>>From around the globe. It's the cube covering data citizens. 21 brought to you by Colibra >>Welcome to the cubes coverage of Collibra data citizens 21. I'm Lisa Martin. I have three guests with me here today. Colibra customer Freddie Mac, please welcome JAG chief data officer and vice president of single family data and decisions. Jog. Welcome to the cube. >>Thank you, Lisa. Look forward to be, >>Uh, excellent on Kiko LSU as well. Vice president data transformation and analytics solution on Kay. Good to have you on the program. >>Thank you, Lisa. Great to be here and >>A teeth Malik senior director from the single family division at Freddie Mac is here as well. A team welcome. So we have big congratulations in order. Uh, pretty Mac was just announced at data citizens as the winners of the Colibra excellence award for data program of the year. Congratulations on that. We're going to unpack that. Talk about what that means, but I'd love to get familiar with the 3d Jack. Start with you. Talk to me a little bit about your background, your current role as chief data officer. >>Appreciate it, Lisa, thank you for the opportunity to share our story. Uh, my name is Arvind calls me Jack. And as you said, I'm just single-family chief data officer at Freddie Mac, but those that don't know, Freddie Mac is a Garland sponsored entity that supports the U S housing finance system and single family deals with the residential side of the marketplace, as CDO are responsible for our managed content data lineage, data governance, business architecture, which Cleaver plays a integral role, uh, in, in depth, that function as well as, uh, support our shared assets across the enterprise and our data monetization efforts, data, product execution, decision modeling, as well as our business intelligence capabilities, including AI and ML for various use cases as a background, starting my career in New York and then moved to Boston and last 20 years of living in the Northern Virginia DC area and fortunate to have been responsible for business operations, as well as led and, um, executed large transformation efforts. That background has reinforced the power of data and how, how it's so critical to meeting our business objectives. Look forward to our dialogue today, Lisa, once again. >>Excellent. You have a great background and clearly not a dull moment in your job with Freddy, Matt. And tell me a little bit about your background, your role, what you're doing at Freddie >>Mac. Definitely. Um, hi everyone. I'm,, I'm vice president of data transformation and analytics solutions. And I worked for JAG. I'm responsible for many of the things he said, including leading our transformation to the cloud and migrating all our existing data assets front of that transformation journey. I'm also responsible for our business information and business data architecture, decision modeling, business intelligence, and some of the analytics and artificial intelligence. I started my career back in the day as a computer engineer, but I've always been in the financial industry up in New York. And now in the Northern Virginia area, I called myself that bridge between business and technology. And I would say, I think over the last six years with data found that perfect spot where business and technology actually come together to solve real problems and, and really lead, um, you know, businesses to the next stage of, so thank you Lisa for the opportunity today. Excellent. >>And we're going to unpack you call yourself the bridge between business and it that's always such an important bridge. We're going to talk about that in just a minute, but I want to get your background, tell our audience about you. >>Uh, I'm Alec Malek, I'm senior director of business, data architecture, data transformation, and Freddie Mac. Uh, I'm responsible for the overall business data architecture and transformation of the existing data onto the cloud data lake. Uh, my team is responsible for the Kleberg platform and the business analysts that are using and maintaining the data in Libra and also driving the data architecture in close collaboration with our engineering teams. My background is I'm a engineer at heart. I still do a lot of development. This is my first time as of crossing over onto the bridge onto business side of maintaining data and working with data teams. >>Jan, let's talk about digital transformation. Freddie Mac is a 50 year old and growing company. I always love talking with established businesses about digital transformation. It's pretty challenging. Talk to me about your initial plan and what some of the main challenges were that you were looking to solve. >>Uh, great question, Lisa, and, uh, it's definitely pertinent as you say, in our digital world or figuring out how we need to accomplish it. If I look at our data, modernization is it is a major program and, uh, effort, uh, in, in our, in our division, what started as a reducing cost or looking at an infrastructure play, moving from physical data assets to the cloud, as well as enhancing our resiliency as quickly morphed into meeting business demand and objectives, whether it be for sourcing, servicing or securitization of our loan products. So where are we as we think about creating this digital data marketplace, we are, we are basically forming, empowering a new data ecosystem, which Columbia is definitely playing a major role. It's more than just a cloud native data lake, but it's bringing in some of our current assets and capabilities into this new data landscape. >>So as we think about creating an information hub, part of the challenges, as you say, 50 years of having millions of loans and millions of data across multiple assets, it's frigging out that you still have to care and feed legacy while you're building the new highway and figuring out how you best have to transform and translate and move