>> Narrator: From around the globe. It's theCUBE, with digital coverage of Actifio Data Driven 2020, brought to you by Actifio. >> Welcome back. I'm Stuart Miniman and this is theCUBE's coverage of Actifio Data Driven 2020. We wish everybody could join us in Boston, but instead we're doing it online this year, of course, and really excited. We're going to be digging into the value of data, how DataOps, data scientists are leveraging data. And joining me on the program, Scott Buckles, he's the North American Business Executive for database data science and DataOps with IBM, Scott, welcome to theCUBE. >> Thanks Stuart, thanks for having me, great to see you. >> Start with the Actifio-IBM partnership. Anyone that knows that Actifio knows that the IBM partnership is really the oldest one that they've had, either it's hardware through software, those joint solutions go together. So tell us about the partnership here in 2020. >> Sure. So it's been a fabulous partnership. In the DataOps world where we are looking to help, all of our customers gain efficiency and effectiveness in their data pipeline and getting value out of their data, Actifio really compliments a lot of the solutions that we have very well. So the folks from everybody from the up top, all the way through the engineering team, is a great team to work with. We're very, very fortunate to have them. How many or any specific examples or anonymized examples that you can share about joint (indistinct). >> I'm going to stay safe and go on the anonymized side. But we've had a lot of great wins, several significantly large wins, where we've had clients that have been struggling with their different data pipelines. And I say data pipeline, I mean getting value from understanding their data, to developing models and and doing the testing on that, and we can get into this in a minute, but those folks have really needed a solution where Actifio has stepped in and provided that solution. To do that at several of the largest banks in the world, including one that was a very recent merger down in the Southeast, where we were able to bring in the Actifio solution and address our, the customer's needs around how they were testing and how they were trying to really move through that testing cycle, because it was a very iterative process, a very sequential process, and they just weren't doing it fast enough, and Actifio stepped in and helped us deliver that in a much more effective way, in a much more efficient way, especially when you into a bank or two banks rather that are merging and have a lot of work to convert systems into one another and converge data, not an easy task. And that was one of the best wins that we've had in the recent months. And again, going back to the partnership, it was an awesome, awesome opportunity to work with them. >> Well, Scott, as I teed up for the beginning of the conversation, you've got data science and DataOps, help us understand how this isn't just a storage solution, when you're talking about BDP. How does DevOps fit into this? Talk a little bit about some of the constituents inside your customers that are engaging with the solution. >> Yeah. So we call it DataOps, and DataOps is both a methodology, which is really trying to combine the best of the way that we've transformed how we develop applications with DevOps and Agile Development. So going back 20 years ago, everything was a waterfall approach, everything was very slow , and then you had to wait a long time to figure out whether you had success or failure in the application that you had developed and whether it was the right application. And with the advent of DevOps and continuous delivery, the advent of things like Agile Development methodologies, DataOps is really converging that and applying that to our data pipelines. So when we look at the opportunity ahead of us, with the world exploding with data, we see it all the time. And it's not just structured data anymore, it's unstructured data, it's how do we take advantage of all the data that we have so that we can make that impact to our business. But oftentimes we are seeing where it's still a very slow process. Data scientists are struggling or business analysts are struggling to get the data in the right form so that they can create a model, and then they're having to go through a long process of trying to figure out whether that model that they've created in Python or R is an effective model. So DataOps is all about driving more efficiency, more speed to that process, and doing it in a much more effective manner. And we've had a lot of good success, and so it's part methodology, which is really cool, and applying that to certain use cases within the, in the data science world, and then it's also a part of how do we build our solutions within IBM, so that we are aligning with that methodology and taking advantage of it. So that we have the AI machine learning capabilities built in to increase that speed which is required by our customers. Because data science is great, AI is great, but you still have to have good data underneath and you have to do it at speed. Well, yeah, Scott, definitely a theme that I heard loud and clear read. IBM think this year, we do a lot of interviews with theCUBE there, it was helping with the tools, helping with the processes, and as you said, helping customers move fast. A big piece of IBM strategy there are the Cloud Paks. My understanding you've got an update with regards to BDP and Cloud Pak. So to tell us what the new releases here for the show. >> Yeah. So in our (indistinct) release that's coming up, we will be to launch BDP directly from Cloud Pak, so that you can take advantage of the Activio capabilities, which we call virtual data pipeline, straight from within Cloud Pak. So it's a native integration, and that's the first of many things to