to be, is it optimized for performance, right? Can I get to the petabytes of scale of data and how does the visual query building experiences look on top of that? So we are just so super excited about obviously with Cloud Pak for Data with this new enhancements, how we are reading the story around with data fabric, intelligent data fabric, with three things, right? Auto privacy, auto catalog and AutoSQL. And John, this is also on top of what we've been also talking about AutoAI for a while and this is all about AI life cycle management. There's going to be tons of enhancements coming in as to how we are simplifying federated training, right? Federated training across complex and siloed data sets is going to be important fact sheets. How do we improve the model quality and explainability that is going to be very critical for us and the time series optimizations? So all these auto stuff weaved with our intelligent data fabric, is going to be our next generation of Cloud Pak for Data. And I must say, John, lot of our clients are early adopters, early customers, been giving us the fantastic feedback. And it's like, this is what's needed. And you mentioned that earlier, right? Complexity is known, everybody wants a solution. What a great way to get these quick outcomes of the data, right? We've got to monetize the data. And that's what it's going to lead to. >> Madhu, is very exciting, great insight and congratulations. I love the auto name, autopilot, AutoAI. Just, it sounds automated. And I guess my final question for you is one that's a little bit more current around hybrid and that is the big theme that we're seeing. And we've been reporting and kind of connecting the dots here in the CUBE, through all the different interviews with you guys and all your partners is this ecosystem dynamic now with hybrid cloud is more important than ever before because cloud and cloud operations is API based. So more and more people are connected. So is this where auto privacy, auto catalog and AutoSQL and AI connect in? Is that where it's relevant? Because hybrid cloud really speaks about ecosystems and partnering 'cause no one does it alone anymore. >> Absolutely, you hit it on the nail, right? Hybrid is all not just about on-prem and one cloud is all about intra clouds and inter clouds and everywhere. And that is where the data fabric helps us, right? And that auto privacy meaning your data is spread across, how do I understand what are the policies across? Or do I respect my data residency? So exactly to the point that is what this gives us the solution. >> That's awesome. I do remember the old school inter networking was a category in the industry, connecting networks together. Now we have inter clouding, inter DataOps. It's all good, exciting. Thanks for coming on, really appreciate your insights. You're a pro and love the work you're doing over there in the product management. Great job and looking forward to hearing more. Thanks for coming on. >> Thank you, thank you John. >> Okay, Madhu Kochar, here at VP of product management IBM Data and AI, the hottest area in hybrid cloud. Of course, it's the CUBE coverage for IBM Think2021, I'm John Furrier. Thanks for watching. (upbeat music)

>>from around the globe. It's the cube >>With digital coverage of IBM think 20 >>21 brought to you >>by IBM Hey, welcome back to the cubes coverage of IBM think 2021 virtual. I'm john for a host of the cube we're here with my do Kochar, who's the vice president, Product management for IBM data and Ai also cube alumni. Great to see you do. Thanks for coming on the cube remotely. Soon to be in person. I hope soon. Great to see you. >>Thanks john obviously very, very happy to be here and yeah, like you said, hopefully next time face to face. >>Yeah, I can't, we've had many conversations to pass on the cube about data machine learning now more than ever it's prime time and as companies put the ai to work, you know, they're facing more and more challenge especially with the growing data complexity and equality. What are they doing to solve it, solve these problems today? I mean as cloud scales here, its transformation innovation, cloud scale, still complexity. What are they doing to solve this? >>Yeah, you're right, john right. The data complexity is just becoming overwhelming and it's threatening what I would say progress and you would agree to that right. As organizations are struggling to turn these complex data landscapes and to get some sustained value out of it and it's becoming costly and I believe the worst of it is because of the rapid pace of digitalization is happening right now, generate. The data is just getting generated at higher velocity, all different types of data and all different touchpoints and traditionally, you know, trying to move replicate data, integrate data and bring it all into the big data store is just not working out right? It's becoming costly And and the stats shows that 97 of enterprise data is still not trusted or analyzed and with all the movements happening onto the migrating of data to multi clouds is just adding a lot of complexity. Our point of view always has been, we've been talking a lot about hybrid and to me, hybrid approach is the one that truly accepts the reality, that data will be everywhere and it's going to be changing by the minute and it turned and we have to figure out how do we turn that problem into an advantage? Um To me, automation is going to be inevitable and what we truly believe in is like you gotta leave the data where it is bring ai to your data and that means what's aI for business here. Right. So that really means that you have to be able to understand the language of the business, automates, workflows and experiences and truly deliver the trust and predictability in these outcomes. That is where john I firmly believe that we need to be going, >>do you know? That's so right on. And I think the business cases is well understood. What's interesting is that we were talking to some other IBM Rs about autonomous shipping, about ships that are being powered by automation and getting all that data and integrating in all kinds of diverse sources um is also a business challenge. So autonomous vehicles, autonomous everything these days requires massive amounts of data, ingestion and processing and insights and and operationalize ng and decision making, all kind of coming in. This is like the holy grail of, of automation. So I have to ask you what is the iBMS perspective on managing data that exists in different forms and on across different environments in an organization? Because this is where the diversity comes in and it's actually better for the data because a I loves diverse data because the better the data the better the eye. Right? So but it's still complicated. How are you guys looking at this perspective of managing data that exists? >>Yeah, No, that's a that's a great question. And I think this is where we need to be thinking about what we are talking about here is that you need what I call an intelligent data fabric. Data fabric is a is a term which is just picking up in the industry. And the definition which I would say an intelligent data fabric is what weaves together and automates data and ai lifecycle over anything over anything means any data, any cloud, anywhere. Right? And what do you get with this? What you get with this is that you are able to then unlock totally new insights from unified data. You're able to democrat is your trusted