data and assets to this new platform. What we've been striving for is looking at what is the business demand or what is the business use case, and what's the value to help prioritize that transformation. Exciting part is, as you think about new uses of acquiring and distribution of data, as well as news new use cases for prescriptive and predictive analytics, the power of what we're building in our daily, this new data ecosystem, we're feeling comfortable, we'll meet the business demand, but as any CTO will tell you demand is always, uh, outpaces our capacity. And that's why we want to be very diligent in terms of our execution plan. So we're very excited as to what we've accomplished so far this year and looking forward as we offered a remainder year. And as you go into 2022. Excellent, >>Thanks JAG. Uh, two books go to you. As I mentioned in the intro of that Freddie Mac has won the Culebra excellence award for data program of the year. Again, congratulations on that, but I'd love to understand the Kleber center of excellence that you're building at Freddie Mac. First of all, define what a center of excellence is to Freddie Mac and then what you're specifically building. Yeah, sure. >>So the Cleaver center of excellence provides us the overall framework from a people and process standpoint to focus in on our use of Colibra and for adopting best practices. Uh, we can have teams that are focused just on developing best practices and implementing workflows and lineage within Collibra and implementing and adopting a number of different aspects of Libra. It provides the central hub of people being domain experts on the tool that can then be leveraged by different groups within the organization to maintain, uh, the tool. >>Put another follow on question a T for you. How does Freddie Mac define, uh, dated citizens as anybody in finance or sales or marketing or operations? What does that definition of data citizen? >>It's really everyone it's within the organization. They all consume data in different ways and we provide a way of governing data and for them to get a better understanding of data from Collibra itself. So it's really everyone within the organization that way. >>Excellent. Okay. Let's go over to you a big topic at data citizens. 21 is collaboration. That's probably a word that we used a ton in the last 15 plus months or so it was every business really pivoted quickly to figure out how do we best collaborate. But something that you talked about in your intro is being the bridge between business and it, I want to understand from your perspective, how can data teams help to drive improved collaboration between business and it, >>The collaboration between business and technology have been a key focus area for us over the last few years, we actually started an agile transformation journey two years ago that we called modern delivery. And that was about moving away from project teams to persistent product teams that brought business and technology together. And we've really been able to pioneer that in the data space within Freddie Mac, where we have now teams with product owners coming from the data team and then full stack ID developers with them creating these combined teams to meet the business needs. We found that bringing these teams together really remove the barriers that were there in the interaction and the employee satisfaction has been high. And like you said, over the last 16 months with the pandemic, we've actually seen the productivity stay same or even go up because the teams were all working together, they work as a unit and they all have the sense of ownership versus working on a project that has a finite end date to fail. So we've, um, you know, we've been really lucky with having started this two years ago. Well, and >>That's great. And congratulations about either maintaining productivity or having it go up during the last 16 months, which had been incredibly challenging. Jack. I want to ask you what does winning this award from Collibra what does this mean to you and your team and does this signify that you're really establishing a data first culture? >>Great question, Lisa again. Um, I think winning the award, uh, just from a team standpoint, it's a great honor. Uh, Kleber has been a fantastic partner. And when I think about the journey of going from spread sheets, right, that all of us had in the past to now having all our business class returns lineage, and really being at the forefront of our data monetization. So as we think about moving to the cloud Beliebers step in step with us in terms of our integral part of that holistic delivery model, when I ultimately, as a CDO, it's really the team's honor and effort, cause this has been a multi-year journey to get here. And it's great that Libra as a, as a partner has helped us achieve some of these goals, but also recognized, um, where we are in terms of, uh, as looking at data as a product and some of our, um, leading forefront and using that holistic delivery, uh, to, uh, to meet our business objectives. So overall poorly jazzed when, uh, we've been found that we wanted the data program here at Collibra and very honored, um, uh, to, to win this award. That's >>Where we got to bring back I'm jazzed. I liked that jug sticking with you, let's unpack a little bit, some of those positive results, those business outcomes that you've seen so far from the data program. What are those? >>Yeah. So again, if you were thinking about a traditional CDO model, what were the terms that would have been used few years ago? It was around governance and may have been viewed as an oversight. Um, maybe less talking, um, monetization of what it was, the business values that you needed to accomplish collectively. It's really those three building blocks managing content. You got to trust the source, but ultimately it's empowering the business. So the