come with how we are tying those two capabilities and those two solutions more closely together. So we're excited about it and we're looking forward to getting it in our customer's hands. >> All right. And that's the Cloud Pak for Data, if I have that correct, right? >> That's called Cloud Pak for data, correct, sorry, yes. Absolutely, I should have been more clear. >> No, it's all right. It's, it's definitely, we've been watching that, those different solutions that IBM is building out with the Cloud Paks, and of course data, as we said, it's so important. Bring us inside a little bit, if you could, the customers. What are the use cases, those problems that you're helping your customers solve with these solution? >> Sure. So there's three primary use cases. One is about accelerating the development process. Getting into how do you take data from its raw form, which may or may not be usable, in a lot of cases it's not, and getting it to a business ready state, so that your data scientists, your business, your data models can take advantage of it, about speed. The second is about reducing storage costs. As data has exponentially grown so has storage costs. We've been in the test data management world for a number of years now. And our ability to help customers reduce that storage footprint is also tied to actually the acceleration piece, but helping them reduce that cost is a big part of it. And then the third part is about mitigating risk. With the amount of data security challenges that we've seen, customers are continuously looking for ways to mitigate their exposure to somebody manipulating data, accessing production data and manipulating production data, especially sensitive data. And by virtualizing that data, we really almost fully mitigate that risk of them being able to do that. Somebody either unintentionally or intentionally altering that data and exposing a client. >> Scott, I know IBM is speaking at the Data Driven event. I read through some of the pieces that they're talking about. It looks like really what you talk about accelerating customer outcomes, helping them be more productive, if you could, what, what are some of key measurements, KPIs that your customers have when they successfully deploy the solution? >> So when it comes to speed, it's really about, we're looking at about how are we reducing the time of that project, right? Are we able to have a material impact on the amount of time that we see clients get through a testing cycle, right? Are we taking them from months to days, are we taking them from weeks to hours? Having that type of material impact. The other piece on storage costs is certainly looking at what is the future growth? You're not necessarily going to reduce storage costs, but are you reducing the growth or the speed at which your storage costs are growing. And then the third piece is really looking at how are we minimizing the vulnerabilities that we have. And when you go through an audit, internally or externally around your data, understanding that the number of exposures and helping find a material impact there, those vulnerabilities are reduced. >> Scott, last question I have for you. You talk about making data scientists more efficient and the like, what are you seeing organizationally, have teams come together or are they planning together, who has the enablement to be able to leverage some of the more modern technologies out there? >> Well, that's a great question. And it varies. I think the organizations that we see that have the most impact are the ones that are most open to bringing their data science as close to the business as possible. The ones that are integrating their data organizations, either the CDO organization or wherever that may set it. Even if you don't have a CDO, that data organization and who owned those data scientists, and folding them and integrating them into the business so that they're an integral part of it, rather than a standalone organization. I think the ones that sort of weave them into the fabric of the business are the ones that get the most benefit and we've seen have the most success thus far. >> Well, Scott, absolutely. We know how important data is and getting full value out of those data scientists, critical initiative for customers. Thanks so much for joining us. Great to get the updates. >> Oh, thank you for having me. Greatly appreciated. >> Stay tuned for more coverage from Activio Data Driven 2020. I'm Stuart Miniman, and thank you for watching theCUBE. (upbeat music)

>> Narrator: Live from Miami, Florida, it's The Cube. Covering IBM's Data in AI Forum, brought to you by IBM. >> Welcome back to Miami, everybody. You're watching The Cube, the leader in live tech coverage. We're here covering the IBM Data and AI Forum. Scott Buckles is here to my right. He's the business unit executive at IBM and long time Cube alum, Daniel Hernandez is the Vice President of Data and AI group. Good to see you guys, thanks for coming on. >> Thanks for having us. >> Good to see you. >> You're very welcome. We're going to talk about data ops, kind of accelerating the journey to AI around data ops, but what is data ops and how does it fit into AI? Daniel, we'll start with you. >> There's no AI without data. You've got data science to help you build AI. You've got dev ops to help you build apps. You've got nothing to basically help you prepare data for AI. Data ops is the equivalent of dev ops, but for delivering AI ready data. >> So, how are you, Scott, dealing with this topic with customers, is it resonating? Are they leaning into it, or are they saying, "what?" >> No, it's absolutely resonating. We have a lot of customers that are doing a lot of good things on the data science side. But, trying to get the right data at the right people, and do it fast, is a huge problem. They're finding they're