>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, it's theCUBE. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer Symposium or Summit in San Francisco at the Parc 55 Hotel. We came up here last year. It's a really small event, very intimate, but a lot of practitioners sharing best practices and we're excited to have a really data-driven company represented, see Scott Zoldi, Chief Analytics Officer from FICO, Scott, great to see you. >> It's great to be here, thanks Jim. >> Absolutely. So, before we jump into it, I was just kind of curious. One of the things that comes up all the time, when we do Chief Data Officer and there's this whole structuring of how do people integrate data organizationally? Does it report to the CIO, the CEO? So, how have you guys done it, where do you report into in the FICO? >> So at FICO, when we work with data, it's generally going up through our CIO, but as part of that we have both the Chief Analytics Officer and the Chief Technology Officer that are also part of that responsibility of ensuring that we organize the data correctly, we have the proper governance in place, right, and the proper sort of concerns around privacy and security in place. >> Right, so you guys have been in the data business forever, I mean, data is your business, so when you hear all this talk about digital transformation and becoming more data-driven as a company, how does that impact a company like FICO? You guys have been doing this forever. What kind of opportunities are there to take, kind of, analytics to the next level? >> For us, I think it's really exciting. So, you're right, we've been at it for 60 years, right? And analytics is at the core of our business, and operationalizing out the data and around bringing better analytics into play. And now there's this new term, you know, Operationalizing Analytics. And so as we look at digital, we look at all the different types of data that are available to decisions and all the computation power that we have available today, it's really exciting now, to see the types of decisions that can be made with all the data and different types of analytics that are available today. >> Right, so what are some of those nuanced decisions? 'Cause, you know, from the outside world looking in, we see, kind of binary decisions, you know either I get approved for the card or not, or I get the unfortunate, you know you card didn't get through, we had a fraud event, I got to call and tell them please turn my card back on. Seems very binary, so as you get beyond the really simple binary, what are some of the things that you guys have been able to do with the business, having a much more obviously nuanced and rich set of data from which to work? >> So one of the things that we focus on is really around having a profile of each and every customer so we can make a better behavioral decision. So we're trying to understand behavior, ultimately, and that behavior can be manifested in terms of making a fraud decision, or a credit decision. But it's really around personalized analytics, essentially like an analytics of one, that allows us to understand that customer very, very well to make a decision around, what is the next sort of opportunity from a business perspective, a retention perspective, or improving that customer experience. Right, and then how much is it is your driving, could you talk about the operationalizing this? So there's operationalizing it inside the computers and the machines that are making judgements, and scoring things, and passing out decisions, versus more the human factor, the human touch. How do you divide which goes where? And how do you prioritize so that more people get more data from which to work with and make decisions, versus just the ones that are driven inside of an algorithm, inside of a machine? >> Yeah, it's a great point, because a lot of times organizations want to apply analytics to the data they have, but they haven't given a thought to the entire operization of that. So we generally look at it in four parts. One is around data, what is the data we need to make a decision, 'cause decisions always come first, business decisions. Where is that data, how do we gather it and then make it available? Next stage, what are the analytics that we want to apply? And that involves the time that we need to make a decision and how to make that decision over time. And then comes the people part, right? What is the process to work with that score, record the use of, let's say, an analytic, what was the outcome, was it more positive or based on using that analytic, right? And incorporating that back to make a change to the business over time, make actions over time in terms of improving that process, and that's a continual sort of process that you have to have when you operationalize analytics. Otherwise, this could be a one-off sort of analytic adventure, but not part of the core business. >> Right, and you don't want that. Now what about the other data, you know third-party data that you've brought in that isn't kind of part your guys' core? Obviously you have a huge corpus of your own internal data and through your partner financial institutions, but have you started to pull in more kind of third-party data, social data, other types of things to help you build that behavioral model? >> It kind of depends on the business that we're in and the region that we're in. Some regions, for example, outside the United States they're taking much more advantage of social data and social media, and even mobile data to make, let's say, credit decisions. But we generally are finding that most organizations aren't even looking that up, they already have it housed appropriately and to the maximum extent, and so that's usually where our focus is. Right, so to shift gears about the inside, and there's an interesting term, explainable AI, I've never heard that phrase, so what exactly, when you guys talk about explainable AI, what does that mean? Yeah, so machine-learning is kind of a very, very hot topic today and it's one that is focused on development of machine-learning models that learn relationships in data. And it means that you can leverage algorithms to make decisions based on collecting all this information. Now, the challenge is that these algorithms are much more intelligent than a human being, they're superhuman, but generally they're very difficult to understand how they made the decision, and how they came up with a score. So, explainable AI is around deconstructing and analyzing that model so we can provide examples and reasons for why the model scored the way it did. And that's actually paramount, because today we need to provide explanations as part of regulatory concerns around the use of these models, and so it's a very core part of that fact that as we operationalize analytics, and we use things like machine-learning and artificial intelligence, that explainability, the ability to say why did this model score me this way, is at front and center so we can have that dialogue with a customer and they can understand the reasons, and maybe improve the outcome in the future. >> Right, and was that driven primarily by regulations or because it just makes sense to be able to pull back the onion? On the other hand, as you said, the way machines learn and the way machines operate is very different than the way humans calculate, so maybe, I don't know if there's just some stuff in there that's just not going to make sense to a person. So how do you kind of square that circle? >> So, for us our journey to explainable AI started in the early 90s, so it's always been core to our business because, as you say, it makes common sense that you need to be able to explain that score, and if you're going to have a conversation with the customer. You know, since that time, machine-learning's become much more mainstream. There's over 2,000 start-up companies today all trying to apply machine-learning and AI. >> Right. >> And that's where regulation is coming in, because in the early days we used explainable AI to make sure we understood what the model did, how to explain it to our governance teams, how to explain it to our customers, and the customers explain it to their clients, right? Today, it's around having regulation to make sure that machine-learning and artificial intelligence is used responsibly in business. >> Yeah, it's pretty amazing, and that's why I think we hear so much about augmented intelligence as opposed to artificial intelligence, there's nothing artificial about it. It's very different, but it really is trying to add to, you know, provide a little bit more data, a little bit more structure, more context to people that are trying to make decisions. >> And that's critically important because, you know, very often, the AI or machine-learning will make a decision differently than we will, so it can add some level of insight to us, but we always need that human factor in there to kind of validate the reasons, the explanations, and then make sure that we have that kind of human judgment that's running alongside. >> Right, right. So I can't believe I'm going to sit here and say that it's, whatever it is, May 15th today, the year's almost halfway over. But what are some of your priorities for the balance of the year, what are some of the things you are working on as you look forward? Obviously, FICO's a big data-driven company, you guys have a ton of data, you're in a ton of transactions so you've got kind of a front edge of this whole process. What are you looking at, what are some of your short-term priorities, mid-term priorities, as you move through the balance of the year and into next year? >> So number one is around explainable AI, right? And really helping organizations get that ability to explain their models. We're also focused very much around bringing more of the unsupervised analytic technologies to the market. So, very often when you build a model, you have a set of data and a set of outcomes, and you train that model, and you have a model that makes prediction. But more and more, we have parts of our businesses today that where unsupervised analytic models are much more important, in areas like-- >> What does that mean, unsupervised analytics models? >> So, essentially what it means is we're trying to look for patterns that are not normal, unlike any other customers. So if you think about a money launderer, there's going to be very few people that will behave like a money launderer, or an insider, or something along those lines. And so, by building really, really good models of predicting normal behavior any deviation or a mis-prediction from that model could point to something that's very abnormal, and something that should be investigated. And very often, we use those in areas of cyber-security crimes, blatant money laundering, insider fraud, in areas like that where you're not going to have a lot of outcome data, of data to train on, but you need to still make the decisions. >> Wow. Which is really hard for a computer, right? That's the opposite of the types of problems that they like. They like a lot of, a lot of, of revs. >> Correct, so that's why the focus is on understanding good behavior really, really well. And anything different than what it thinks is good could be potentially valuable. >> Alright, Scott, well keep track of all of our scores, we all depend on it. (laughs) >> Scott: We all do. >> Thanks for taking a few minutes out of your day. >> Scott: Appreciate it. >> Alright, he's Scott, I'm Jeff, you are watching theCUBE from San Francisco. Thanks for watching. (upbeat electronic music)

>> Announcer: From the Corinium Chief Analytics Officer Conference. Spring, San Francisco, it's theCUBE! >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event 2018. About 100 people, really intimate, a lot of practitioners sharing best practices about how they got started, how are they really leveraging data and becoming digitally transformed, analytically driven, data driven. We're excited to have Vishal Morde. He's the VP of Data Science at Barclays, welcome. >> Glad to be here, yeah. >> Absolutely. So we were just talking about Philly, you're back in Delaware, and you actually had a session yesterday talking about Barclays journey. So I was wondering if you could share some of the highlights of that story with us. >> Absolutely, so I had a talk, I opened the conference with data science journey at Barclays. And, we have been on this journey for five years now where we transform our data and analytics practices and really harness the power of Big Data, Machine Learning, and advanced analytics. And the whole idea was to use this power of, newly found power that we have, to make the customer journey better. Better through predictive models, better through deeper and richer consumer insights and better through more personalized customer experience. So that is the sole bet. >> Now it's interesting because we think of financial services as being a data driven, organization already. You guys are way ahead Obviously Wall Street's trading on microseconds. What was different about this digital transformation than what you've been doing for the past? >> I think the key was, we do have all the data in the world. If you think about it, banks know everything about you, right? We have our demographic data, behaviors data. From very granular credit card transactions data, we have your attitudal data, but what we quickly found out that we did not have a strategy to use that data well. To improve our our productivity, profitability of a business and make the customer experience better. So what we did was step one was developing a comprehensive data strategy and that was all about organizing, democratizing, and monetizing our data assets. And step towards, then we went about the monetization part in a very disciplined way. We built a data science lab where we can quickly do a lot of rapid prototyping, look at any idea in machine learning data science, incubate it, validate it, and finally, it was ready for production. >> So I'm curious on that first stage, so you've got all this data, you've been collecting it forever, suddenly now you're going to take an organized approach to it. What'd you find in that first step when you actually tried to put a little synthesis and process around what you already had? >> Well the biggest challenge was, the data came from different sources. So we do have a lot of internal data assets, but we are in the business where we do have to get a lot of external data. Think about credit bureau's, right? Also we have a co-brand business, where we work with partners like Uber, imagine the kind of data we get from them, we have data from American Airlines. So our idea was to create a data governance structure of, we formed a Chief Data Office, the officer forum, we got all the people across our organization to understand the value of data. We are a data driven company as you said but, it took us a while to take that approach and importance of data, and then, data analytics need to be embedded in the organizational DNA, and that's what we're going to focus on first. Data awareness of importance of data, importance of governance as well, and then we could think about democratizing and monetizing, organization's the key for us. >> Right, right, well so how did you organize, how has the Chief Data Officer, what did he or she, who did he or she report to, how did you organize? >> Right, so it was directly reporting to our CEO. >> Jeff: Into the CEO, not into the CIO? >> Not into the CIO. We had a technology office, we do kind of, have a line-of-sight or adopted line with technology, and we made sure that that office has a lot of high-level organization buy-in, they are given budgets to make sure the data governance was in place, key was to get data ownership going. We were using a lot of data, but there was no data ownership. And that was the key, once we know that, who actually owned this data, then you can establish a governance framework, then you can establish how you use this data, and then, how to be monetized. >> So who owned it before you went through this exercise, just kind of, it was just kind of there? >> Yeah, there wasn't a clear ownership, and that's the key for us. Once you establish ownership, then it becomes an asset, we were not treating data as an asset, so there was a change in, kind of mindset, that we had to go through, that data is an asset, and it was used as a means to an end, rather than an asset. >> Right, well what about the conflict with the governance people, I'm sure there was a lot of wait, wait, wait, we just can't open this up to anybody, I'm sure it's a pretty interesting discussion because you have to open it up to more people, but you still have to obviously follow the regs. >> Right, and that's where there are a lot of interesting