data usage across more people. You unleash truly productivity right? And you reduce cost and risk and you make A I for business easier. Like I was talking about you bring your ai to the data, it's all about ai for business you make A I for business easier, faster and more trusted. And underneath the covers, automation is what's going to help us to scale all this. So by truly bringing the intelligent data fabric together which helps us automate and view all these things is going to be the answer. >>I'd love noses like democratizing trusted data usage and unifying data and getting all those insights because that drives the value I'm sold on that and I love it and I understand it the question I have for you and I heard this term, I'd love to get you to help me define it for the audience. The notion of distributed data life cycles, can you describe and define what does that mean for an organization? >>So truly that would just mean right? Like I was talking about, what is intelligent data fabric? This is all about data is everywhere, right? You're it is in your um operational data stores, your data warehouses and now with spans of multiple clouds, you know, people are moving their workloads to various clouds. It's everywhere. When you have to make a decision, you have to be able to have access to all that data, right? You have to keep in mind. What are your data privacy rules? What is your data residency rules around it? Right. And how do I make, how do I analyze that data? How do I categorize that data to have some business outcomes? Right. And anything what you're doing right now? It's gonna require a I to it. How do I apply ai to where data lives? That is where the whole aspect of the intelligent data fabric needs to come into the picture. >>Yeah. My next questions around some of the new announcements that you guys have here thinking I want to get this new new upgrade new data pack announcement. Because I think, I mean cloud pack for data because having data become operationalized is much more sensitive than it was in the wild west of the early days. Like hey, the data's everywhere, people are concerned, you know, and there's compliance risk and getting sued and different sovereignty issues. So, you know, you guys have had this IBM cloud pack for data for a few years now helping clients with the chance, some of the data challenges. I think we've talked about it. You guys are announcing more here the next generation. Could you explain this announcement? >>Yes, Yes and yeah, you're right. Cloud Back for data was announced about three years ago. We launched it then. Uh many customers in production with that, so very proud. And it really cloud back for data in simple terms is all about your data platform and analytics platform. Right. So at this tank we are announcing what I call the next generation of cloud back for data, a lot of enhancements going in. But I would like to focus on 33 top key capabilities which we are bringing in. And it's all about how we are weaving together as part of the intelligent data fabric. I was talking about earlier. So the three capabilities, which I want the audience to walk away is number one auto privacy. What does that mean? It means? How do we automate how you enforce universal data and usage policies across hybrid data and cloud ecosystems of various sources and users and how to express that in you to users in business terms and why? Because this is going to further simplify risk mitigation across an organization of self serve data consumers. So that's all about auto privacy. The second key capabilities will be around auto catalog. Very, very critical. I call it the brain of it, Right? This is where how we automate how it is that the data is discovered how is catalogued and enriched for users, relevance of maintenance of knowledge for business ready data, right? Which is spread again across hybrid hybrid sources and multiple cloud landscapes. The third thing very critical is what we call auto sequel. This is how you're going to automate how you access, update and unify data spread across distributed data and cloud landscapes without the need of actually doing any data movements or replication if it's needed. So. And and part of that data access is also needs to be, is it optimized for performance? Right. Can I get to the petabytes of scale of data? And how does the visual query building experiences look on top of that? So we are just so super excited about obviously with cloud back for data, with this new enhancements, how we are reading the story around with data fabric, intelligent data fabric with three things. Right? Auto privacy, auto catalog and auto sequel and john this is also on top of what we've been also talking about Auto Ai for a while and this is all about aI Lifecycle management. There's gonna be tons of enhancements coming in as to how we are simplifying federal training right? Federally training across complex and siloed data. Such is going to be important fact sheets. How do we improve the model quality and explain ability that is going to be very critical for us and the time series optimizations. So all these auto stuff weaved with our um, intelligent data fabric is going to be our next generation of cloud back for data. And I must say john a lot of our clients are early adopters, early customers been giving us the fantastic feedback and it's like this is what's needed. And you mentioned that earlier, right, complexity is known. Everybody wants a solution. What a great way to, to get this uh, quick outcomes of the data, right? We've got to monetize the data and that's what it's going to need to >>do is very exciting gradients. Congratulations. I love the auto name. Autopilot. Auto, Auto Auto A I just, it sounds automated and I guess my final question for you is, um, one that's a little bit more current around hybrid and that is the big theme that we're seeing and we've been reporting and kind of connecting the dots here in the cube through all the different interviews with you guys and and all your partners. Is this ecosystem dynamic now with hybrid cloud is more important than ever before because cloud and cloud operations is A. P. I based so more and more people are connecting. So is this where auto privacy, Auto catalog and auto sequel and A I connect in. Is that where it's relevant because Hybrid cloud really speaks about ecosystems and partnering because no one does it alone anymore. >>Absolutely. You hit it on the nail, right. Hybrid is all not just about on prem and one cloud, it's all about intra clouds and inter clouds and everywhere. And that is where the the data fabric helps us, right. And the auto privacy meaning your data is spread across. How do I understand? What are the policies across? How do I respect my data residency? So exactly to the point that is what this gives us the solution. >>Remember the old school inter networking was a category in the industry, you know, connecting networks together. Now we have inter clouding into data ops all all good, exciting. Thanks for coming on. Really appreciate your insights. Uh you're a pro and love the work you're doing over there, the product management. Great job, looking forward to hearing more. Thanks for coming on. >>Thank you. Thank you. >>Don. Okay madu Kochar here at VP of Product measure, IBM Data and ai the hottest area in hybrid cloud. Of course it's the cube coverage probably I think 2021. I'm john ferrier. Thanks for watching. Mhm. >>Mhm mm. Yeah.

