>> 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)

(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)