advancement in data science, where, in the area of data governance, there are new tools out there which lets you track who's actually accessing your data. Once we had that infrastructure, then you can start figuring out okay, how do we allow access, how do we actually proliferate that data across different levels of the organization? Because data needs to be in the hands of decision makers, no matter who they are, could be our CEO, to somebody who's taking our phone calls. So that democratization piece became so important, then we can think about how do you-- you can't directly jump into monetization phase before you get your, all the ducks in order. >> So what was the hardest part, the biggest challenge, of that first phase in organizing the data? >> Creating that 360 degree view on our customers, we had a lot of interesting internal data assets, but we were missing big pieces of the puzzles, where we're looking at, you're trying to create a 360 degree view on a customer, it does take a while to get that right, and that's where the data, setting up the data governance piece, setting up the CDO office, those are the more painful, more difficult challenges, but they lay the foundation for all the the work that we wanted to do, and it allowed to us to kind of think through more methodically about our problems and establish a foundation that we can now, we can take any idea and use it, and monetize it for you. >> So it's interesting you, you said you've been on this journey for five years, so, from zero to a hundred, where are you on your journey do you think? >> Right, I think we're just barely scratching the surface, (both laughing) - I knew you were going to say that >> Because I do feel that, the data science field itself is evolving, I look at data science as like ever-evolving, ever-mutating kind of beast, right? And we just started our journey, I think we are off to a good start, we have really good use-cases, we have starting using the data well, we have established importance of data, and now we are operationalized on the machine learning data science projects as well. So that's been great, but I do feel there's a lot of untapped potential in this, and I think it'll only get better. >> What about on the democratization, we just, in the keynote today there was a very large retailer, I think he said he had 50 PhDs on staff and 150 data centers this is a multi-billion dollar retailer. How do you guys deal with resource constraints of your own data science team versus PhDs, and trying to democratize the decision making out to a much broader set of people? >> So I think the way we've thought about this is think big, but start small. And what we did was, created a data science lab, so what it allowed is to kind of, and it was the cross-functional team of data scientists, data engineers, software developers kind of working together, and that is a primary group. And they were equally supported by your info-sec guys, or data governance folks, so, they're a good support group as well. And with that cross-functional team, now we are able to move from generating an idea, to incubating it, making sure it has a true commercial value and once we establish that, then we'll even move forward operationalization, so it was more surgical approach rather than spending millions and millions of dollars on something that we're not really sure about. So that did help us to manage a resource constraint now, only the successful concepts were actually taken through operationalization, and we before, we truly knew the bottom line impact, we could know that, here's what it means for us, and for consumers, so that's the approach that we took. >> So, we're going to leave it there, but I want to give you the last word, what advice would give for a peer, not in the financial services industry, they're not watching this. (both laugh) But you know, in terms of doing this journey, 'cause it's obviously, it's a big investment, you've been at it for five years, you're saying you barely are getting started, you're in financial services, which is at it's base, basically an information technology industry. What advice do you give your peers, how do they get started, what do they do in the dark days, what's the biggest challenge? >> Yeah, I feel like my strong belief is, data science is a team sport, right? A lot of people come and ask me: how do we find these unicorn data scientist, and my answer always being that, they don't exist, they're figments of imagination. So it's much better to take cross-functional team, with a complimentary kind of skill set, and get them work together, how do you fit different pieces of the puzzle together, will determine the success of the program. Rather than trying to go really big into something, so that's, the team sport is the key concept here, and if I can get the word out across, that'll be really valuable. >> Alright, well thanks for sharin' that, very useful piece of insight! >> Vishal: Absolutely! >> Alright thanks Vishal, I'm Jeff Frick, you are watching theCUBE, from the Corinium Chief Analytic Officer summit, San Francisco, 2018, at the Parc 55, thanks for watching! (bubbly music plays)

(techno music) >> Announcer: From the Corinium Chief Analytics Officer Conference Spring San Francisco. It's theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Parc 55 Hotel at the Corinium Chief Analytics Officer Spring 2018 event, about 100 people, pretty intimate affair. A lot of practitioners here talking about the challenges of Big Data and the challenges of Analytics. We're really excited to have a very special Cube guest. I think he was the first guy to launch his company on theCUBE. It was Big Data New York City 2013. I remember it distinctly. It's Prakash Nanduri, the co-founder and CEO of Paxata. Great to see you. >> Great seeing you. Thank you for having me back. >> Absolutely. You know we got so much mileage out of that clip. We put it on all of our promotional materials. You going to launch your company? Launch your company on theCUBE. >> You know it seems just like yesterday but it's been a long ride and it's been a fantastic ride. >> So give us just a quick general update on the company, where you guys are now, how things are going. >> Things are going fantastic. We continue to grow. If you recall, when we launched, we launched the whole notion of democratization of information in the enterprise with self service data prep. We have gone onto now delivered real value to some of the largest brands in the world. We're very proud that 2017 was the year when massive amount of adoption of Paxata's adaptive information platform was taken across multiple industries, financial services, retail, CPG, high tech, in the OIT space. So, we just keep growing and it's the usual challenges of managing growth and managing, you know, the change in the company as you, as you grow from being a small start-up to know being a real company. >> Right, right. There's good problems and bad problems. Those are the good problems. >> Yes, yes. >> So, you know, we do so many shows and there's two big themes over and over and over like digital transformation which gets way over used and then innovation and how do you find a culture of innovation. In doing literally thousands of these interviews, to me it seems pretty simple. It is about democratization. If you give more people the data, more people the tools to work with the data, and more people the power to do something once they find something in the data, and open that up to a broader set of people, they're going to find innovations, simply the fact of doing it. But the reality is those three simple steps aren't necessarily very easy to execute. >> You're spot on, you're spot on. I like to say that when we talk about digital transformation the real focus should be on the deed . And it really centers around data and it centers around the whole notion of democratization, right? The challenge always in large enterprises is democratization without governance becomes chaos. And we always need to focus on democratization. We need to focus on data because as we all know data is the new oil, all of that, and governance becomes a critical piece too. But as you recall, when we launched Paxata, the entire vision from day one has been while the entire focus around digitization covers many things right? It covers people processes. It covers applications. It's a very large topic, the whole digital transformation of enterprise. But the core foundation to digital transformation, data democratization governance, but the key issue is the companies that are going to succeed are the companies that turn data into information that's relevant for every digital transformation effort. >> Right, right. >> Because if you do not turn raw data into information, you're just dealing with raw data which is not useful >> Jeff: Right >> And it will not be democratized. >> Jeff: Right >> Because the business will only consume the information that is contextual to their need, the information that's complete and the information that is clean. >> Right, right. >> So that's really what we're driving towards. >> And that's interesting 'cause the data, there's so many more sources of data, right? There's data that you control. There's structured data, unstructured data. You know, I used to joke, just the first question when you'd ask people "Where's your data?", half the time they couldn't even, they couldn't even get beyond that step. And that's before you start talking about cleaning it and making it ready and making it available. Before you even start to get into governance and rights and access so it's a really complicated puzzle to solve on the backend. >> I think it starts with first focusing on what are the business outcomes we are driving with digital transformation. When you double-click on digital transformation and then you start focusing on data and information, there's a few things that come to fore. First of all, how do I leverage information to improve productivity in my company? There's multiple areas, whether it is marketing or supply chain or whatever. The second notion is how do I ensure that I can actually transform the culture in my company and attract the brightest and the best by giving them the the environment where democratization of information is actually reality, where people feel like they're empowered to access data and turn it into information and then be able to do really interesting things. Because people are not interested on being subservient to somebody who gives them the data. They want to be saying "Give it to me. "I'm smart enough. "I know analytics. "I think analytically and I want to drive my career forward." So the second thing is the cultural aspect to it. And the last thing, which is really important is every company, regardless of whether you're making toothpicks or turbines, you are looking to monetize data. So it's about productivity. It's about cultural change and attracting of talent. And it's about monetization. And when it comes to monetization of data, you cannot be satisfied with only covering enterprise data which is sitting in my enterprise systems. You have to be able to focus on, oh, how can I leverage the IOT data that's being generated from my products or widgets. How can I generate social immobile? How can I consume that? How can I bring all of this together and get the most complete insight that I need for my decision-making process? >> Right. So, I'm just curious, how do you see it your customers? So this is the chief analytics officer, we go to chief data officer, I mean, there's all these chief something officers that want to get involved in data and marketing is much more involved with it. Forget about manufacturing. So when you see successful cultural change, what drives that? Who are the people that are successful and what is the secret to driving the cultural change that we are going to be data-driven, we are going to give you the tools, we are going to make the investment to turn data which historically was even arguably a liability 'cause it had to buy a bunch o' servers to stick it on, into that now being an asset that drives actionable outcomes? >> You know, recently I was having this exact discussion with the CEO of one of the largest financial institutions in the world. This gentleman is running a very large financial services firm, is dealing with all the potential disruption where they're seeing completely new type of PINTEC products coming in, the whole notion of blockchain et cetera coming in. Everything is changing. Everything looks very dramatic. And what we started talking about is the first thing as the CEO that we always focus on is do we have the right people? And do we have the people that are motivated and driven to basically go and disrupt and change? For those people, you need to be able to give them the right kind of tools, the right kind of environment to empower them. This doesn't start with lip service. It doesn't start about us saying "We're going to be on a digital transformation journey" but at the same time, your data is completely in silos. It's locked up. There is 15,000 checks and balances before I can even access a simple piece of data and third, even when I get access to it, it's too little, too late or it's garbage in, garbage out. And that's not the culture. So first, it needs to be CEO drive, top down. We are going to go through digital transformation which means we are going to go through a democratization effort which means we are going to look at data and information as an asset and that means we are not only going to be able to harness these assets, but we're also going to monetize these assets. How are we going to do it? It depends very much on the business you're in, the vertical industry you play in, and your strengths and weaknesses. So each company has to look at it from their perspective. There's no one size fits all for everyone. >> Jeff: Right. >> There are some companies that have fantastic cultures of empowerment and openness but they may not have the right innovation or the right kind of product innovation skills in place. So it's about looking at data across the board. First from your culture and your empowerment, second about democratization of information which is where a company like Paxata comes in, and third, along with democratization, you have to focus on governance because we are for-profit companies. We have a fiducial responsibility to our customers and our regulators and therefore we cannot have democratization without governance. >> Right, right >> And that's really what our biggest differentiation is. >> And then what about just in terms of the political play inside the company. You know, on one hand, used to be if you held the information, you had the power. And now that's changed really 'cause there's so much information. It's really, if you are the conduit of information to help people make better decisions, that's actually a better position to be. But I'm sure there's got to be some conflicts going through digital transformation where I, you know, I was the keeper of the kingdom and now you want to open that up. Conversely, it must just be transformational for the people on the front lines that finally get the data that they've been looking for to run the analysis that they want to rather than waiting for the weekly reports to come down from on high. >> You bet. You know what I like to say is that if you've been in a company for 10, 15 years and if you felt like a particular aspect, purely selfishly, you felt a particular aspect was job security, that is exactly what's going to likely make you lose your job today. What you thought 10 years ago was your job security, that's exactly what's going to make you lose your job today. So if you do not disrupt yourself, somebody else will. So it's either transform yourself or not. Now this whole notion of politics and you know, struggle within the company, it's been there for as long as, humans generally go towards entropy. So, if you have three humans, you have all sort of issues. >> Jeff: Right, right. >> The issue starts frankly with leadership. It starts with the CEO coming down and not only putting an edict down on how things will be done but actually walking the walk with talking the talk. If, as a CEO, you're not transparent, it you're not trusting your people, if you're not sharing information which could be confidential, but you mention that it's confidential but you have to keep this confidential. If you trust your people, you give them the ability to, I think it's a culture change thing. And the second thing is incentivisation. You have to be able to focus on giving people the ability to say "by sharing my data, "I actually become a hero." >> Right, right. >> By giving them the actual credit for actually delivering the data to achieve an outcome. And that