>> Live from Boston, it's theCUBE covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM CDO Summit here in beautiful Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillin. We have two guests for this segment, we have Susan Wagner, who is the VP Data Artificial Intelligence and Governance at Deutsche Telekom and Madhu Kochar, whose the Vice President Analytics Product Development at IBM. Thank you so much for coming on the show. >> Thank you. >> Happy to be here. Susan you're coming to us from Berlin, tell us a little bit about what you it's a relatively new job title and Paul was marveling before the cameras are rolling. Do you have artificial intelligence in your job title? Tell us a little bit about what you do at Deutsche Telekom. >> So we have a long history, working with data and this is a central role in the headquarter guiding the different data and artificial intelligence activities within Deutsche Telekom. So we have different countries, different business units, we have activities there. We have already use case catalog of 300,000 cases there and from a central point we are looking at it and saying, how are we able really to get the business benefit out of it. So we are looking at the different product, the different cases and looking for some help for the business units, how to scale things. For example, we have a case we implemented in one of our countries, it was about a call center to predict if someone calls the call center, if this is a problem, we would never have(laughing) at Deutsche Telekom but it could happen and then we open a ticket and we are working on it and then we're closing that ticket and but the problem is not solved, so the ticket comes again and the customer will call again and this is very bad for us bad for the customer and we did on AI project, there predicting what kind of tickets will come back in future and this we implemented in a way that we are able to use it not only in one country, but really give it to the next country. So our other business units other countries can take the code and use it in another country. That's one example. >> Wow. >> How would you define artificial intelligence? There's someone who has in your job-- (laughing) >> That's sometimes very difficult question I must admit. I'm normally if I would say from a scientific point, it's really to have a machine that works and feels and did everything like a human. If you look now at the hype, it's more about how we learn, how we do things and not about I would say it's about robotic and stuff like that but it's more how we are learning and the major benefit we are getting now out of artificial intelligence is really that we are able now to really work on data. We have great algorithm and a lot of progress there and we have the chips that develops so far that we are able to do that. It's far away from things like a little kid can do because little kid can just, you show them an apple and then it knows an apple is green. It's were-- >> A little kid can't open a support ticket. (laughing) >> Yeah, but that's very special, so in where we special areas, we are already very, very good in things, but this is an area, for example, if you have an (mumbles) who is able like we did to predict this kind of tickets this agreement is not able at the moment to say this as an apple and this is an orange, so you need another one. So we are far away from really having something like a general intelligence there. >> Madhu do I want to bring you into this conversation. (laughing) And a little bit just in terms of what Susan was saying the sort of the shiny newness of it all. Where do you think we are in terms of thinking about the data getting in the weeds of the data and then also sort of the innovations that we saw, dream about really impacting the bottom line and making the customer experience better and also the employee experience better? >> Yeah, so from IBM perspective, especially coming from data and analytics, very simple message, right? We have what we say your letter to AI. Everybody like Susan and every other company who is part of doing any digital transformation or modernization is talking about Ai. So our message is very simple, in order to get to the letter of AI, the most critical part is that you have access to data, right? You can trust your data, so this way you can start using it in terms of building models, not just predictive models but prescriptive and diagnostics. Everything needs to kind of come together, right? So that is what we are doing in data analytics. Our message is very, very simple. The innovations are coming in from the perspectives of machine learning, deep learning and making and to me that all equates to automation, right? A lot of this stuff data curation, I think you can Susan, tell how long and how manual the data curation aspects can be. Now with machine learning, getting to your latter of AI, You can do this in a matter of hours, right? And you can get to your business users, you can if your CHARM model, If your clients are not happy, your fraud, you have to detect in your bank or retail industry, it just applies to all the industry. So there is tons of innovation happening. We just actually announced a product earlier called IBM Cloud Private for Data. This is our the analytics platform which is ready with data built in governance to handle all your data curation and be building models which you can test it out, have all the DevOps and push it into production. Really, really trying to get clients like Deutsche Telekom to get their journey there faster. Very simple-- >> We've heard from many of our guests today about the importance of governance, of having good quality data before you can start building anything with it. What was that process like? How is the... what is the quality of data like at Deutsche Telekom and what work did it take to get it in that condition. >> So data quality is a major issue everywhere, because as Madhu that this is one of the essential things to really get into learning, if you want to learn, you need the data and we have in the different countries, different kind of majorities and what we are doing at the moment is that we are really doing it case by case because you cannot do everything from the beginning, so you start with one of the cases looking what to do there? How to define the quality? And then if the business asked for the next case, then you can integrate that, so you have the business impact, you have demand from the business and then you can integrate the data quality there and we are doing it really step by step because to