best success that I could say at Freddy, as you're moving to this digital world, it's really empowering the business to figure out the new capabilities and demand and objectives that we're meeting. We're not going to be able to transform the mortgage industry. We're not going to be able or any, any industry, if we're still stuck in old world thinking, and ultimately data is going to be the blood that has to enable those capabilities. >>So if you tell me the business best success, we're no longer talking a okay, I got my data governance, what do we have to do? It's all embedded together. And as I alluded to that partnership between business and it informing that data is a product where you now you're delivering capabilities holistically from program teams all across data. It's no longer an afterthought. As I said, a few minutes ago, you're able to then meet the demand what's current. And how do we want to think about going forward? So it's no longer buzzwords of digital data marketplace. What is the value of that? And that's what the success, I think if our group collectively working across the organization, it's just not one team it's across the organization. Um, and we have our partners, our operations, everyone from business owners, all swimming in the same direction with, and I would say critical management support. So top of the house, our, our head of business, my, my boss was the COO full supportive in terms of how we're trying to execute and I've makes us, um, it's critical because when there is a potential, trade-offs, we're all looking at it collectively as an organization, >>Right. And that's the best viewpoint to have is that sort of centralized unified vision. And as you say, JAG, the support from, from up top, uh, I'd see if I want to ask you, you establish the Culebra center of excellence. What are you focused on now? >>So we really focused in allowing our users to consume data and understand data and really democratizing data so that they can really get a better understanding of that. So that's a lot of our focus and engaging with Collibra and getting them to start to define things in Colibra law form. That's a lot of focus right now. >>Excellent. Want to stay with you one more question and take that I'm gonna ask to all of you, what are you most excited about a lot of success that you've talked about transforming a legacy institution? What are you most excited about and what are the next steps for the data program? Uh, teak what's are your thoughts? >>Yeah, so really modernizing onto, uh, onto a cloud data lake and allowing all of the users and, uh, Freddie Mac to consume data with the level of governance that we need around. It is a exciting proposition for me. >>What would you say is most exciting to you? >>I'm really looking forward to the opportunities that artificial intelligence has to offer, not just in the augmented analytics space, but in the overall data management life cycle. There's still a lot of things that are manual in the data management space. And, uh, I personally believe, uh, artificial intelligence has a huge role to play there. And Jackson >>Question to you, it seems like you have a really strong collaborative team. You have a very collaborative relationship with management and with Collibra, what are you excited about? What's coming down the pipe. >>So Lisa, if I look at it, you know, we sit back here June, 2021, where were we a year ago? And you think about a lot of the capabilities and some of the advancements that we may just in a year sitting virtually using that word jazzed or induced or feeling really great about. We made a lot of accomplishments. I'm excited what we're going to be doing for the next year. So there's other use cases, and I could talk about AIML and OCHA talks about, you know, our new ecosystem. Seeing those use cases come to fruition so that we're, we are contributing to value from a business standpoint. The organization is what really keeps me up. Uh, keeps me up at night. It gets me up in the morning and I'm really feeling dues for the entire division. Excellent. >>Well, thank you. I want to thank all three of you for joining me today. Talking about the successes that Freddie Mac has had transforming in partnership with Colibra again, congratulations on the Culebra excellence award for the data program. It's been a pleasure talking to all three of you. I'm Lisa Martin. You're watching the cubes coverage of Collibra data citizens 21.

>> Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Welcome back here at AWS re:Invent. Day three of our coverage here on theCUBE. We have been here since Tuesday, bringing you all kind of sights and insights from the show floor here. Some 40 guests that we've had on this set alone. Have a person that's actually four sets around here. There's a lot of content to capture. A lot of excitement in the air. And I'm John, that's Rebecca. I don't have to tell you that, you know that. We're joined by Ankit Khandelwal, who's the Senior Director of Engineering to Kyvos Insights. Good to see you, Ankit. >> Thank you, good to be here. >> And Ajay Anand, who's the Vice President of Products and Marketing at Kyvos as well. Thank you for joining us gentlemen. We appreciate the time. >> It's good to be back with you. >> All right, so share a little bit, just for folks at home who are watching and may not be familiar with Kyvos. I doubt there are many. (Rebecca laughing) But just in case, share with us a little bit, and with them, your core mission. >> Yeah, so what Kyvos does is we deliver the capability of doing instant business intelligence on data at a massive scale, either on-premises or in the cloud. So, one of the big problems people have is when they're trying to connect from their BI tools to huge amounts of data, it takes a long time for the data to come back into the tool. As they are dragging and dropping, they