takes a lot of work. But if you do not actually drive the cultural change, you will not drive the digital transformation and you will not drive the democratization of information. >> And have you seen people try to do it without making the commitment? Have you seen 'em pay the lip service, spend a few bucks, start a project but then ultimately they, they hamstring themselves 'cause they're not actually behind it? >> Look, I mean, there's many instances where companies start on digital transformation or they start jumping into cool terms like AI or machine-learning, and there's a small group of people who are kind of the elites that go in and do this. And they're given all the kind of attention et cetera. Two things happen. Because these people who are quote, unquote, the elite team, either they are smart but they're not able to scale across the organization or many times, they're so good, they leave. So that transformation doesn't really get democratized. So it is really important from day one to start a culture where you're not going to have a small group of exclusive data scientists. You can have those people but you need to have a broader democratization focus. So what I have seen is many of the siloed, small, tight, mini science projects end up failing. They fail because number one, either the business outcome is not clearly identified early on or two, it's not scalable across the enterprise. >> Jeff: Right. >> And a majority of these exercises fail because the whole information foundation that is taking raw data turning it into clean, complete, potential consumable information, to feed across the organization, not just for one siloed group, not just one data science team. But how do you do that across the company? That's what you need to think from day one. When you do these siloed things, these departmental things, a lot of times they can fail. Now, it's important to say "I will start with a couple of test cases" >> Jeff: Right, right. >> "But I'm going to expand it across "from the beginning to think through that." >> So I'm just curious, your perspective, is there some departments that are the ripest for being that leading edge of the digital transformation in terms of, they've got the data, they've got the right attitude, they're just a short step away. Where have you seen the great place to succeed when you're starting on kind of a smaller PLC, I don't know if you'd say PLC, project or department level? >> So, it's funny but you will hear this, it's not rocket science. Always they say, follow the money. So, in a business, there are three incentives, making more money, saving money, or staying out of jail. (laughs) >> Those are good. I don't know if I'd put them in that order but >> Exactly, and you know what? Depending on who are you are, you may have a different order but staying out of jail if pretty high on my list. >> Jeff: I'm with you on that one. >> So, what are the ambiants? Risk and compliance. Right? >> Jeff: Right, right. >> That's one of those things where you absolutely have to deliver. You absolutely have to do it. It's significantly high cost. It's very data and analytic centric and if you find a smart way to do it, you can dramatically reduce your cost. You can significantly increase your quality and you can significantly increase the volume of your insights and your reporting, thereby achieving all the risk and compliance requirements but doing it in a smarter way and a less expensive way. >> Right. >> That's where incentives have really been high. Second, in making money, it always comes down to sales and marketing and customer success. Those are the three things, sales, marketing, and customer success. So most of our customers who have been widely successful, are the ones who have basically been able to go and say "You know what? "It used to take us eight months "to be able to even figure out a customer list "for a particular region. "Now it takes us two days because of Paxata "and because of the data prep capabilities "and the governance aspects." That's the power that you can deliver today. And when you see one person who's a line of business person who says "Oh my God. "What used to take me eight months, "now it's done in half a day". Or "What use to take me 22 days to create a report, "is now done in 45 minutes." All of a sudden, you will not have a small kind of trickle down, you will have a tsunami of democratization with governance. That's what we've seen in our customers. >> Right, right. I love it. And this is just so classic too. I always like to joke, you know, back in the day, you would run your business based on reports from old data. Now we want to run your business with stuff you can actually take action on now. >> Exactly. I mean, this is public, Shameek Kundu, the chief data officer of Standard Chartered Bank and Michael Gorriz who's the global CIO of Standard Chartered Bank, they have embraced the notion that information democratization in the bank is a foundational element to the digital transformation of Standard Chartered. They are very forward thinking and they're looking at how do I democratize information for all our 87,500 employees while we maintain governance? And another major thing that they are looking at is they know that the data that they need to manipulate and turn into information is not sitting only on premise. >> Right, right. >> It's sitting across a multi-cloud world and that's why they've embraced the Paxata information platform to be their information fabric for a multi-cloud hybrid world. And this is where we see successes and we're seeing more and more of this, because it starts with the people. It starts with the line of business outcomes and then it starts with looking at it from scale. >> Alright, Prakash, well always great to catch up and enjoy really watching the success of the company grow since you launched it many moons ago in New York City >> yes Fantastic. Always a pleasure to come back here. Thank you so much. >> Alright. Thank you. He's Prakash, I'm Jeff Frick. You're watching theCUBE from downtown San Francisco. Thanks for watching. (techno music)

(techno music) >> Announcer: Live, from downtown San Francisco, it's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody, this is theCUBE, the leader in live tech coverage. We're here at the Parc 55 in San Francisco covering the IBM CDO Strategy Summit. I'm here with Joel Horwitz who's the Vice President of Digital Partnerships & Offerings at IBM. Good to see you again Joel. >> Thanks, great to be here, thanks for having me. >> So I was just, you're very welcome- It was just, let's see, was it last month, at Think? >> Yeah, it's hard to keep track, right. >> And we were talking about your new role- >> It's been a busy year. >> the importance of partnerships. One of the things I want to, well let's talk about your role, but I really want to get into, it's innovation. And we talked about this at Think, because it's so critical, in my opinion anyway, that you can attract partnerships, innovation partnerships, startups, established companies, et cetera. >> Joel: Yeah. >> To really help drive that innovation, it takes a team of people, IBM can't do it on its own. >> Yeah, I mean look, IBM is the leader in innovation, as we all know. We're the market leader for patents, that we put out each year, and how you get that technology in the hands of the real innovators, the developers, the longtail ISVs, our partners out there, that's the challenging part at times, and so what we've been up to is really looking at how we make it easier for partners to partner with IBM. How we make it easier for developers to work with IBM. So we have a number of areas that we've been adding, so for example, we've added a whole IBM Code portal, so if you go to developer.ibm.com/code you can actually see hundreds of code patterns that we've created to help really any client, any partner, get started using IBM's technology, and to innovate. >> Yeah, and that's critical, I mean you're right, because to me innovation is a combination of invention, which is what you guys do really, and then it's adoption, which is what your customers are all about. You come from the data science world. We're here at the Chief Data Officer Summit, what's the intersection between data science and CDOs? What are you seeing there? >> Yeah, so when I was here last, it was about two years ago in 2015, actually, maybe three years ago, man, time flies when you're having fun. >> Dave: Yeah, the Spark Summit- >> Yeah Spark Technology Center and the Spark Summit, and we were here, I was here at the Chief Data Officer Summit. And it was great, and at that time, I think a lot of the conversation was really not that different than what I'm seeing today. Which is, how do you manage all of your data assets? I think a big part of doing good data science, which is my kind of background, is really having a good understanding of what your data governance is, what your data catalog is, so, you know we introduced the Watson Studio at Think, and actually, what's nice about that, is it brings a lot of this together. So if you look in the market, in the data market, today, you know we used to segment it by a few things, like data gravity, data movement, data science, and data governance. And those are kind of the four themes that I continue to see. And so outside of IBM, I would contend that those are relatively separate kind of tools that are disconnected, in fact Dinesh Nirmal, who's our engineer on the analytic side, Head of Development there, he wrote a great blog just recently, about how you can have some great machine learning, you have some great data, but if you can't operationalize that, then really you can't put it to use. And so it's funny to me because we've been focused on this challenge, and IBM is making the right steps, in my, I'm obviously biased, but we're making some great strides toward unifying the, this tool chain. Which is data management, to data science, to operationalizing, you know, machine learning. So that's what we're starting to see with Watson Studio. >> Well, I always push Dinesh on this and like okay, you've got a collection of tools, but are you bringing those together? And he flat-out says no, we developed this, a lot of this from scratch. Yes, we bring in the best of the knowledge that we have there, but we're not trying to just cobble together a bunch of disparate tools with a UI layer. >> Right, right. >> It's really a fundamental foundation that you're trying to build. >> Well, what's really interesting about that, that piece, is that yeah, I think a lot of folks have cobbled together a UI layer, so we formed a partnership, coming back to the partnership view, with a company called Lightbend, who's based here in San Francisco, as well as in Europe, and the reason why we did that, wasn't just because of the fact that Reactive development, if you're not familiar with Reactive, it's essentially Scala, Akka, Play, this whole framework, that basically allows developers to write once, and it kind of scales up with demand. In fact, Verizon actually used our platform with Lightbend to launch the iPhone 10. And they show dramatic improvements. Now what's exciting about Lightbend, is the fact that application developers are developing with Reactive, but if you turn around, you'll also now be able to operationalize models with Reactive as well. Because it's basically a single platform to move between these two worlds. So what we've continued to see is data science kind of separate from the application world. Really kind of, AI and cloud as different universes. The reality is that for any enterprise, or any company, to really innovate, you have to find a way to bring those two worlds together, to get the most use out of it. >> Fourier always says "Data is the new development kit". He said this I think five or six years ago, and it's barely becoming true. You guys have tried to make an attempt, and have done a pretty good job, of trying to bring those worlds together in a single platform, what do you call it? The Watson Data Platform? >> Yeah, Watson Data Platform, now Watson Studio, and I think the other, so one side of it is, us trying to, not really trying, but us actually bringing together these disparate systems. I mean we are kind of a systems company, we're IT. But not only that, but bringing our trained algorithms, and our trained models to the developers. So for example, we also did a partnership with Unity, at the end of last year, that's now just reaching some pretty good growth, in terms of bringing the Watson SDK to game developers on the Unity platform. So again, it's this idea of bringing the game developer, the application developer, in closer contact with these trained models, and these trained algorithms. And that's where you're seeing incredible things happen. So for example, Star Trek Bridge Crew, which I don't know how many Trekkies we have here at the CDO Summit. >> A few over here probably. >> Yeah, a couple? They're using our SDK in Unity, to basically allow a gamer to use voice commands through the headset, through a VR headset, to talk to other players in the virtual game. So we're going to see more, I can't really disclose too much what we're doing there, but there's some cool stuff coming out of that partnership. >> Real immersive experience driving a lot of data. Now you're part of the Digital Business Group. I like the term digital business, because we talk about it all the time. Digital business, what's the difference between a digital business and a business? What's the, how they use data. >> Joel: Yeah. >> You're a data person, what does that mean? That you're part of the Digital Business Group? Is that an internal facing thing? An external facing thing? Both? >> It's really both. So our Chief Digital Officer, Bob Lord, he has a presentation that he'll give, where he starts out, and he goes, when I tell people I'm the Chief Digital Officer they usually think I just manage the website. You know, if I tell people I'm a Chief Data Officer, it means I manage our data, in governance over here. The reality is that I think these Chief Digital Officer, Chief Data Officer, they're really responsible for business transformation. And so, if you actually look at what we're doing, I think on both sides is we're using data, we're using marketing technology, martech, like Optimizely, like Segment, like some of these great partners of ours, to really look at how we can quickly A/B test, get user feedback, to look at how we actually test different offerings and market. And so really what we're doing is we're setting up a testing platform, to bring not only our traditional offers to market, like DB2, Mainframe, et cetera, but also bring new offers to market, like blockchain, and quantum, and others, and actually figure out how we get better product-market fit. What actually, one thing, one story that comes to mind, is if you've seen the movie Hidden Figures- >> Oh yeah. >> There's this scene where Kevin Costner, I know this is going to look not great for IBM, but I'm going to say it anyways, which is Kevin Costner has like a sledgehammer, and he's like trying to break down the wall to get the mainframe in the room. That's what it feels like sometimes, 'cause we create the best technology, but we forget sometimes about the last mile. You know like, we got to break down the wall. >> Where am I going to put it? >> You know, to get it in the room! So, honestly I think that's a lot of what we're doing. We're bridging that last mile, between these different audiences. So between developers, between ISVs, between commercial buyers. Like how do we actually make this technology, not just accessible to large enterprise, which are our main clients, but also to the other ecosystems, and other audiences out there. >> Well so that's interesting Joel, because as a potential partner of IBM, they want, obviously your go-to-market, your massive company, and great distribution channel. But at the same time, you want more than that. You know you want to have a closer, IBM always focuses on partnerships that have intrinsic value. So you talked about offerings, you talked about quantum, blockchain, off-camera talking about cloud containers. >> Joel: Yeah. >> I'd say cloud and containers may be a little closer than those others, but those others are going to take a lot of market development. So what are the offerings that you guys are bringing? How do they get into the hands of your partners? >> I mean, the commonality with all of these, all the emerging offerings, if you ask me, is the distributed nature of the offering. So if you look at blockchain, it's a distributed ledger. It's a distributed transaction chain that's secure. If you look at data, really and we can hark back to say, Hadoop, right before object storage, it's distributed storage, so it's not just storing