bring it to the business from the beginning, it's very, very difficult. >> You mentioned, one of the new products that you announced just today, what are some of the-- (laughing) >> We announced it in may. >> Oh, okay, I'm sorry. >> It's okay still new. >> In terms of the other innovations in the pipeline, what I mean this is such a marvelous and exciting time for technology. What are some of the most exciting developments that you see? >> I think the most exciting, especially if I talk about what I do day out everything revolves around metadata, right? Used to be not a very sticky term, but it is becoming quite sexy all over again, right? And all the work in automatic metadata generation, understanding the lineage where the data is coming from. How easy, we can make it to the business users, then all the machine learning algorithms which we are doing in terms of our prescriptive models and predictive, right? Predictive maintenance is such a huge thing. So there's a lot of work going on there and then also one of the aspects is how do you build once and run anywhere, right? If you really look at the business data, it's behind the firewalls, Is in multicloud. How do you bring solutions which are going to be bringing all the data? Doesn't matter where it resides, right? And so there's a lot of innovation like that which we are working and bringing in onto our platform to make it really simple story make data easy access which you can trust. >> One of the remarkable things about machine learning is that the leading libraries have all been open source, Google, Facebook, eBay, others have open source their libraries. What impact do you think that has had on the speed with which machine learning is developed? >> Just amazing, right. I think that gives us that agility to quickly able to use it, enhance it, give it back to the community. That has been the one of the tenants for, I think that how everybody's out there, moving really really fast. Open source is going to play a very critical role for IBM, and we're seeing that with many of our clients as well. >> What tools are you using? >> We're using different kind of tools that depending on the departments, so the data scientists like to use our patents. (laughing) They are always use it, but we are using a lot like the Jupiter notebook, for example, to have different kind of code in there. We have in one of our countries, the classical things like thus there and the data scientists working with that one or we have the Cloud-R workbench to really bringing things into the business. We have in some business-- >> Data science experience. >> IBM, things integrated, so it it really depends a little bit on the different and that's a little bit the challenge because you really have to see how people working together and how do we really get the data, the models the sharing right. >> And then also the other challenges that all the CDOs face that we've been talking about today, the getting by in the-- >> Yes. >> The facing unrealistic expectations of what data can actually do. I mean, how would you describe how you are able to work with the business side? As a chief working in the chief data office. >> Yeah, so what I really like and what I'm always doing with the business that we are going to the business and doing really a joint approach having a workshop together like the design thinking workshop with the business and the demand has to come from the business. And then you have really the data scientists in there the data engineers best to have the operational people in there and even the controlling not all the time, but that it's really clear that all people are involved from the beginning and then you're really able to bring it into production. >> That's the term of DataOps, right? That's starting to become a big thing. DevOps was all about to agility. Now DataOps bring all these various groups together and yeah I mean that's how you we really move forward. >> So for organizations so that's both of you for organizations that are just beginning to go down the machine learning path that are excited by everything you've been hearing here. What advice would you have for them? They're just getting started. >> I think if you're just getting started to me, the long pole item is all about understanding where your data is, right? The data curation. I have seen over and over again, everybody's enthusiastic. They love the technology, but the... It just doesn't progress fast enough because of that. So invest in tooling where they have automation with machine learning where they can quickly understand it, right? Data virtualization, nobody's going to move data, right? They're sitting in bedrock systems access to that which I call dark data, is important because that is sometimes your golden nugget because that's going to help you make the decisions. So to me that's where I would focus first, everything else around it just becomes a lot easier. >> Great. >> So-- >> Do you have a best practice too? Yeah. >> Yeah. Focus on really bringing quick impact on some of the cases because they're like the management needs success, so you need some kind of quick access and then really working on the basics like Madhu said, you need to have access of the data because if you don't start work on that it will take you every time like half a year. We have some cases where we took finance department half a year to really get all that kind of data and you have to sharpen that for the future, but you need the fast equipments. You need to do both. >> Excellent advice. >> Right, well Susan and Madhu thank you so much for coming on theCUBE, it's been great having you. >> Thank you. >> Thank you. >> I'm Rebecca Knight for Paul Gillin we will have more from theCUBE's live coverage of the IBM CDO just after this. (upbeat music)