spending too much time prepping data, getting the data into the models, and they're not spending enough time failing fast with some of those models, or getting the models that they need to put in production into production fast enough. So, this absolutely resonates with them because I think it's been confusing for a long time. >> So, AI's scary to a lot of people, right? It's a complicated situation, right? And how do you make it less scary? >> Talk about problems that can be solved with it, basically. You want a better customer experience in your contact center, you want a similarly amazing experience when they're interacting with you on the web. How do you do that? AI is simply a way to get it done, and a way to get it done exceptionally well. So, that's how I like to talk about it. I don't start with here's AI, tell me what problems you can solve. Here are the problems you've got, and where appropriate, here's where AI can help. >> So what are some of your favorite problems that you guys are solving with customers. >> Customer and employee care, which, basically, is any business that does business has customers. Customer and employee care are huge a problem space. Catching bad people, financial crimes investigation is a huge one. Fraud, KYC AML as an example. >> National security, things like that, right? >> Yeah. >> You spend all your time with customers, what else? >> Well, customer experience is probably the one that we're seeing the most. The other is being more efficient. Helping businesses solve those problems quicker, faster. Try to find new avenues for revenue. How to cut costs out of their organization, out of their run time. Those are the ones that we see the most. >> So when you say customer experience, immediately chat bots jumps into my head. But I know we're talking more than, sort of a, transcends chat bots, but double click on customer experience, how are people applying machine intelligence to improve customer experience? >> Well, when I think of it, I think about if you call in to Delta, and you have one bad experience, or your airline, whatever that airline may be, that that customer experience could lead to losing that customer forever, and there used to be an old adage that you have one bad experience and you tell 10 people about it, you have a good one, and you tell one person, or two peoples. So, getting the right data to have that experience is where it becomes a challenge and we've seen instances where customers, or excuse me, organizations are literally trying to find the data on the screen while the customer is on hold. So, they're saying, "can I put you on hold?" and they're trying to go out and find it. So, being able to automate finding that data, getting it in the right hands, to the right people, at the right time, in moment's notice, is a great opportunity for AI and machine learning, and that's an example of how we do it. >> So, from a technical standpoint, Daniel, you guys have this IBM Cloud Pak for Data that's going to magic data virtualization thing. Let's take an example that Scott just gave us, think of an airline. I love my mobile app, I can do everything on my mobile app, except there are certain things I can't do, I have to go to the website. There are certain things I have to do with e-commerce that I have to go to the website that I can't do. Sometimes watching a movie, I can't order a movie from the app, I have to go to website, the URL, and order it there and put it on my watch list. So, I presume that there's some technical debt in each of those platforms, and there's no way to get the data from here, and the data from here talking to each other. Is that the kind of problem that you're solving? >> Yes, and in this particular case, you're actually touching on what we mean by customer and employee care everywhere. The interaction you have on your phone should be the same as the interaction and the kind of response on the web, which should be the same, if not better, when you're talking to a human being. How do you have the exceptional customer and employee care, all channels. Today, say the art is, I've got a specific experience for my phone, a specific experience for my website, a specific, different experience in my contact center. The whole work we're doing around Watson Assistant, and it as a virtual assistant, is to be that nervous system that underpins all channels, and with Cloud Pak for Data, we can deliver it anywhere. You want to run your contact center on an IBM Cloud? Great. You want to run it on Amazon, Azure, Google, your own private center, or everything in between, great. Cloud Pak for Data is how you get Watson Assistant, the rest of Watson and our data stack anywhere you want, so you can deliver that same consistent, amazing experience, all channels, anywhere. >> And I know the tone of my question was somewhat negative, but I'm actually optimistic, and there's a couple examples I'll give. I remember Bill Belichick one time said, "Agh, the weather, it can't ever get the weather right," this is probably five, six years ago. Actually, they do pretty well with the weather compared to 10 or 15 years ago. The other is fraud detection. In the last 10 years, fraud detection has become so much better in terms of just the time it takes to identify a fraud, and the number of false positives. Even in the last, I'd say, 12 to 18 months, false positives are way down. I think that's machine intelligence, right? >> I mean, if you're using business rules, they're not way down. They're still way up. If you're using more sophisticated techniques, that are depending upon the operational data to be trained, then they should be way down. But, there is still a lot of these systems that are based on old school business rules that can't keep up. They're producing alerts that, in many cases, are ignored, and because they're