on your hard drive locally, it's storing on a distributed network of servers that are all over the world and data centers. If you look at cloud, and containers, what you're really doing is not running your application on an individual server that can go down. You're using containers because you want to distribute that application over a large network of servers, so that if one server goes down, you're not going to be hosed. And so I think the fundamental shift that you're seeing is this distributed nature, which in essence is cloud. So I think cloud is just kind of a synonym, in my opinion, for distributed nature of our business. >> That's interesting and that brings up, you're right, cloud and Big Data/Hadoop, we don't talk about Hadoop much anymore, but it kind of got it all started, with that notion of leave the data where it is. And it's the same thing with cloud. You can't just stuff your business into the public cloud. You got to bring the cloud to your data. >> Joel: That's right. >> But that brings up a whole new set of challenges, which obviously, you're in a position just to help solve. Performance, latency, physics come into play. >> Physics is a rough one. It's kind of hard to avoid that one. >> I hear your best people are working on it though. Some other partnerships that you want to sort of, elucidate. >> Yeah, no, I mean we have some really great, so I think the key kind of partnership, I would say area, that I would allude to is, one of the things, and you kind of referenced this, is a lot of our partners, big or small, want to work with our top clients. So they want to work with our top banking clients. They want, 'cause these are, if you look at for example, MaRisk and what we're doing with them around blockchain, and frankly, talk about innovation, they're innovating containers for real, not virtual containers- >> And that's a joint venture right? >> Yeah, it is, and so it's exciting because, what we're bringing to market is, I also lead our startup programs, called the Global Entrepreneurship Program, and so what I'm focused on doing, and you'll probably see more to come this quarter, is how do we actually bridge that end-to-end? How do you, if you're startup or a small business, ultimately reach that kind of global business partner level? And so kind of bridging that, that end-to-end. So we're starting to bring out a number of different incentives for partners, like co-marketing, so I'll help startups when they're early, figure out product-market fit. We'll give you free credits to use our innovative technology, and we'll also bring you into a number of clients, to basically help you not burn all of your cash on creating your own marketing channel. God knows I did that when I was at a start-up. So I think we're doing a lot to kind of bridge that end-to-end, and help any partner kind of come in, and then grow with IBM. I think that's where we're headed. >> I think that's a critical part of your job. Because I mean, obviously IBM is known for its Global 2000, big enterprise presence, but startups, again, fuel that innovation fire. So being able to attract them, which you're proving you can, providing whatever it is, access, early access to cloud services, or like you say, these other offerings that you're producing, in addition to that go-to-market, 'cause it's funny, we always talk about how efficient, capital efficient, software is, but then you have these companies raising hundreds of millions of dollars, why? Because they got to do promotion, marketing, sales, you know, go-to-market. >> Yeah, it's really expensive. I mean, you look at most startups, like their biggest ticket item is usually marketing and sales. And building channels, and so yeah, if you're, you know we're talking to a number of partners who want to work with us because of the fact that, it's not just like, the direct kind of channel, it's also, as you kind of mentioned, there's other challenges that you have to overcome when you're working with a larger company. for example, security is a big one, GDPR compliance now, is a big one, and just making sure that things don't fall over, is a big one. And so a lot of partners work with us because ultimately, a number of the decision makers in these larger enterprises are going, well, I trust IBM, and if IBM says you're good, then I believe you. And so that's where we're kind of starting to pull partners in, and pull an ecosystem towards us. Because of the fact that we can take them through that level of certification. So we have a number of free online courses. So if you go to partners, excuse me, ibm.com/partners/learn there's a number of blockchain courses that you can learn today, and will actually give you a digital certificate, that's actually certified on our own blockchain, which we're actually a first of a kind to do that, which I think is pretty slick, and it's accredited at some of the universities. So I think that's where people are looking to IBM, and other leaders in this industry, is to help them become experts in their, in this technology, and especially in this emerging technology. >> I love that blockchain actually, because it's such a growing, and interesting, and innovative field. But it needs players like IBM, that can bring credibility, enterprise-grade, whether it's security, or just, as I say, credibility. 'Cause you know, this is, so much of negative connotations associated with blockchain and crypto, but companies like IBM coming to the table, enterprise companies, and building that ecosystem out is in my view, crucial. >> Yeah, no, it takes a village. I mean, there's a lot of folks, I mean that's a big reason why I came to IBM, three, four years ago, was because when I was in start-up land, I used to work for H20, I worked for Alpine Data Labs, Datameer, back in the Hadoop days, and what I realized was that, it's an opportunity cost. So you can't really drive true global innovation, transformation, in some of these bigger companies because there's only so much that you can really kind of bite off. And so you know at IBM it's been a really rewarding experience because we have done things like for example, we partnered with Girls Who Code, Treehouse, Udacity. So there's a number of early educators that we've partnered with, to bring code to, to bring technology to, that frankly, would never have access to some of this stuff. Some of this technology, if we didn't form these alliances, and if we didn't join these partnerships. So I'm very excited about the future of IBM, and I'm very excited about the future of what our partners are doing with IBM, because, geez, you know the cloud, and everything that we're doing to make this accessible, is bar none, I mean, it's great. >> I can tell you're excited. You know, spring in your step. Always a lot of energy Joel, really appreciate you coming onto theCUBE. >> Joel: My pleasure. >> Great to see you again. >> Yeah, thanks Dave. >> You're welcome. Alright keep it right there, everybody. We'll be back. We're at the IBM CDO Strategy Summit in San Francisco. You're watching theCUBE. (techno music) (touch-tone phone beeps)

>> Announcer: Live from downtown San Francisco, it's the Cube, covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody. We're at the Parc 55 in Union Square and this is the Cube, the leader in live tech coverage and we're covering exclusive coverage of the IBM CDO strategy summit. IBM has these things, they book in on both coasts, one in San Francisco one in Boston, spring and fall. Great event, intimate event. 130, 150 chief data officers, learning, transferring knowledge, sharing ideas. Cayn Woodruff is here as the principle data scientist at IBM and she's joined by Ritesh Ororo, who is the director of digital analytics at HCL Technologies. Folks welcome to the Cube, thanks for coming on. >> Thank you >> Thanks for having us. >> You're welcome. So we're going to talk about data management, data engineering, we're going to talk about digital, as I said Ritesh because digital is in your title. It's a hot topic today. But Caryn let's start off with you. Principle Data Scientist, so you're the one that is in short supply. So a lot of demand, you're getting pulled in a lot of different directions. But talk about your role and how you manage all those demands on your time. >> Well, you know a lot of, a lot of our work is driven by business needs, so it's really understanding what is critical to the business, what's going to support our businesses strategy and you know, picking the projects that we work on based on those items. So it's you really do have to cultivate the things that you spend your time on and make sure you're spending your time on the things that matter and as Ritesh and I were talking about earlier, you know, a lot of that means building good relationships with the people who manage the systems and the people who manage the data so that you can get access to what you need to get the critical insights that the business needs, >> So Ritesh, data management I mean this means a lot of things to a lot of people. It's evolved over the years. Help us frame what data management is in this day and age. >> Sure, so there are two aspects of data in my opinion. One is the data management, another the data engineering, right? And over the period as the data has grown significantly. Whether it's unstructured data, whether it's structured data, or the transactional data. We need to have some kind of governance in the policies to secure data to make data as an asset for a company so the business can rely on your data. What you are delivering to them. Now, the another part comes is the data engineering. Data engineering is more about an IT function, which is data acquisition, data preparation and delivering the data to the end-user, right? It can be business, it can be third-party but it all comes under the governance, under the policies, which are designed to secure the data, how the data should be accessed to different parts of the company or the external parties. >> And how those two worlds come together? The business piece and the IT piece, is that where you come in? >> That is where data science definitely comes into the picture. So if you go online, you can find Venn diagrams that describe data science as a combination of computer science math and statistics and business acumen. And so where it comes in the middle is data science. So it's really being able to put those things together. But, you know, what's what's so critical is you know, Interpol, actually, shared at the beginning here and I think a few years ago here, talked about the five pillars to building a data strategy. And, you know, one of those things is use cases, like getting out, picking a need, solving it and then going from there and along the way you realize what systems are critical, what data you need, who the business users are. You know, what would it take to scale that? So these, like, Proof-point projects that, you know, eventually turn into these bigger things, and for them to turn into bigger things you've got to have that partnership. You've got to know where your trusted data is, you've got to know that, how it got there, who can touch it, how frequently it is updated. Just being able to really understand that and work with partners that manage the infrastructure so that you can leverage it and make it available to other people and transparent. >> I remember when I first interviewed Hilary Mason way back when and I was asking her about that Venn diagram and she threw in another one, which was data hacking. >> Caryn: Uh-huh, yeah. >> Well, talk about that. You've got to be curious about data. You need to, you know, take a bath in data. >> (laughs) Yes, yes. I mean yeah, you really.. Sometimes you have to be a detective and you have to really want to know more. And, I mean, understanding the data is like the majority of the battle. >> So Ritesh, we were talking off-camera about it's not how titles change, things evolve, data, digital. They're kind of interchangeable these days. I mean we always say the difference between a business and a digital business is how they have used data. And so digital being part of your role, everybody's trying to get digital transformation, right? As an SI, you guys are at the heart of it. Certainly, IBM as well. What kinds of questions are our clients asking you about digital? >> So I ultimately see data, whatever we drive from data, it is used by the business side. So we are trying to always solve a business problem, which is to optimize the issues the company is facing, or try to generate more revenues, right? Now, the digital as well as the data has been married together, right? Earlier there are, you can say we are trying to analyze the data to get more insights, what is happening in that company. And then we came up with a predictive modeling that based on the data that will statically collect, how can we predict different scenarios, right? Now digital, we, over the period of the last 10 20 years, as the data has grown, there are different sources of data has come in picture, we are talking about social media and so on, right? And nobody is looking for just reports out of the Excel, right? It is more about how you are presenting the data to the senior management, to the entire world and how easily they can understand it. That's where the digital from the data digitization, as well as the application digitization comes in picture. So the tools are developed over the period to have a better visualization, better understanding. How can we integrate annotation within the data? So these are all different aspects of digitization on the data and we try to integrate the digital concepts within our data and analytics, right? So I used to be more, I mean, I grew up as a data engineer, analytics engineer but now I'm looking more beyond just the data or the data preparation. It's more about presenting the data to the end-user and the business. How it is easy for them to understand it. >> Okay I got to ask you, so you guys are data wonks. I am too, kind of, but I'm not as skilled as you are, but, and I say that with all due respect. I mean you love data. >> Caryn: Yes. >> As data science becomes a more critical skill within organizations, we always talk about the amount of data, data growth, the stats are mind-boggling. But as a data scientist, do you feel like you have access to the right data and how much of a challenge is that with clients? >> So we do have access to the data but the challenge is, the company has so many systems, right? It's not just one or two applications. There are companies we have 50 or 60 or even hundreds of application built over last 20 years. And there are some applications, which are basically duplicate, which replicates the data. Now, the challenge is to integrate the data from different systems because they maintain different metadata. They have the quality of data is a concern. And sometimes with the international companies, the rules, for example, might be in US or India or China, the data acquisitions are different, right? And you are, as you become more global, you try to integrate the data beyond boundaries, which becomes a more compliance issue sometimes, also, beyond the technical issues of data integration. >> Any thoughts on that? >> Yeah, I think, you know one of the other issues too, you have, as you've heard of shadow IT, where people have, like, servers squirreled away under their desks. There's your shadow data, where people have spreadsheets and databases that, you know, they're storing on, like a small server or that they share within their department. And so you know, you were discussing, we were talking earlier about the different systems. And you might have a name in one system that's one way and a name in another system that's slightly different, and then a third system, where it's it's different and there's extra granularity to it or some extra twist. And so you really have to work with all of the people that own these processes and figure out what's the trusted source? What can we all agree on? So there's a lot of... It's funny, a lot of the data problems are people problems. So it's getting people to talk and getting people to agree on, well this is why I need it this way, and this is why I need it this way, and figuring out how you come to a common solution so you can even create those single trusted sources that then everybody can go to and everybody knows that they're working with the the right thing and the same thing that they all agree on. >> The politics of it and, I mean, politics is kind of a pejorative word but let's say dissonance, where you have maybe of a back-end syst6em, financial system and the CFO, he or she is looking at the data saying oh, this is what the data says and then... I remember I was talking to a, recently, a chef in a restaurant said that the