>> narrator: From Las Vegas it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. My name is Dave Vellante. I'm here with Peter Burris, my co-host, and you're watching theCUBE, the leader in live tech coverage. Our day two of our wall to wall coverage of IBM Think. Madhu Kochar is here. She's the Vice President of Analytics, Product Development at IBM and she's joined by Pat Maqetuka >> Close enough. She's a data officer at Nedbank. Ladies, welcome to theCUBE. You have to say your last name for me. >> Patricia Maqetuka. >> Oh, you didn't click >> I did! >> Do it again. >> Maqetuka. >> Amazing. I wish I could speak that language. Well, welcome. >> Madhu & Pat: Thank you. >> Good to see you again. >> Madhu: Thank you. >> Let's start with IBM Think. New show for you guys you consolidated, you know, six big tent events into one. There's a lot of people, there's too many people, to count I've been joking. 30, 40 thousand people, we're not quite sure, but how's the event going for you? What are clients telling you? >> Yeah, no, I mean, to your point, yes, we brought in all three big pillars together; a lot of folks here. From data and analytics perspective, an amazing, amazing event for us. Highlights from yesterday with Arvind Krishna on our research. What's happening. You know, five for five, that was really inspiring for all of us. You know, looking into the future, and it's not all about technology, was all about how we are here to help protect the world and change the world. So that as a, as a gige, as an engineer, that was just so inspiring. And as I was talking to our clients, they walk away with IBM as really a solution provider and helping, so that was really good. I think today's, Ginni's, keynote was very inspiring, as well. From our clients, we got some of our key clients, you know, Nedbank is here with us, and we've been talking a lot about our future, our strategy. We just announced, Ginni actually announced, our new product, IBM Cloud Private for Data. Everything around data, you know, where we are really bringing the power of data and analytics all together on a private cloud. So that's a huge announcement for us, and we've been talking a lot with our clients and the strategies resonating and particularly, where I come from in terms of the co-ordinance and integration space, this is definitely becoming now the "wow" factor because it helps stitch the entire solutions together and provide, you know, better insights to the data. >> Pat from your perspective, you're coming from Johannesburg so you probably like the fact that there's all IBM in one, so you don't have to come back to three or four conferences every year, but love your perspectives on that, and can you please tell us about Nedbank and your role as Chief Data Officer. >> Nedbank is one of the big five financial banks in South Africa. I've been appointed as the CDO about 18 months ago, so it's a new role in the bank per say. However, we're going through tremendous transformation in the bank and especially our IT eco-system has been transformed because we need to keep up with what is happening in the IT world. >> Are you the banks first Chief Data Officer? >> Definitely yes. I'm the first. >> Okay. So, you're a pioneer. I have to ask you then, so where did you start when you took over as the Chief Data Officer? I mean banking is one of those industries that tends to be more Chief Data Officer oriented, but it's a new role, so where did you start? >> Well we are not necessary new in the data, per say. We have had traditional data warehousing functions in the organization with traditional warehousing or data roles in the organization. However, the Chief Data role was never existent in the bank and actual fact, the bank appointed two new roles 18 months ago. One of them was the Chief Digital Officer, who is my colleague, and myself being appointed as the Chief Data Officer. >> Interesting, okay. I talked to somebody from Northern Trust yesterday and she was the lead data person and she said, "I had to start with a mission. We had to find the mission first and then we looked at the team, and then we evolved into, how we contribute to the business, how we improve data quality, who has access, what skills we needed." Does that seem like a logical progression and did you take a similar path? >> I think every bank will look at it differently or every institution. However, from a Nedbank perspective, we were given the gift by the regulator in bringing the BCBS 239 compliancy into play, so what the bank then did, how do we leverage, not just being compliant, but leveraging the data to create competitive advantage, and to create new sources of revenue. >> Okay. Let's talk about, Madhu we talked about this in New York City, you know, governance, compliance, kind of an evil word to a lot of business people. Although, your contention was "Look, it's reality. You can actually turn it into a positive." So talk about that a little bit and then we can tie it into Nedbank's experiences. >> Yeah, so I firmly believe in, you know, in the past governance 1.0 was all about compliance and regulations, very critical, but that's all we drew. I believe now, it's all about governance 2.0, where it's not just the compliance, but how do I drive insights, you know, so data is so, so critical from that perspective, and driving insights quicker to your businesses, is going to be very important, so as we engage with Nedbank and other clients as well, they are turning that because they are incumbents. They know their data, they've got a lot of data, you know, some of, they know sitting in structure, law structure land, and it's really, really important that they quickly able to assess what's in it, classify it, right, and then quickly deliver the results to the businesses, which they're looking for, so we're, I believe in lieu of governance 2.0, and compliance and regulations are always going to be with us, and we're making, actually, a lot of improvements in our technology, introducing machine learning, how we can do these things faster and quicker. >> So one of the first modern pieces of work that Peter and I did was around data classification and that seems to be, I heard this theme before, it seems to be a component or a benefit of putting governance in place. That you can automate data classification and use it to affect policy, but Pat, from your standpoint, how do you approach governance, what are the business benefits beyond "we have to do this"? >> Like I earlier on alluded to, we took the regulation as a gift and said, "How do we turn this regulation into benefits for the organization?" So in looking at the regulation we then said, "How do we then structure the approach?" So we looked at the two prompts. The first was, the right to win. The right to win meaning that we are able to utilize the right to compete approach from a regulation perspective, to create a platform and a foundation for analytics for our organization. We also created the blueprint for our enterprise data program and in the blueprint, we also came up with key nine principles of what it means to stay true to our data. I.e. you mentioned classification, you mentioned data politic, you mentioned lineage. Those are the key aspects within our principles. The other key principle we also indicated was the issue around duplication. How do we ensure that we describe data once, we