ignored, you're susceptible to bad issues. With, especially AI based techniques for fraud detection, you better have good data to train this stuff, which gets back to the whole data ops thing, and training those with good data, which data ops can help you get done. >> And a key part to data ops is the people and the process. It's not just about automating things and automating the data to get it in the right place. You have to modernize those business processes and have the right skills to be able to do that as well. Otherwise, you're not going to make the progress. You're not going to reap the benefits. >> Well, that was actually my next question. What about the people and the process? We were talking before, off camera, about our PA, and he's saying "pave the cow path." But sometimes you actually have to re-engineer the process and you might not have the skill set. So it's people and process, and then technology you lay in. And we've always talked about this, technology is always going to change. Smart technologists will figure it out. But, the people and the process, that's the hardest part. What are you seeing in the field? >> We see a lot of customers struggling with the people and process side, for a variety of reasons. The technology seems to be the focus, but when we talk to customers, we spend a lot of time saying, "well, what needs to change in your business process "when this happens? "How do those business rules need to change "so you don't get those false positives?" Because it doesn't matter at the end of the day. >> So, can we go back to the business rules thing? So, it sounds like the business rules are sort of an outdated, policy based, rigid sort of structure that's enforced no matter what. Versus machine intelligence, which can interpret situations on the fly, but can you add some color to that and explain the difference between what you call sort of business rules based versus AI based. >> So the AI based ones, in this particular case, probably classic statistical machine learning techniques, to do something like know who I am, right? My name is Danny Hernandez, if you were to Google Danny Hernandez, the number one search result is going to be a rapper. There is a rapper that actually just recently came out, he's not even that good, but he's a new one. A statistical machine learning technique would be able to say, "all right, given Daniel "and the context information I know about him, "when I look for Daniel Hernandez, "and I supplement the identity with that "contextual information, it means it's one of "the six that work at IBM." Right? >> Not the rapper. >> Not the rapper. >> Not the rapper. >> Exactly. I don't mind being matched with a rapper, but match me with a good rapper. >> All you've got to do is search Daniel Hernandez and The Cube and you'll find him. >> Ha, right. Bingo. Actually that's true. So, in any case, the AI based techniques basically allow you to isolate who I am, based on more features that you know about me, so that you get me right. Because if you can't even start there, with whom are you transacting, you're not going to have any hope of detecting fraud. Either that, or you're going to get false positives because you're going to associate me with someone that I'm not, and then it's just going to make me upset, because when you should be transacting with me, you're not because you're saying I'm someone I'm not. >> So, that ties back to what we were saying before, know you're customer and anti money laundering. Which, of course, was big, and still is, during the crypto craze. Maybe crypto is not as crazy, but that was a big deal when you had bitcoin at whatever it was. What are some practical applications for KYC AML that you're seeing in the field today? >> I think that what we see a lot of, what we're applying in my business is automating the discovery of data and learning about the lineage of that data. Where did it come from? This was a problem that was really hard to solve 18 months ago, because it took a lot of man power to do it. And as soon as you did it once, it was outdated. So, we've recently released some capabilities within Watson Knowledge Catalog that really help automate that, so that as the data continues to grow, and continues to change, as it always does, that rather than having two, three hundred business analysts or data stewards trying to go figure that out, machine learning can go do that for you. >> So, all the big banks are glomming on to this? >> Absolutely. >> So think about any customer onboarding, right? You better know who your customer is, and you better have provisions around anti money laundering. Otherwise, there's going to be some very serious downside risk. It's just one example of many, for sure. >> Let's talk about some of the data challenges because we talked a lot about digital, digital business, I've always said the difference between a business and a digital business is how they use data. So, what are some of the challenging issues that customers are facing, and particularly, incumbents, Ginni Rometty used the term a couple of events ago, and it might have even been World of Watson, incumbent disruptors, maybe that was the first think, which I thought was a very poignant term. So, what are some of the data challenges that these incumbents are facing, and how is IMB helping solve them? >> For us, one of them that we see is just understanding where their data is. There is a lot of dark data out there that they haven't discovered yet. And what impact is that having on their analytics, what opportunities aren't they taking advantage of, and what risks are they being exposed to by that being out there. Unstructured data is another big part of it as well. Structured data is sort of the easy answer to solving the data problem, >> [Daniel Hernandez] But still hard. >> But still hard. Unstructured