CFO saw this but I know that's not the case, I don't have the data to prove it. So I'm going to go get the data. And so, and then as they collect that data they bring together. So I guess in some ways you guys are mediators. >> [Caryn And Ritesh] Yes, yes. Absolutely. >> 'Cause the data doesn't lie you just got to understand it. >> You have to ask the right question. Yes. And yeah. >> And sometimes when you see the data, you start, that you don't even know what questions you want to ask until you see the data. Is that is that a challenge for your clients? >> Caryn: Yes, all the time. Yeah >> So okay, what else do we want to we want to talk about? The state of collaboration, let's say, between the data scientists, the data engineer, the quality engineer, maybe even the application developers. Somebody, John Fourier often says, my co-host and business partner, data is the new development kit. Give me the data and I'll, you know, write some code and create an application. So how about collaboration amongst those roles, is that something... I know IBM's gone on about some products there but your point Caryn, it's a lot of times it's the people. >> It is. >> And the culture. What are you seeing in terms of evolution and maturity of that challenge? >> You know I have a very good friend who likes to say that data science is a team sport and so, you know, these should not be, like, solo projects where just one person is wading up to their elbows in data. This should be something where you've got engineers and scientists and business, people coming together to really work through it as a team because everybody brings really different strengths to the table and it takes a lot of smart brains to figure out some of these really complicated things. >> I completely agree. Because we see the challenges, we always are trying to solve a business problem. It's important to marry IT as well as the business side. We have the technical expert but we don't have domain experts, subject matter experts who knows the business in IT, right? So it's very very important to collaborate closely with the business, right? And data scientist a intermediate layer between the IT as well as business I will say, right? Because a data scientist as they, over the years, as they try to analyze the information, they understand business better, right? And they need to collaborate with IT to either improve the quality, right? That kind of challenges they are facing and I need you to, the data engineer has to work very hard to make sure the data delivered to the data scientist or the business is accurate as much as possible because wrong data will lead to wrong predictions, right? And ultimately we need to make sure that we integrate the data in the right way. >> What's a different cultural dynamic that was, say ten years ago, where you'd go to a statistician, she'd fire up the SPSS.. >> Caryn: We still use that. >> I'm sure you still do but run some kind of squares give me some, you know, probabilities and you know maybe run some Monte Carlo simulation. But one person kind of doing all that it's your point, Caryn. >> Well you know, it's it's interesting. There are there are some students I mentor at a local university and you know we've been talking about the projects that they get and that you know, more often than not they get a nice clean dataset to go practice learning their modeling on, you know? And they don't have to get in there and clean it all up and normalize the fields and look for some crazy skew or no values or, you know, where you've just got so much noise that needs to be reduced into something more manageable. And so it's, you know, you made the point earlier about understanding the data. It's just, it really is important to be very curious and ask those tough questions and understand what you're dealing with. Before you really start jumping in and building a bunch of models. >> Let me add another point. That the way we have changed over the last ten years, especially from the technical point of view. Ten years back nobody talks about the real-time data analysis. There was no streaming application as such. Now nobody talks about the batch analysis, right? Everybody wants data on real-time basis. But not if not real-time might be near real-time basis. That has become a challenge. And it's not just that prediction, which are happening in their ERP environment or on the cloud, they want the real-time integration with the social media for the marketing and the sales and how they can immediately do the campaign, right? So, for example, if I go to Google and I search for for any product, right, for example, a pressure cooker, right? And I go to Facebook, immediately I see the ad within two minutes. >> Yeah, they're retargeting. >> So that's a real-time analytics is happening under different application, including the third-party data, which is coming from social media. So that has become a good source of data but it has become a challenge for the data analyst and the data scientist. How quickly we can turn around is called data analysis. >> Because it used to be you would get ads for a pressure cooker for months, even after you bought the pressure cooker and now it's only a few days, right? >> Ritesh: It's a minute. You close this application, you log into Facebook... >> Oh, no doubt. >> Ritesh: An ad is there. >> Caryn: There it is. >> Ritesh: Because everything is linked either your phone number or email ID you're done. >> It's interesting. We talked about disruption a lot. I wonder if that whole model is going to get disrupted in a new way because everybody started using the same ad. >> So that's a big change of our last 10 years. >> Do you think..oh go ahead. >> oh no, I was just going to say, you know, another thing is just there's so much that is available to everybody now, you know. There's not this small little set of tools that's restricted to people that are in these very specific jobs. But with open source and with so many software-as-a-service products that are out there, anybody can go out and get an account and just start, you know, practicing or playing or joining a cackle competition or, you know, start getting their hands on.. There's data sets that are out there that you can just download to practice and learn on and use. So, you know, it's much more open, I think, than it used to be. >> Yeah, community additions of software, open data. The number of open day sources just keeps growing. Do you think that machine intelligence can, or how can machine intelligence help with this data quality challenge? >> I think that it's it's always going to require people, you know? There's always going to be a need for people to train the machines on how to interpret the data. How to classify it, how to tag it. There's actually a really good article in Popular Science this month about a woman who was training a machine on fake news and, you know, it did a really nice job of finding some of the the same claims that she did. But she found a few more. So, you know, I think it's, on one hand we have machines that we can augment with data and they can help us make better decisions or sift through large volumes of data but then when we're teaching the machines to classify the data or to help us with metadata classification, for example, or, you know, to help us clean it. I think that it's going to be a while before we get to the point where that's the inverse. >> Right, so in that example you gave, the human actually did a better job from the machine. Now, this amazing to me how.. What, what machines couldn't do that humans could, you know last year and all of a sudden, you know, they can. It wasn't long ago that robots couldn't climb stairs. >> And now they can. >> And now they can. >> It's really creepy. >> I think the difference now is, earlier you know, you knew that there is an issue in the data. But you don't know that how much data is corrupt or wrong, right? Now, there are tools available and they're very sophisticated tools. They can pinpoint and provide you the percentage of accuracy, right? On different categories of data that that you come across, right? Even forget about the structure data. Even when you talk about unstructured data, the data which comes from social media or the comments and the remarks that you log or are logged by the customer service representative, there are very sophisticated text analytics tools available, which can talk very accurately about the data as well as the personality of the person who is who's giving that information. >> Tough problems but it seems like we're making progress. All you got to do is look at fraud detection as an example. Folks, thanks very much.. >> Thank you. >> Thank you very much. >> ...for sharing your insight. You're very welcome. Alright, keep it right there everybody. We're live from the IBM CTO conference in San Francisco. Be right back, you're watching the Cube. (electronic music)

>> Announcer: Live, from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. >> We're back at the IBM CDO Strategy Summit in San Francisco, we're at the Parc 55, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, and I'm here with Krishna Venkatraman, who is with IBM, he's the Vice President of Data Science and Data Governance. Krishna, thanks for coming on. >> Thank you, thank you for this opportunity. >> Oh, you're very welcome. So, let's start with your role. Your passion is really creating value from data, that's something you told me off-camera. That's a good passion to have these days. So what's your role at IBM? >> So I work for Inderpal, who's GCDO. He's the CDO for the company, and I joined IBM about a year ago, and what I was intrigued by when I talked to him early on was, you know, IBM has so many assets, it's got a huge history and legacy of technology, enormous, copious amounts of data, but most importantly, it also has a lot of experience helping customers solve problems at enterprise scale. And in my career, I started at HP Labs many, many years ago, I've been in a few startups, most recently before I joined IBM, I was at On Deck. What I've always found is that it's very hard to extract information and insights from data unless you have the end-to-end pieces in place, and when I was at On Deck, we built all of it from scratch, and I thought this would be a great opportunity to come to IBM, leverage all that great history and legacy and skill to build something that would allow data to almost be taken for granted. So, in a sense, a company doesn't have to think about the pain of getting value extracted from data, they could just say, you know, I trust data just as I trust the other things in life, like when I go buy a book, I know all the backend stuff is done for me, I can trust the product I get. And I was interested in that, and that's the role that Inderpal offered to me. >> So the opposite of On Deck, really. On Deck was kind of a blank sheet of paper, right? And so now you have a complex organization, as Inderpal was describing this morning, so big challenge. Ginni Rometty at IBM Think talked about incumbent disruptors, so that's essentially what IBM is, right? >> Exactly, exactly. The fact is IBM has a history and a culture of making their customers successful, so they understand business problems really well. They have a huge legacy in innovation around technology, and I think now is the right time to put all of those pieces together, right? To string together a lifecycle for how data can work for you, so when you embark on a data project, it doesn't have to take six months, it could be done in two or three days, because you've cobbled together how to manage data at the backend, you've got the data science and the data science lifecycle worked out, and you know how to deploy it into a business process, because you understand the business process really well. And I think, you know, those are the mismatches that I've seen happen over and over again, data isn't ready for the application of machine learning, the machine learning model really isn't well-suited to the eventual environment in which it's deployed, but I think IBM has all of that expertise, and I feel like it's an opportunity for us to tie that together. >> And everybody's trying to get, I often say, get digital right, you know, your customers, your clients, everyone talks about digital transformation, but it's really all about the data, isn't it? Getting the data right. >> Getting the data right, that's where it starts. Tomorrow, I'm doing a panel on trust, you know, we can talk about the CDO and all the great things that are happening and extracting value, but unless you have trust at the beginning and you're doing good data governance, and you're able to understand your data, all of the rest will never happen. >> But you have to have both, alright? Because if you have trust without the data value, then okay. And you do see a lot of organizations just focusing, maybe over-rotating on that privacy and trust and security, for good reason, how do you balance that information as an asset versus liability equation? Because you're trying to get value out of it, and at the same time, you're trying to protect your organization. >> Yeah. I think it's a virtuous cycle, I think they build on each other. If customers trust you with their data, they're going to give you more of it, because they know you're going to use it responsibly, and I think that's a very positive thing, so I actually look at privacy and trust as enablers to create value, rather than somehow they're in competition. >> Not a zero-sum game. >> Not at all. >> Let's talk some more about that, I mean, when you think about it, because I've heard this before, GDPR comes up. Hey, we can turn GDPR into an opportunity, it's not just this onerous, even though it is, regulatory imposition, so maybe some examples or maybe talk through how organizations can take the privacy and trust part of the equation and turn it into value. >> So very simply, what does GDPR promise, right? It's restoring the fundamental rights of data subjects, in terms of their ownership of their data and the processing of their data and the ability to know how that data is used at any point in time. Now imagine if you're a data scientist and you could, for a problem that you're trying to solve, have the same kind of guarantees. You know all about the data, you know where it resides, you know exactly what it contains. They're very similar, you know? They both are asking for the same type of information. So, in a sense, if you solve the GDPR problem well, you have to really understand your data assets very well, and you have to have it governed really well, which is exactly the same need for data scientists. So, in a way, I seem them as, you know, they're twins, separated at some point, but... >> What's interesting, too, is you think about, we were sort of talking about this off-camera, but now, you're one step away from going to a user or customer and saying here, here's your data, do what you like with it. Now okay, in the one case, GDPR, you control it, sort of. But the other is if you want to monetize your own data, why pay the search company for clicking on an ad? Why not monetize your own data based on your reputation or do you see a day where consumers will actually be able to own, truly own their own data? >> I think, as a consumer, as well as a data professional, I think that the technologies are falling into place for that model to possibly become real. So if you have something that's very valuable that other people want, there should be a way for you to get some remuneration for that, right? And maybe it's something like a blockchain. You contribute your data and then when that data is used, you get some little piece of it as your reward for that. I don't know, I think it's possible, I haven't really... >> Nirvana. I wonder if we can talk about disruption, nobody talks about that, we haven't had a ton of conversations here about disruption, it seems to be more applying disciplines to create data value, but coming from the financial services industry, there's an industry that really hasn't been highly disrupted, you know, On Deck, in a way, was trying to disrupt. Healthcare is another one that hasn't been disrupted. Aerospace really hasn't been disrupted. Other industries like publishing, music, taxis, hotels have been disrupted. The premise is, it's the data that enables that disruption. Thoughts on disruption from the standpoint of your clients and how you're helping them become incumbent disruptors? >> I think sometimes disruption happens and then you look back and you say, that was disrupted after all, and you don't notice it when it happens, so even if I look at financial services and I look at small business lending, the expectations of businesses have changed on how they would access capital in that case. Even