ingest it once, but we use it multiple times to answer different questions, and as you are aware, in analytics, the more you mine the data, the more inquisitive you become, so it is, (clears throat) Sorry. It's not been from data to information, information to insight, and eventually insights to foresight, so looking into the future, and now you bring it back into data. >> And also some points that you've made Pat, so the concept is, one of the challenges of using the fuel example, is that governance of fuel, is still governance of a thing. You can apply it here, you can apply it there, you can't apply it to both places. Data's different and you were very, very accurate when you said "We wanted to find it once, we want to ingest it once, we want to use it multiple times". That places a very different set of conditions on the types of governance and in many respects, in the past, other types of assets where there is this sense of scarcity, it is a problem, but one of the things that I'm, and this is a question, is the opportunity, you said the regulatory opportunity, is the opportunity, because data can be shared, should we start treating governance really as a way of thinking about how to generate value out of data, and not a way of writing down the constraints of how we use it. What do you think about that? >> I think you are quite right with that because the more you give the people the opportunity to go and explore, so you unleash empowerment, you unleash freedom for them to go and explore. They will not see governance as a stick like I initially indicated, but they see it as business as usual, so it will come natural. However, it doesn't happen overnight. People need to be matured, organization is to be matured. Now, the first step you have to do is to create those policies, create awareness around the policies, and make sure that the people who are utilizing the data are trained in to what are the do's and the don'ts. We are fully aware that cyber security's one of our biggest threats, so you can also not look at how you create security around your data. People knowing that how I use my data it is an asset of the bank and not an asset of an individual. >> I know you guys have to go across the street, but I wanted to get this in. You're a global analytics global elite client; I want to understand what the relationship is. I mean, IBM, why IBM, maybe make a few comments about your relationship with the company. >> I think we as Nedbank, we are privileged, actually, to be inculcated into this global elite program of IBM. That has helped me in actual, in advancing what we need to do from a data perspective because anytime I can pick up a phone to collaborate with the IBM MaaS, I can pick up the phone whenever I need support, I need guidance. I don't have to struggle alone because they've done it with all the other clients before, so why should I reinvent the wheel, whereas someone else has done it, so let me tap into that, so that that can progress quicker than try it first. >> Alright. Madhu, we'll give you the final word. On Think and your business and your priorities. >> So, Think is amazing, you know, the opportunity to meet with all our clients and coming from product development, talking about our strategy and getting that validation is just good, you know, sharing open road maps with clients like Nedbank and our other global elites, you know. It gives us an opportunity, not just sharing of the road maps, but actually a lot of co-creation, right, to take us into the future, so I'm having a blast. I got to go run over and meet a few other clients, but thank you for having us over here. It's a pleasure. >> You're very welcome and thank you so much for coming on and telling your story, Pat, and Madhu, always a pleasure to see you. >> Thank you. >> Alright, got to get in your high horse and go. Thanks for watching everybody, we'll be right back after this short break. You're watching theCUBE live from IBM Think 2018. We'll be right back. (electronic music)

>> 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)

>> Narrator: Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering DataWorks Summit 2017, brought to you by Hortonworks. (digitalized music) >> Welcome back to theCUBE. We are live at day one of the DataWorks Summit, in the heart of Silicon Valley. I'm Lisa Martin with theCUBE; my co-host George Gilbert. We're very excited to be joined by our two next guests. Going to be talking about a lot of the passion and the energy that came from the keynote this morning and some big announcements. Please welcome Madhu Kochar, VP of analytics and product development and client success at IBM, and Jamie Engesser, VP of product management at Hortonworks. Welcome guys! >> Thank you. >> Glad to be here. >> First time on theCUBE, George and I are thrilled to have you. So, in the last six to eight months doing my research, there's been announcements between IBM and Hortonworks. You guys have been partners for a very long time, and announcements on technology partnerships with servers and storage, and presumably all of that gives Hortonworks Jamie, a great opportunity to tap into IBM's enterprise install base, but boy today? Socks blown off with this big announcement between IBM and Hortonworks. Jamie, kind of walk us through that, or sorry Madhu I'm going to ask you first. Walk us through this announcement today. What does it mean for the IBM-Hortonworks partnership? Oh my God, what an exciting, exciting day right? We've been working towards this one, so three main things come out of the announcement today. First is really the adoption by Hortonworks of IBM data sciences machine learning. As you heard in the announcement, we brought the machine learning to our mainframe where the most trusted data is. Now bringing that to the open source, big data on Hadoop, great right, amazing. Number two is obviously the whole aspects around our big sequel, which is bringing the complex-query analytics, where it brings all the data together from all various sources and making that as HDP and Hadoop and Hortonworks and really adopting that amazing announcement. Number three, what we gain out of this humongously, obviously from an IBM perspective is the whole platform. We've been on this journey together with Hortonworks since 2015 with ODPI, and we've been all champions in the open source, delivering a lot of that. As we start to look at it, it makes sense to merge that as a platform, and give to our clients what's most needed out there, as we take our journey towards machine learning, AI, and enhancing the enterprise data