 From around the globe, it's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. Hey, welcome back to theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We're here with Madhu Kochar, who's the Vice President of 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, you know, they're facing more and more challenges, especially with the growing data complexity and also equality. What are they doing to 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 at 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, 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 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, it's 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. 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, automate 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. My dear, you know, that's so right on. And I think the business case is well understood. What's interesting 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 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 because the better the data, the better the AI, right? But it's still complicated. How are you guys looking at this perspective of managing data that exists in different forms? Yeah, no, 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 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. You know, I love notions 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've 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? It is in your 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 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, 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 in the early days, like, hey, the data is everywhere. People are concerned, you know, and there's compliance, there's 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 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 pack 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 pack 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 pack 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 that the data is discovered, how it's 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 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 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 Cloudpack for data with this new enhancements, how we are meeting 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 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, we've done with our intelligent data fabric is going to be our next generation of Cloudpack 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 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, it's very exciting, great insight and congratulations. I love the auto name and autopilot, auto AI, 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 connecting. So is this where auto privacy, auto catalog and auto SQL and AI 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 intra-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? How do I respect my data residency? So exactly to the point that is what this gives us the solution. That's awesome, Madhu, remember the old school? Inter-networking was a category in the industry, connecting networks together. Now we have inter-clouding, inter-data ops. So 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 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 Manager IBM Data and AI. The hottest area in hybrid cloud, of course, is the CUBE coverage for how you think 2021. I'm John Furrier. Thanks for watching.