data is something that almost feels like an afterthought a lot of times. But, the opportunities and risks there are equally, if not greater, to your business. >> So yeah, what you're saying it's an afterthought, because a lot of times people are saying, "that's too hard." >> Scott Buckles: Right. >> Forget it. >> Scott Buckles: Right. Right. Absolutely. >> Because there's gold in them there hills, right? >> Scott Buckles: Yeah, absolutely. >> So, how does IBM help solve that problem? Is it tooling, is it discovery tooling? >> Well, yeah, so we recently released a product called InstaScan, that helps you to go discover unstructured data within any cloud environment. So, that was released a couple months ago, that's a huge opportunity that we see where customers can actually go and discover that dark data, discover those risks. And then combine that with some of the capabilities that we do with structured data too, so you have a holistic view of where your data is, and start tying that together. >> If I could add, any company that has any operating history is going to have a pretty complex data environment. Any company that wants to employ AI has a fundamental choice. Either I bring my AI to the data, or I bring my data to the AI. Our competition demand that you bring your data to the AI, which is expensive, hard, often impossible. So, if you have any desire to employ this stuff, you had better take the I'm going to bring my AI to the data approach, or be prepared to deal with a multi-year deployment for this stuff. So, that principle difference in how we think about the problem, means that we can help our customers apply AI to problem sets that they otherwise couldn't because they would have to move. And in many cases, they're just abandoning projects all together because of that. >> So, now we're starting to get into sort of data strategy. So, let's talk about data strategy. So, it starts with, I guess, understanding the value of your data. >> [Daniel Hernandez] Start with understanding what you got. >> Yeah, what data do I have. What's the value of that data? How do I get to that data? You just mentioned you can't have a strategy that says, "okay, move all the data into some God box." >> Good luck. >> Yeah. That won't work. So, do customers have coherent data strategies? Are they formulating? Where are we on that maturity curve? >> Absolutely, I think the advent of the CDO role, as the Chief Data Officer role, has really helped bring the awareness that you have to have that enterprise data strategy. >> So, that's a sign. If there's a CDO in the house. >> There's someone working on enterprise, yeah, absolutely. >> So, it's really their role, the CDO's role, to construct the data strategy. >> Absolutely. And one of the challenges that we see, though, in that, is that because it is a new role, is like going back to Daniel's historical operational stuff, right? There's a lot of things you have to sort out within your data strategy of who owns the data, right? Regardless of where it sits within an enterprise, and how are you applying that strategy to those data assets across the business. And that's not an easy challenge. That goes back to the people process side of it. >> Well, right. I bet you if I asked Jim Cavanaugh what's IBM's data strategy, I bet you he'd have a really coherent answer. But I bet you if I asked Scott Hebner, the CMO of the data and AI group, I bet you I'd get a somewhat different answer. And so, there's multiple data strategies, but I guess it's (mumbles) job to make sure that they are coherent and tie in, right? >> Absolutely. >> Am I getting this? >> Absolutely. >> Quick study. >> So, what's IBM's data strategy? (laughs) >> Data is good. >> Data is good. Bring AI to the data. >> Look, I mean, data and AI, that's the name of the business, that's the name of the portfolio that represents our philosophy. No AI without data, increasingly, not a lot of value of data without AI. We have to help our customers understand this, that's a skill, education, point of view problem, and we have to deliver technology that actually works in the wild, in their environment, not as we want them to be, but as they are. Which is often messy. But I think that's our fun. It's the reason we've been here for a while. >> All right, I'll give you guys a last word, we got to run, but both Scott and Daniel, take aways from the event today, things that you're excited about, things that you learned. Just give us the bumper sticker. >> For me, you talk about whether people recognize the need for a data strategy in their role. For me, it's people being pumped about that, being excited about it, recognizing it, and wanting to solve those problems and leverage the capabilities that are out there. >> We've seen a lot of that today. >> Absolutely. And we're at a great time and place where the capabilities and the technologies with machine learning and AI are applicable and real, that they're solving those problems. So, I think that gets everybody excited, which is cool. >> Bring it home, Daniel. >> Excitement, a ton of experimentation with AI, some real issues that are getting in the way of full-scale deployments, a methodology data ops, to deal with those real hardcore data problems in the enterprise, resonating, a technology stack that allows you to implement that as a company is, through Cloud Pak for Data, no matter where they want to run is what they need, and I'm happy we're able to deliver it to them. >> Great. Great segment, guys. Thanks for coming. >> Awesome. Thank you. >> Data, applying AI to that data, scaling with the cloud, that's the innovation cocktail that we talk about all the time on The Cube. Scaling data your way, this is Dave Vellante and we're in Miami at the AI and Data Forum, brought to you by IBM. We'll be right back right after this short break. (upbeat music)