warehousing strategy. >> Awesome, Jamie from your perspective on the product management side, what is this? What's the impact and potential downstream, great implications for Hortonworks? >> I think there's two things. I think Hortonworks has always been very committed to the open source community. I think with Hortonworks and IBM partnering on this, number one is it brings a much bigger community to bear, to really push innovation on top of Hadoop. That innovation is going to come through the community, and I think that partnership drives two of the biggest contributors to the community to do more together. So I think that's number one is the community interest. The second thing is when you look at Hadoop adoption, we're seeing that people want to get more and more value out of Hadoop adoption, and they want to access more and more data sets, to number one get more and more value. We're seeing the data science platform become really fundamental to that. They're also seeing the extension to say, not only do I need data science to get and add new insights, but I need to aggregate more data. So we're also seeing the notion of, how do I use big sequel on top of Hadoop, but then I can federate data from my mainframe, which has got some very valuable data on it. DB2 instances and the rest of the data repositories out there. So now we get a better federation model, to allow our customers to access more of the data that they can make better business decisions on, and they can use data science on top of that to get new learnings from that data. >> Let me build on that. Let's say that I'm a Telco customer, and the two of you come together to me and say, we don't want to talk to you about Hadoop. We want to talk to you about solving a problem where you've got data in applications and many places, including inaccessible stuff. You have a limited number of data scientists, and the problem of cleaning all the data. Even if you build models, the challenge of integrating them with operational applications. So what do the two of you tell me the Telco customer? >> Yeah, so maybe I'll go first. So the Telco, the main use case or the main application as I've been talking to many of the largest Telco companies here in U.S. and even outside of U.S. is all about their churn rate. They want to know when the calls are dropping, why are they dropping, why are the clients going to the competition and such? There's so much data. The data is just streaming and they want to understand that. I think if you bring the data science experience and machine learning to that data. That as said, it doesn't matter now where the data resides. Hadoop, mainframes, wherever, we can bring that data. You can do a transformation of that, cleanup the data. The quality of the data is there so that you can start feeding that data into the models and that's when the models learn. More data it is, the better it is, so they train, and then you can really drive the insights out of it. Now data science the framework, which is available, it's like a team sport. You can bring in many other data scientists into the organization who could have different analyst reports to go render for or provide results into. So being a team support, being a collaboration, bringing together with that clean data, I think it's going to change the world. I think the business side can have instant value from the data they going to see. >> Let me just test the edge conditions on that. Some of that data is streaming and you might apply the analytics in real time. Some of it is, I think as you were telling us before, sort of locked up as dark data. The question is how much of that data, the streaming stuff and the dark data, how much do you have to land in a Hadoop repository versus how much do you just push the analytics out too and have it inform a decision? >> Maybe I can take a first thought on it. I think there's a couple things in that. There's the learnings, and then how do I execute the learnings? I think the first step of it is, I tend to land the data, and going to the Telecom churn model, I want to see all the touch points. So I want to see the person that came through the website. He went into the store, he called into us, so I need to aggregate all that data to get a better view of what's the chain of steps that happened for somebody to churn? Once I end up diagnosing that, go through the data science of that, to learn the models that are being executed on that data, and that's the data at rest. What I want to do is build the model out so that now I can take that model, and I can prescriptively run it in this stream of data. So I know that that customer just hung up off the phone, now he walked in the store and we can sense that he's in the store because we just registered that he's asking about his billing details. The system can now dynamically diagnose by those two activities that this is a churn high-rate, so notify that teller in the store that there's a chance of him rolling out. If you look at that, that required the machine learning and data science side to build the analytical model, and it required the data-flow management and streaming analytics to consume that model to make a real-time insight out of it, to ultimately stop the churn from happening. Let's just give the customer a discount at the end of the day. That type of stuff; so you need to marry those two. >> It's interesting, you articulated that very clearly. Although then the question I have is now not on the technical side, but on the go-to market side. You guys have to work very very closely, and this is calling at a level that I assume is not very normal for Hortonworks, and it's something that is a natural sales motion for IBM. >> So maybe I'll first speak up, and then I'll let you add some color to that. When I look at it, I think there's a lot of natural synergies. IBM and Hortonworks have been partnered since day one. We've always continued on the path. If you look at it, and I'll bring up community again and open source again, but we've worked very well in the community. I think that's incubated a really strong and fostered a really strong relationship. I think at the end of the day we both look at what's going to be the outcome for the customer and working back from that, and we tend to really engage at that level. So what's the outcome and then how do we make a better product to get to that outcome? So I think there is a lot of natural synergies in that. I think to your point, there's lots of pieces that we need to integrate better together, and we will join that over time. I think we're already