though the early providers of that service may not be the ones who win in the end, that's a different matter, so I think the idea that, you know, and I feel like this confluence of technologies, where's there's blockchain or quantum computing or even regulation that's coming in, that's sort of forcing certain types of activities around cleaning up data, they're all happening simultaneously. I think we will see certain industries and certain processes transform dramatically. >> Orange Bank was an example that came up this morning, an all-digital bank, you can't call them, right? You can't walk into their branch. You think banks will lose control of the payment systems? They've always done a pretty good job of hanging onto them, but... >> I don't know. I think, ultimately, customers are going to go to institutions they trust, so it's all going to end up with, do you trust the entity you've given your precious commodities to, right? Your data, your information, I think companies that really take that seriously and not take it as a burden are the ones who are going to find that customers are going to reach out to them. So it's more about not necessarily whether banks are going to lose control or whether... Which banks are going to win, is the way I would look at it. >> Maybe the existing banks might get trouble, but there's so many different interesting disruption scenarios, I mean, you think about Watson in healthcare, maybe we're at the point already where machines can make better diagnoses than doctors. You think about retail, and certain retail won't go away, obviously grocery and maybe high-end luxury malls won't go away, but you wonder about the future of retail as a result of this data disruption. Your thoughts? >> On retail? I do feel like, because the data is getting more, people are going to have more access to their own information, it will lead to a change in business models in certain cases. And the friction or the forces that used to keep customers with certain businesses may dissolve, so if you don't have friction, then it's going to end up with value and loyalty and service, and those are the ones I think that will thrive. >> Client comes to you, says, Krishna, I'm really struggling with my overall data strategy, my data platform, governance, skills, all the things that Inderpal talked about this morning, where do I start? >> I would start with making sure that the client has really thought about the questions they need answered. What is it that you really want to answer with data, or it doesn't even have to be with data, for the business, with its strategy, with its tactics, there have to be a set of questions framed up that are truly important to that business. And then starting from there, you can say, you know, let's slow it down and see what technologies, what types of data will help support answering those questions. So there has to be an overarching value proposition that you're trying to solve for. And I see, you know, that's why when, the way we work in our organization is, we look at use cases as a way to drive the technology adoption. What are the big business processes you are trying to transform, what's the value you expect to create, so we have a very robust discovery process where we ask people to answer those types of questions, we help them with it. We ask them to think through what they would do if they had the perfect answer, how they will implement it, how they will measure it. And then we start working on the technology. I often think technology is an easier question to answer once you know what you want to ask. >> Totally. Is that how you spend your time, mostly working with the lines of business, trying to help them sort of answer those questions? >> That is one part of my charter. So my charter involves basically four areas, the first is data governance, just making sure that we are creating all the tools and processes so that we can guarantee that when data is used, it is trusted, it is certified, and that it's always going to be reliable. The second piece is building up a real data competency and data science competency in the organization, so we know how to use data for different types of business value, and then the third is actually taking these client engagements internally and making sure that they are successful. So our model is what we call co-creation. We ask business teams to contribute their own resources. Data engineers, data scientists, business experts. We contribute specialized skills as well. And so we're jointly in the game together, right? So that's the third piece. And the last piece is, we're building out this platform that Inderpal showed this morning, that platform needs product management, so we are also working on, what are the fundamental pieces of functionality we want in the platform, and how do we make sure they're on the roadmap and they're prioritized in the right way. >> Excellent. Well, Krishna, thanks very much for coming to theCUBE, it was a pleasure meeting you. >> Thanks. >> Alright, keep it right there everybody, we'll be back with our next guest. You're watching theCUBE live from IBM CDO Summit in San Francisco. We'll be right back. (funky electronic music) (phone dialing)

>> Narrator: Live from downtown San Francisco, it's theCUBE, covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. >> We're back in San Francisco, we're here at the Parc 55 at the IBM Chief Data Officer Strategy Summit. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante and IBM's Chief Data Officer Strategy Summit, they hold them on both coasts, one in Boston and one in San Francisco. A couple times each year, about 150 chief data officers coming in to learn how to apply their craft, learn what IBM is doing, share ideas. Great peer networking, really senior audience. John Thomas is here, he's a distinguished engineer and director at IBM, good to see you again John. >> Same to you. >> Thanks for coming back in theCUBE. So let's start with your role, distinguished engineer, we've had this conversation before but it just doesn't happen overnight, you've got to be accomplished, so congratulations on achieving that milestone, but what is your role? >> The road to distinguished engineer is long but today, these days I spend a lot of my time working on data science and in fact am part of what is called a data science elite team. We work with clients on data science engagements, so this is not consulting, this is not services, this is where a team of data scientists work collaboratively with a client on a specific use case and we build it out together. We bring data science expertise, machine learning, deep learning expertise. We work with the business and build out a set of tangible assets that are relevant to that particular client. >> So this is not a for-pay service, this is hey you're a great customer, a great client of ours, we're going to bring together some resources, you'll learn, we'll learn, we'll grow together, right? >> This is an investment IBM is making. It's a major investment for our top clients working with them on their use cases. >> This is a global initiative? >> This is global, yes. >> We're talking about, what, hundreds of clients, thousands of clients? >> Well eventually thousands but we're starting small. We are trying to scale now so obviously once you get into these engagements, you find out that it's not just about building some models. There are a lot of challenges that you've got to deal with in an enterprise setting. >> Dave: What are some of the challenges? >> Well in any data science engagement the first thing is to have clarity on the use case that you're engaging in. You don't want to build models for models' sake. Just because Tensorflow or scikit-learn is great and build models, that doesn't serve a purpose. That's the first thing, do you have clarity of the business use case itself? Then comes data, now I cannot stress this enough, Dave, there is no data science without data, and you might think this is the most obvious thing, of course there has to be data, but when I say data I'm talking about access to the right data. Do we have governance over the data? Do we know who touched the data? Do we have lineage on that data? Because garbage in, garbage out, you know this. Do we have access to the right data in the right control setting for my machine learning models we built. These are challenges and then there's another challenge around, okay, I built my models but how do I operationalize them? How do I weave those models into the fabric of my business? So these are all challenges that we have to deal with. >> That's interesting what you're saying about the data, it does sound obvious but having the right data model as well. I think about when I interact with Netflix, I don't talk to their customer service department or their marketing department or their sales department or their billing department, it's one experience. >> You just have an experience, exactly. >> This notion of incumbent disruptors, is that a logical starting point for these guys to get to that point where they have a data model that is a single data model? >> Single data model. (laughs) >> Dave: What does that mean, right? At least from an experienced standpoint. >> Once we know this is the kind of experience we want to target, what are the relevant data sets and data pieces that are necessary to make their experience happen or come together. Sometimes there's core enterprise data that you have in many cases, it has been augmented with external data. Do you have a strategy around handling your internal, external data, your structured transactional data, your semi-structured data, your newsfeeds. All of these need to come together in a consistent fashion for that experience to be true. It is not just about I've got my credit card transaction data but what else is augmenting that data? You need a model, you need a strategy around that. >> I talk to a lot of organizations and they say we have a good back-end reporting system, we have Cognos we can build cubes and all kinds of financial data that we have, but then it doesn't get down to the front line. We have an instrument at the front line, we talk about IOT and that portends change there but there's a lot of data that either isn't persisted or not stored or doesn't even exist, so is that one of the challenges that you see enterprises dealing with? >> It is a challenge. Do I have access to the right data, whether that is data at rest or in motion? Am I persisting it the way I can consume it later? Or am I just moving big volumes of data around because analytics is there, or machine learning is there and I have to move data out of my core systems into that area. That is just a waste of time, complexity, cost, hidden costs often, 'cause people don't usually think about the hidden costs of moving large volumes of data around. But instead of that can I bring analytics and machine learning and data science itself to where my data is. Not necessarily to move it around all the time. Whether you're dealing with streaming data or large volumes of data in your Hadoop environment or mainframes or whatever. Can I do ML in place and have the most value out of the data that is there? >> What's happening with all that Hadoop? Nobody talks about Hadoop anymore. Hadoop largely became a way to store data for less, but there's all this data now and a data lake. How are customers dealing with that? >> This is such an interesting thing. People used to talk about the big data, you're right. We jumped from there to the cognitive It's not like that right? No, without the data then there is no cognition there is no AI, there is no ML. In terms of existing investments in Hadoop for example, you have to absolutely be able to tap in and leverage those investments. For example, many large clients have investments in large Cloudera or Hortonworks environment, or Hadoop environments so if you're doing data science, how do you push down, how do you leverage that for scale, for example? How do you access the data using the same access control mechanisms that are already in place? Maybe you have Carbros as your mechanism how do you work with that? How do you avoid moving data off of that environment? How do you push down data prep into the spar cluster? How do you do model training in that spar cluster? All of these become important in terms of leveraging your existing investments. It is not just about accessing data where it is, it's also about leveraging the scale that the company has already invested in. You have hundred, 500 node Hadoop clusters well make the most of them in terms of scaling your data science operations. So push down and access data as much as possible in those environments. >> So Beth talked today, Beth Smith, about Watson's law, and she made a little joke about that, but to me its poignant because we are entering a new era. For decades this industry marched to the cadence of Moore's law, then of course Metcalfe's law in the internet era. I want to make an observation and see if it resonates. It seems like innovation is no longer going to come from doubling microprocessor speed and the network is there, it's built out, the internet is built. It seems like innovation comes from applying AI to data together to get insights and then being able to scale, so it's cloud economics. Marginal costs go to zero and massive network effects, and scale, ability to track innovation. That seems to be the innovation equation, but how do you operationalize that? >> To your point, Dave, when we say cloud scale, we want the flexibility to do that in an off RAM public cloud or in a private cloud or in between, in a hybrid cloud environment. When you talk about operationalizing, there's a couple different things. People think that, say I've got a super Python programmer and he's great with Tensorflow or scikit-learn or whatever and he builds these models, great, but what happens next, how do you actually operationalize those models? You need to be able to deploy those models easily. You need to be able to consume those models easily. For example you have a chatbot, a chatbot is dumb until it actually calls these machine learning models, real time to make decisions on which way the conversation should go. So how do you make that chatbot intelligent? It's when it consumes the ML models that have been built. So deploying models, consuming models, you create a model, you deploy it, you've got to push it through the development test staging production phases. Just the same rigor that you would have for any applications that are deployed. Then another thing is, a model is great on day one. Let's say I built a fraud detection model, it works great on day one. A week later, a month later it's useless because the data that it trained on is not what the fraudsters are using now. So patterns have changed, the model needs to be retrained How do I understand the performance of the model stays good over time? How do I do monitoring? How do I retrain the models? How do I do the life cycle management of the models and then scale? Which is okay I deployed this model out and its great, every application is calling it, maybe I have partners calling these models. How do I automatically scale? Whether what you are using behind the scenes or if you are going to use external clusters for scale? Technology is like spectrum connector from our HPC background are very interesting counterparts to this. How do I scale? How do I burst? How do I go from an on-frame to an off-frame environment? How do I build something behind the firewall but deploy it into the cloud? We have a chatbot or some other cloud-native application, all of these things become interesting in the operationalizing. >> So how do all these conversations that you're having with these global elite clients and the challenges that you're unpacking, how do they get back into innovation for IBM, what's that process like? >> It's an interesting place to be in because I am hearing and experiencing first hand real enterprise challenges and there we see our product doesn't handle this particular thing now? That is an immediate circling back with offering management and development. Hey guys we need this particular function because I'm seeing this happening again and again in customer engagements. So that helps us shape our products, shape our data science offerings, and sort of running with the flow of what everyone is doing, we'll look at that. What do our clients want? Where are they headed? And shape the products that way. >> Excellent, well John thanks very much for coming back in theCUBE and it's a pleasure to see you again. I appreciate your time. >> Thank you Dave. >> All right good to see you. Keep it right there everybody we'll be back with our next guest. We're live from the IBM CDO strategy summit in San Francisco, you're watching theCUBE.