starting with the data science experience. A bunch of integration touchpoints there. I think you're going to see in the information governance space, with Atlas being a key underpinning and information governance catalog on top of that, ultimately moving up to IBM's unified governance, we'll start getting more synergies there as well and on the big sequel side. I think when you look at the different pods, there's a lot of synergies that our customers will be driving and that's what the driving factors, along with the organizations are very well aligned. >> And VPF engineering, so there's a lot of integration points which were already identified, and big sequel is already working really well on the Hortonworks HDP platform. We've got good integration going, but I think more and more on the data science. I think in end of the day we end up talking to very similar clients, so going as a joined go-to market strategy, it's a win-win. Jamie and I were talking earlier. I think in this type of a partnership, A our community is winning and our clients, so really good solutions. >> And that's what it's all about. Speaking of clients, you gave a great example with Telco. When we were talking to Rob Thomas and Rob Bearden earlier on in the program today. They talked about the data science conversation is at the C-suite, so walk us through an example of whether it's a Telco or maybe a healthcare organization, what is that conversation that you're having? How is a Telco helping foster what was announced today and this partnership? >> Madhu: Do you want to take em? >> Maybe I'll start. When we look in a Telco, I think there's a natural revolution, and when we start looking at that problem of how does a Telco consume and operate data science at a larger scale? So at the C-suite it becomes a people-process discussion. There's not a lot of tools currently that really help the people and process side of it. It's kind of an artist capability today in the data science space. What we're trying to do is, I think I mentioned team sport, but also give the tooling to say there's step one, which is we need to start learning and training the right teams and the right approach. Step two is start giving them access to the right data, etcetera to work through that. And step three, giving them all the tooling to support that, and tooling becomes things like TensorFlow etcetera, things like Zeppelin, Jupiter, a bunch of the open source community evolved capabilities. So first learn and training. The second step in that is give them the access to the right data to consume it, and then third, give them the right tooling. I think those three things are helping us to drive the right capabilities out of it. But to your point, elevating up to the C-suite. It's really they think people-process, and I think giving them the right tooling for their people and the right processes to get them there. Moving data science from an art to a science, is I would argue at a top level. >> On the client success side, how instrumental though are your clients, like maybe on the Telco side, in actually fostering the development of the technology, or helping IBM make the decision to standardize on HDP as their big data platform? >> Oh, huge, huge, a lot of our clients, especially as they are looking at the big data. Many of them are actually helping us get committers into the code. They're adding, providing; feet can't move fast enough in the engineering. They are coming up and saying, "Hey we're going to help" "and code up and do some code development with you." They've been really pushing our limits. A lot of clients, actually I ended up working with on the Hadoop site is like, you know for example. My entire information integration suite is very much running on top of HDP today. So they are saying, OK what's next? We want to see better integration. So as I called a few clients yesterday saying, "Hey, under embargo this is something going to get announced." Amazing, amazing results, and they're just very excited about this. So we are starting to get a lot of push, and actually the clients who do have large development community as well. Like a lot of banks today, they write a lot of their own applications. We're starting to see them co-developing stuff with us and becoming the committers. >> Lisa: You have a question? >> Well, if I just were to jump in. How do you see over time the mix of apps starting to move from completely custom developed, sort of the way the original big data applications were all written, down to the medal-ep in MapReduce. For shops that don't have a lot of data scientists, how are we going to see applications become more self-service, more pre-packaged? >> So maybe I'll give a little bit of perspective. Right now I think IBM has got really good synergies on what I'll call vertical solutions to vertical organizations, financial, etcetera. I would say, Hortonworks has took a more horizontal approach. We're more of a platform solution. An example of one where it's kind of marrying the two, is if you move up the stack from Hortonworks as a platform to the next level up, which is Hortonworks as a solution. One of the examples that we've invested heavily in is cybersecurity, and in an Apache project called Metron. Less about Metron and more about cybersecurity. People want to solve a problem. They want to defend an attacker immediately, and what that means is we need to give them out-of-the-box models to detect a lot of common patterns. What we're doing there, is we're investing in some of the data science and pre-packaged models to identify attack vectors and then try to resolve that or at least notify you that there's a concern. It's an example where the data science behind it, pre-packaging that data science to solve a specific problem. That's in the cybersecurity space and that case happens to be horizontal where Hortonwork's strength is. I think in the IBM case, there's a lot more vertical apps that we can apply to. Fraud, adjudication, etcetera. >> So it sounds like we're really just hitting the tip of the iceberg here, with the potential. We want to thank you both for joining us on theCUBE today, sharing your excitement about this deepening, expanding partnership between Hortonworks and IBM. Madhu and Jamie, thank you so much for joining George and I today on theCUBE. >> Thank you. >> Thank you Lisa and George. >> Appreciate it. >> Thank you. >> And for my co-host George Gilbert, I am Lisa Martin. You're watching us live on theCUBE, from day one of the DataWorks Summit in Silicon Valley. Stick around, we'll be right back. (digitalized music)