>> Narrator: Live from downtown San Francisco, it's The Cube, covering IBM chief data officer strategy summit 2018, brought to you by IBM. (playful music) >> Welcome back to the IBM chief data officer strategy summit in San Francisco. We're here at the Parc 55. My name is Dave Vellante, and you're watching The Cube, the leader in live tech coverage, #IBMCDO. Seth Dobrin is here. He's the chief data officer for IBM analytics. Seth, good to see you again. >> Good to see you again, Dave. >> Many time Cube alum; thanks for coming back on. Asim Tewary, Tewary? Tewary; sorry. >> Tewary, yes. >> Asim Tewary; I can't read my own writing. Head of data science and advanced analytics at Verizon, and from Jersey. Two east coast boys, three east coast boys. >> Three east coast boys. >> Yeah. >> Welcome, gentlemen. >> Thank you. >> Asim, you guys had a panel earlier today. Let's start with you. What's your role? I mean, we talked you're the defacto chief data officer at Verizon. >> Yes, I'm responsible for all the data ingestion platform, big data, and the data science for Verizon, for wireless, wire line, and enterprise businesses. >> It's a relatively new role at Verizon? You were saying previously you were CDO at a financial services organization. Common that a financial service organization would have a chief data officer. How did the role come about at Verizon? Are you Verizon's first CDO or-- >> I was actually brought in to really pull together the analytics and data across the enterprise, because there was a realization that data only creates value when you're able to get it from all the difference sources. We had separate teams in the past. My role was to bring it all together, to have a common platform, common data science team to drive revenue across the businesses. >> Seth, this is a big challenge, obviously. We heard Caitlyn this morning, talking about the organizational challenges. You got data in silos. Inderpal and your team are basically, I call it dog-fooding. You're drinking your own champagne. >> Champagne-ing, yeah. >> Yeah, okay, but you have a similar challenge. You have big company, complex, a lot of data silos coming. Yeah, I mean, IBM is really, think of it as five companies, right? Any one of them would be a fortune 500 company in and of themselves. Even within each of those, there were silos, and then Inderpal trying to bring them across, you know, the data from across all of them is really challenging. Honestly, the technology part, the bringing it together is the easy part. It's the cultural change that goes along with it that's really, really hard, to get people to think about it as IBM's or Verizon's data, and not their data. That's really how you start getting value from it. >> That's a cultural challenge you face is, "Okay, I've got my data; I don't want to share." How do you address that? >> Absolutely. Governance and ownership of data, having clear roles and responsibilities, ensuring there's this culture where people realize that data is an asset of the firm. It is not your data or my data; it is firm's data, and the value you create for the business is from that data. It is a transformation. It's changing the people culture aspect, so there's a lot of education. You know, you have to be an evangelist. You wear multiple hats to show people the value, why they should do. Obviously, I had an advantage because coming in, Verizon management was completely sold to the idea that the data has to be managed as an enterprise asset. Business was ready and willing to own data as an enterprise asset, and so it was relatively easier. However, it was a journey to try to get everyone on the same page in terms of ensuring that it wasn't the siloed mentality. This was a enterprise asset that we need to manage together. >> A lot of organizations tell me that, first of all, you got to have top-down buy-in. Clearly, you had that, but a lot of the times I hear that the C-suite says, "Okay, we're going to do this," but the middle management is sort of, they got to PNL, they've got to make their plan, and it takes them longer to catch up. Did you face that challenge, and how do you ... How were you addressing it? >> Absolutely. What we had to do was really make sure that we were not trying to boil the ocean, that we were trying to show the values. We found champions. For example, finance, you know, was a good champion for us, where we used the data and analytics to really actually launch some very critical initiatives for the firm, asset-backed securities. For the first time, Verizon launched ABS, and we actually enabled that. That created the momentum, if you will, as to, "Okay, there's value in this." That then created the opportunity for all the other business to jump on and start leveraging data. Then we all are willing to help and be part of the journey. >> Seth, before you joined IBM, obviously the company was embarking on this cognitive journey. You know, Watson, the evolution of Watson, the kind of betting a lot on cognitive, but internally you must have said, "Well, if we're going to market this externally, "we'd better become a cognitive enterprise." One of the questions that came up on the panel was, "What is a cognitive enterprise?" You guys, have you defined it? Love to ask Asim the same question. >> Yeah, so I mean, a cognitive enterprise is really about an enterprise that uses data and analytics, and cognition to run their business, right? You can't just jump to being a cognitive enterprise, right? It's a journey or a ladder, right? Where you got to get that foundation data in order. Then you've got to start even being able to do basic analytics. Then you can start doing things like machine learning, and deep learning, and then you can get into cognition. It's not a, just jump to the top of the ladder, because there's just a lot of work that's required to do it. You can do that within a business unit. The whole company doesn't need to get there, and in fact, you'll see within a company, different part of the company will be at different stages. Kind of to Asim's point about partnering with finance, and that's my experience both at IBM and before I joined. You find a partner that's going to be a champion for you. You make them immensely successful, and everyone else will follow because of shame, because they don't want to be out-competed by their peers. >> So, similar definition of a cognitive enterprise? >> Absolutely. In fact, what I would say is cognitive is a spectrum, right? Where most companies are at the low end of that spectrum where using data for decision-making, but those are reports, BI reports, and stuff like that. As you evolve to become smarter and more AI machine learning, that's when you get into predictive, where you're using the data to predict what might happen based on prior historical information. Then that evolution goes all the way to being prescriptive, where you're not only looking back and being able to predict, but you're actually able to recommend action that you want to take. Obviously, with the human involvement, because governance is an important aspect to all of this, right? Completely agree that the cognitive is really covering the spectrum of prescriptive, predictive, and using data for all your decision making. >> This actually gets into a good point, right? I mean, I think Asim has implemented some deep learning models at Verizon, but you really need to think about what's the right technology or the right, you know, the right use case for that. There's some use cases where descriptive analytics is the right answer, right? There's no reason to apply machine learning or deep learning. You just need to put that in front of someone. Then there are use cases where you do want deep learning, either because the problem is so complex, or because the accuracy needs to be there. I go into a lot of companies to talk to senior executives, and they're like, "We want to do deep learning." You ask them what the use case is, and you're like, "Really, that's rules," right? It gets back to Occam's razor, right? The simplest solution is always the answer, is always the best answer. Really understanding from your perspective, having done this at a couple of companies now, kind of when do you know when to use deep learning versus machine learning, versus just basic statistics? >> How about that? >> Yeah. >> How do you parse that? >> Absolutely. You know, like anything else, it's very important to understand what problem you're trying to solve. When you have a hammer, everything looks like a nail, and deep learning might be one of those hammers. What we do is make sure that any problem that requires explain-ability, interpret-ability, you cannot use deep learning, because you cannot explain when you're using deep learning. It's a multi-layered neural network algorithm. You can't really explain why the outcome was what it was. For that, you have to use more simpler algorithms, like decision tree, like regression, classification. By the way, 70 to 80% of the problem that you have in the company, can be solved by those algorithms. You don't always use deep learning, but deep learning is a great use case algorithm to use when you're solving complex problems. For example, when you're looking at doing friction analysis as to customer journey path analysis, that tends to be very noisy. You know, you have billions of data points that you have to go through for an algorithm. That is, you know, good for deep learning, so we're using that today, but you know, those are a narrow set of use cases where it is required, so it's important to understand what problem you're trying to solve and where you want to use deep learning. >> To use deep learning, you need a lot of label data, right? >> Yes. >> And that's-- >> A lot of what? Label data? >> Label data. So, and that's often a hurdle to companies using deep learning, even when they have a legitimate deep learning use cases. Just the massive amount of label data you need for that use case. >> As well as scale, right? >> Yeah. >> The whole idea is that when you have massive amounts of data with a lot of different variables, you need deep learning to be able to make that decision. That means you've got to have scale and real time capability within the platform, that has the elasticity and compute, to be able to crunch all that data. >> Yeah. >> Initially, when we started on this journey, our infrastructure was not able to handle that. You know, we had a lot of failures, and so obviously we had to enhance our infrastructure to-- >> You spoke to Samit Gupta and Ed earlier, about, you know, GPUs, and flash storage, and the need for those types of things to do these complex, you know, deep learning problems. We struggled with that even inside of IBM when we first started building this platform as, how do we get the best performance of ingesting the data, getting it labeled, and putting it into these models, these deep learning models, and some of the instance we use that. >> Yeah, my takeaway is that infrastructure for AI has to be flexible, you got to be great granularity. It's got to not only be elastic, but it's got to be, sometimes we call it plastic. It's got to sometimes retain its form. >> Yes. >> Right? Then when you bring in some new unknown workload, you've got to be able to adjust it without ripping down the entire infrastructure. You have to purpose built a whole next set of infrastructure, which is kind of how we built IT over the years. >> Exactly. >> I think, Dave, too, When you and I first spoke four or five years ago, it was all about commodity hardware, right? It was going to Hadoop ecosystem, minimizing, you know, getting onto commodity hardware, and now you're seeing a shift away from commodity hardware, in some instances, toward specialized hardware, because you need it for these use cases. So we're kind of making that. We shifted to one extreme, and now we're kind of shifting, and I think we're going to get to a good equilibrium where it's a balance of commodity and specialized hardware for big data, as much as I hate that word, and advanced analytics. >> Well, yeah, even your cloud guys, all the big cloud guys, they used to, you know, five, six years ago, say, "Oh, it's all commodity stuff," and now it's a lot of custom, because they're solving problems that you can't solve with a commodity. I want to ask you guys about this notion of digital business. To us, the difference between a business and a digital business is how you use data. As you become a digital business, which is essentially what you're doing with cognitive and AI, historically, you may have organized around, I don't know, your network, and certain you've got human skills that are involved, and your customers. I mean, IBM in your case, it's your products, your services, your portfolio, your clients. Increasingly, you're organizing around your data, aren't you? Which brings back to cultural change, but what about the data model? I presume you're trying to get to a data model where the customer service, and the sales, and the marketing aren't separate entities. I don't have to deal with them when I talk to Verizon. I deal with just Verizon, right? That's not easy when the data's all inside. How are you dealing with that challenge? >> Customer is at the center of the business model. Our motto and out goal is to provide the best products to the customers, but even more important, provide the best experience. It is all about the customer, agnostic of the channel, which channel the customer is interacting with. The customer, for the customer, it's one Verizon. The way we are organizing our data platform is, first of all, breaking all the silos. You know, we need to have data from all interactions with the customer, that is all digital, that's coming through, and creating one unified model, essentially, that essentially teaches all the journeys, and all the information about the customer, their events, their behavior, their propensities, and stuff like that. Then that information, using algorithms, like predictive, prescriptive, and all of that, make it available in all channels of engagement. Essentially, you have common intelligence that is made available across all channels. Whether the customer goes to point of sale in a retail store, or calls a call center, talks to a rep, or is on the digital channel, is the same intelligence driving the experience. Whether a customer is trying to buy a phone, or has an issue with a service related aspect of it, and that's the key, which is centralized intelligence from common data lake, and then deliver a seamless experience across all channels for that customer-- >> Independent of where I bought that phone, for example, right? >> Exactly. Maintaining the context is critical. If you went to the store and you know, you're looking for a phone, and you know, you didn't find what you're looking for, you want to do some research, if you go to the digital channel, you should be able to have a seamless experience where we should know that you went, that you're looking for the phone, or you called care and you asked the agent about something. Having that context be transferred across channel and be available, so the customer feels that we know who the customer is, and provide them with a good experience, is the key. >> We have limited time, but I want to talk about skills. It's hard to come by; we talked about that. It's number five on Inderpal's sort of, list of things you've got to do as a CDO. Sometimes you can do MNA, by the weather company. You've got a lot of skills, but that's not always so practical. How have you been dealing with the skills gap? >> Look, skill is hard to find, data scientists are hard to find. The way we are envisioning our talent management is two things we need to take care of. One, we need solid big data engineers, because having a solid platform that has real trans-streaming capability is very critical. Second, data scientists, it's hard to get. However, our plan is to really take the domain experts, who really understand the business, who understand the business process and the data, and give them the tools, automation tools for data science, that essentially, you know, will put it in a box for them, in terms of which algorithm to use, and enable them to create more value. While we will continue to hire specialized data scientists who are going to work on much more of the complex problems, the skill will come from empowering and enabling the domain experts with data science capabilities that automates choosing model development and algorithm development. >> Presumably grooming people in house, right? >> Grooming people in house, and I actually break it down a little more granular. I even say there's data engineers, there's machine learning engineers, there's optimization engineers, then there's data journalists. They're the ones that tell the story. I think we were talking earlier, Asim, about you know, it's not just PhDs, right? You're not just looking for PhDs to fill these rolls anymore. You're looking for people with masters degrees, and even in some cases, bachelors degrees. With IBM's new collar job initiative, we're even bringing on some, what we call P-TECH students, which are five year high school students, and we're building a data science program for them. We're building apprenticeships, which is, you know, you've had a couple years of college, building a data science program, and people look at me like I'm crazy when I say that, but the bulk of the work of a data science program, of executing data science, is not implementing machine learning models. It's engineering features, it's cleaning data. With basic Python skills, this is something that you can very easily teach these people to do, and then under the supervision of a principal data scientist or someone with a PhD or a masters degree, they can start learning how to implement models, but they can start contributing right away with just some basic Python skills. >> Then five, seven years in, they're-- >> Yeah. >> domain experts. All right, guys, got to jump, but thanks very much, Asim, for coming on and sharing your story. Seth, always a pleasure. >> Yeah, good to see you again, Dave. >> All right. >> Thank you, Dave. >> You're welcome. Keep it right there, buddy. >> Thanks. >> We'll be back with our next guest. This is The Cube, live from IBM CDO strategy summit in San Francisco. We'll be right back. (playful music) (phone dialing)

(music playing) >> Narrator: Live, from downtown San Francisco It's the Cube. Covering IBM Chief Data Officer Startegy Summit 2018. Brought to you by: IBM >> Welcome back to San Francisco everybody we're at the Parc 55 in Union Square. My name is Dave Vellante, and you're watching the Cube. The leader in live tech coverage and this is our exclusive coverage of IBM's Chief Data Officer Strategy Summit. They hold these both in San Francisco and in Boston. It's an intimate event, about 150 Chief Data Officers really absorbing what IBM has done internally and IBM transferring knowledge to its clients. Steven Eluk is here. He is one of those internal practitioners at IBM. He's the Vice President of Deep Learning and the Global Chief Data Office at IBM. We just heard from him and some of his strategies and used cases. He's joined by Sumit Gupta, a Cube alum. Who is the Vice President of Machine Learning and deep learning within IBM's cognitive systems group. Sumit. >> Thank you. >> Good to see you, welcome back Steven, lets get into it. So, I was um paying close attention when Bob Picciano took over the cognitive systems group. I said, "Hmm, that's interesting". Recently a software guy, of course I know he's got some hardware expertise. But bringing in someone who's deep into software and machine learning, and deep learning, and AI, and cognitive systems into a systems organization. So you guys specifically set out to develop solutions to solve problems like Steven's trying to solve. Right, explain that. >> Yeah, so I think ugh there's a revolution going on in the market the computing market where we have all these new machine learning, and deep learning technologies that are having meaningful impact or promise of having meaningful impact. But these new technologies, are actually significantly I would say complex and they require very complex and high performance computing systems. You know I think Bob and I think in particular IBM saw the opportunity and realized that we really need to architect a new class of infrastructure. Both software and hardware to address what data scientist like Steve are trying to do in the space, right? The open source software that's out there: Denzoflo, Cafe, Torch - These things are truly game changing. But they also require GPU accelerators. They also require multiple systems like... In fact interestingly enough you know some of the super computers that we've been building for the scientific computing world, those same technologies are now coming into the AI world and the enterprise. >> So, the infrastructure for AI, if I can use that term? It's got to be flexible, Steven we were sort of talking about that elastic versus I'm even extending it to plastic. As Sumit you just said, it's got to have that tooling, got to have that modern tooling, you've got to accommodate alternative processor capabilities um, and so, that forms what you've used Steven to sort of create new capabilities new business capabilities within IBM. I wanted to, we didn't touch upon this before, but we touched upon your data strategy before but tie it back to the line of business. You essentially are a presume a liaison between the line of business and the chief data office >> Steven: Yeah. >> Officer office. How did that all work out, and shake out? Did you defining the business outcomes, the requirements, how did you go about that? >> Well, actually, surprisingly, we have very little new use cases that we're generating internally from my organization. Because there's so many to pick from already throughout the organization, right? There's all these business units coming to us and saying, "Hey, now the data is in the data lake and now we know there's more data, now we want to do this. How do we do it?" You know, so that's where we come in, that's where we start touching and massaging and enabling them. And that's the main efforts that we have. We do have some derivative works that have come out, that have been like new offerings that you'll see here. But mostly we already have so many use cases that from those businesses units that we're really trying to heighten and bring extra value to those domains first. >> So, a lot of organizations sounds like IBM was similar you created the data lake you know, things like "a doop" made a lower cost to just put stuff in the data lake. But then, it's like "okay, now what?" >> Steven: Yeah. >> So is that right? So you've got the data and this bog of data and you're trying to make more sense out of it but get more value out of it? >> Steven: Absolutely. >> That's what they were pushing you to do? >> Yeah, absolutely. And with that, with more data you need more computational power. And actually Sumit and I go pretty far back and I can tell you from my previous roles I heightened to him many years ago some of the deficiencies in the current architecture in X86 etc and I said, "If you hit these points, I will buy these products." And what they went back and they did is they, they addressed all of the issues that I had. Like there's certain issues... >> That's when you were, sorry to interrupt, that's when you were a customer, right? >> Steven: That's when I was... >> An external customer >> Outside. I'm still an internal customer, so I've always been a customer I guess in that role right? >> Yep, yep. >> But, I need to get data to the computational device as quickly as possible. And with certain older gen technologies, like PTI Gen3 and certain issues around um x86. I couldn't get that data there for like high fidelity imaging for autonomous vehicles for ya know, high fidelity image analysis. But, with certain technologies in power we have like envy link and directly to the CPU. And we also have PTI Gen4, right? So, so these are big enablers for me so that I can really keep the utilization of those very expensive compute devices higher. Because they're not starved for data. >> And you've also put a lot of emphasis on IO, right? I mean that's... >> Yeah, you know if I may break it down right there's actually I would say three different pieces to the puzzle here right? The highest level from Steve's perspective, from Steven's teams perspective or any data scientist perspective is they need to just do their data science and not worry about the infrastructure, right? They actually don't want to know that there's an infrastructure. They want to say, "launch job" - right? That's the level of grand clarity we want, right? In the background, they want our schedulers, our software, our hardware to just seamlessly use either one system or scale to 100 systems, right? To use one GPU or to use 1,000 GPUs, right? So that's where our offerings come in, right. We went and built this offering called Powder and Powder essentially is open source software like TensorFlow, like Efi, like Torch. But performace and capabilities add it to make it much easier to use. So for example, we have an extremely terrific scheduling software that manages jobs called Spectrum Conductor for Spark. So as the name suggests, it uses Apache Spark. But again the data scientist doesn't know that. They say, "launch job". And the software actually goes and scales that job across tens of servers or hundreds of servers. The IT team can determine how many servers their going to allocate for data scientist. They can have all kinds of user management, data management, model management software. We take the open source software, we package it. You know surprisingly ugh most people don't realize this, the open source software like TensorFlow has primarily been built on a (mumbles). And most of our enterprise clients, including Steven, are on Redhat. So we, we engineered Redhat to be able to manage TensorFlow. And you know I chose those words carefully, there was a little bit of engineering both on Redhat and on TensorFlow to make that whole thing work together. Sounds trivial, took several months and huge value proposition to the enterprise clients. And then the last piece I think that Steven was referencing too, is we also trying to go and make the eye more accessible for non data scientist or I would say even data engineers. So we for example, have a software called Powder Vision. This takes images and videos, and automatically creates a trained deep learning model for them, right. So we analyze the images, you of course have to tell us in these images, for these hundred images here are the most important things. For example, you've identified: here are people, here are cars, here are traffic signs. But if you give us some of that labeled data, we automatically do the work that a data scientist would have done, and create this pre trained AI model for you. This really enables many rapid prototyping for a lot of clients who either kind of fought to have data scientists or don't want to have data scientists. >> So just to summarize that, the three pieces: It's making it simpler for the data scientists, just run the job - Um, the backend piece which is the schedulers, the hardware, the software doing its thing - and then its making that data science capability more accessible. >> Right, right, right. >> Those are the three layers. >> So you know, I'll resay it in my words maybe >> Yeah please. >> Ease of use right, hardware software optimized for performance and capability, and point and click AI, right. AI for non data scientists, right. It's like the three levels that I think of when I'm engaging with data scientists and clients. >> And essentially it's embedded AI right? I've been making the point today that a lot of the AI is going to be purchased from companies like IBM, and I'm just going to apply it. I'm not going to try to go build my own, own AI right? I mean, is that... >> No absolutely. >> Is that the right way to think about it as a practitioner >> I think, I think we talked about it a little bit about it on the panel earlier but if we can, if we can leverage these pre built models and just apply a little bit of training data it makes it so much easier for the organizations and so much cheaper. They don't have to invest in a crazy amount of infrastructure, all the labeling of data, they don't have to do that. So, I think it's definitely steering that way. It's going to take a little bit of time, we have some of them there. But as we as we iterate, we are going to get more and more of these types of you know, commodity type models that people could utilize. >> I'll give you an example, so we have a software called Intelligent Analytics at IBM. It's very good at taking any surveillance data and for example recognizing anomalies or you know if people aren't suppose to be in a zone. Ugh and we had a client who wanted to do worker safety compliance. So they want to make sure workers are wearing their safety jackets and their helmets when they're in a construction site. So we use surveillance data created a new AI model using Powder AI vision. We were then able to plug into this IVA - Intelligence Analytic Software. So they have the nice gooey base software for the dashboards and the alerts, yet we were able to do incremental training on their specific use case, which by the way, with their specific you know equipment and jackets and stuff like that. And create a new AI model, very quickly. For them to be able to apply and make sure their workers are actually complaint to all of the safety requirements they have on the construction site. >> Hmm interesting. So when I, Sometimes it's like a new form of capture says identify "all the pictures with bridges", right that's the kind of thing you're capable to do with these video analytics. >> That's exactly right. You, every, clients will have all kinds of uses I was at a, talking to a client, who's a major car manufacturer in the world and he was saying it would be great if I could identify the make and model of what cars people are driving into my dealership. Because I bet I can draw a ugh corelation between what they drive into and what they going to drive out of, right. Marketing insights, right. And, ugh, so there's a lot of things that people want to do with which would really be spoke in their use cases. And build on top of existing AI models that we have already. >> And you mentioned, X86 before. And not to start a food fight but um >> Steven: And we use both internally too, right. >> So lets talk about that a little bit, I mean where do you use X86 where do you use IBM Cognitive and Power Systems? >> I have a mix of both, >> Why, how do you decide? >> There's certain of work loads. I will delegate that over to Power, just because ya know they're data starved and we are noticing a complication is being impacted by it. Um, but because we deal with so many different organizations certain organizations optimize for X86 and some of them optimize for power and I can't pick, I have to have everything. Just like I mentioned earlier, I also have to support cloud on prim, I can't pick just to be on prim right, it so. >> I imagine the big cloud providers are in the same boat which I know some are your customers. You're betting on data, you're betting on digital and it's a good bet. >> Steven: Yeah, 100 percent. >> We're betting on data and AI, right. So I think data, you got to do something with the data, right? And analytics and AI is what people are doing with that data we have an advantage both at the hardware level and at the software level in these two I would say workloads or segments - which is data and AI, right. And we fundamentally have invested in the processor architecture to improve the performance and capabilities, right. You could offer a much larger AI models on a power system that you use than you can on an X86 system that you use. Right, that's one advantage. You can train and AI model four times faster on a power system than you can on an Intel Based System. So the clients who have a lot of data, who care about how fast their training runs, are the ones who are committing to power systems today. >> Mmm.Hmm. >> Latency requirements, things like that, really really big deal. >> So what that means for you as a practitioner is you can do more with less or is it I mean >> I can definitely do more with less, but the real value is that I'm able to get an outcome quicker. Everyone says, "Okay, you can just roll our more GPU's more GPU's, but run more experiments run more experiments". No no that's not actually it. I want to reduce the time for a an experiment Get it done as quickly as possible so I get that insight. 'Cause then what I can do I can get possibly cancel out a bunch of those jobs that are already running cause I already have the insight, knowing that that model is not doing anything. Alright, so it's very important to get the time down. Jeff Dean said it a few years ago, he uses the same slide often. But, you know, when things are taking months you know that's what happened basically from the 80's up until you know 2010. >> Right >> We didn't have the computation we didn't have the data. Once we were able to get that experimentation time down, we're able to iterate very very quickly on this. >> And throwing GPU's at the problem doesn't solve it because it's too much complexity or? >> It it helps the problem, there's no question. But when my GPU utilization goes from 95% down to 60% ya know I'm getting only a two-thirds return on investment there. It's a really really big deal, yeah. >> Sumit: I mean the key here I think Steven, and I'll draw it out again is this time to insight. Because time to insight actually is time to dollars, right. People are using AI either to make more money, right by providing better customer products, better products to the customers, giving better recommendations. Or they're saving on their operational costs right, they're improving their efficiencies. Maybe their routing their trucks in the right way, their routing their inventory in the right place, they're reducing the amount of inventory that they need. So in all cases you can actually coordinate AI to a revenue outcome or a dollar outcome. So the faster you can do that, you know, I tell most people that I engage with the hardware and software they get from us pays for itself very quickly. Because they make that much more money or they save that much more money, using power systems. >> We, we even see this internally I've heard stories and all that, Sumit kind of commented on this but - There's actually sales people that take this software & hardware out and they're able to get an outcome sometimes in certain situations where they just take the clients data and they're sales people they're not data scientists they train it it's so simple to use then they present the client with the outcomes the next day and the client is just like blown away. This isn't just a one time occurrence, like sales people are actually using this right. So it's getting to the area that it's so simple to use you're able to get those outcomes that we're even seeing it you know deals close quicker. >> Yeah, that's powerful. And Sumit to your point, the business case is actually really easy to make. You can say, "Okay, this initiative that you're driving what's your forecast for how much revenue?" Now lets make an assumption for how much faster we're going to be able to deliver it. And if I can show them a one day turn around, on a corpus of data, okay lets say two months times whatever, my time to break. I can run the business case very easily and communicate to the CFO or whomever the line of business head so. >> That's right. I mean just, I was at a retailer, at a grocery store a local grocery store in the bay area recently and he was telling me how In California we've passed legislation that does not allow plastic bags anymore. You have to pay for it. So people are bringing their own bags. But that's actually increased theft for them. Because people bring their own bag, put stuff in it and walk out. And he didn't want to have an analytic system that can detect if someone puts something in a bag and then did not buy it at purchase. So it's, in many ways they want to use the existing camera systems they have but automatically be able to detect fraudulent behavior or you know anomalies. And it's actually quite easy to do with a lot of the software we have around Power AI Vision, around video analytics from IBM right. And that's what we were talking about right? Take existing trained AI models on vision and enhance them for your specific use case and the scenarios you're looking for. >> Excellent. Guys we got to go. Thanks Steven, thanks Sumit for coming back on and appreciate the insights. >> Thank you >> Glad to be here >> You're welcome. Alright, keep it right there buddy we'll be back with our next guest. You're watching "The Cube" at IBM's CDO Strategy Summit from San Francisco. We'll be right back. (music playing)