don't get that interactive response. So we solve that by building a BI consumption layer on top of the big data. And what that enables you to do is, you know, once we've pre-processed that data and built multi-dimensional cubes, then you can get that interactive response time, right. So the core technology is OLAP, which has been around for a long time. But what we do is we make OLAP scale to huge amounts of data and really take advantage of the capabilities of the cloud, or big clusters, and on-premises environments, and really scale out with the cloud. >> Can you give us some examples of who your customers are and the kind of specific problems you're solving for them? >> Sure, some of our customers have spoken publicly about us, so I can share what they said. Walgreens spoke about us at the Tableau Conference just a couple of weeks ago. And they're solving problems that they had never imagined they'd be able to solve before. Dealing with hundreds of billions of rows of data and getting instant responses. And these customers are building multi-dimensional cubes at a scale that's never been done before. 100 terabyte cubes. Walgreens is an example of that. Verizon has spoken about us at other conferences as well. >> Ankit, I'd like to know what your take is on, as we were just talking about, the volume that you're dealing with here. Like never before. How do you help your customers figure out what matters? What's important and what's not, because most, or I shouldn't say, much of what they generate really doesn't matter, and yet there are some valuable nuggets in there that they are still trying to extract and then analyze appropriately. So how do you help them with that job? >> Yeah, so you know what happens is organizations and enterprises keep getting more and more data. They take it to a data lake. Now, the data on the ground wasn't enough, and now you have other services which helps you get the data from even space. Andy announced that you can get data from satellite. So all this data. Now once that data reaches the data lake, the next challenge that comes to, or in front of a business user is, how do you really get the ROI out of it? Now when I say ROI, basically know I am talking about ROI of data. And the ROI of data actually improves, comes only when, the data goes in the hands of the business user. So that's the times Kyvos comes into the picture because you want your data and you want your business users to analyze it. It has to be super fast and that's what Kyvos does, number one, and number two, the business users want their data to see in a way that they want. So basically, Kyvos helps you to actually define a semantic layer, put a business view on top of your data. So that a business user actually sees the data the way they want. So those are the things that Kyvos provides and helps the business user to actually get the insights out of the data. >> So this week at AWS, you launched Version 5. Tell our viewers a little bit more about what Version 5 entails, some of the capacities. >> Right, so one big thing is the capability to do Elastic OLAP in the cloud. So the OLAP capability being able to really leverage the infrastructure cost-effectively, scale out to deal with big loads and scale it down as you're building these multi-dimensional cubes. So really being able to deal with the infrastructure cost-effectively and deal with massive amounts of data as you're building these cubes. So you can decide, I want to build a 100 terabyte cube and just spin up the right amount of infrastructure that you need to build that cube and then shrink it down. So that elastic capability both for cube building as well as querying. At Walgreens, they talk about dealing with hundreds and thousands of users both internal and external all connecting to this data using Tableau or some other BI tool, and being able to deliver that instant response to them. So having that elastic capability is the new capability we're offering. >> I think the point is, as Andy was talking about in his yesterday's keynote, if you can do it fast then why not do it fast? I think that's where cloud comes into the picture. That with our Kyvos 5 release, once you set up your Kyvos on the cloud, it could actually use that scalability or the elasticity of the cloud for its benefit and for the benefit of the customer. As the load increases, is that the complexity increases. We could actually scale out and deliver the performance that we promised to deliver. And then once the load actually reduces then we could again reduces the resources that we're consuming and that's how we actually reduce the cost that is borne by the customer. So essentially, that is again, you're now giving them better ROI on the hardware that they're investing on. >> So how do you pump the breaks a little bit on the speed? I mean, in terms of making sure that you're in control? Because speed's one thing, right, very important to have, but we need reliability, you need accuracy, latency is not as much of an issue, but how do you, pump the brakes might not be the right description, but how do you ensure that speed is not an inhibitor and it's actually a facilitator? >> There's a whole bunch of enterprise capabilities that we have to provide. Dealing with the resilience so that it's always available to their business users. Dealing with concurrency as you really scale out with the large numbers of users. Dealing with security, right. So as I mentioned, at Walgreens they've got external users as well as internal users, all accessing the same cube, and they all need to see only what they're allowed to see, right. So we maintain that security, right from the user to the data, and we keep track of who's allowed to see what and expose only that. So all of those capabilities are built into the product. >> And as an engineer, I can actually say that again I would take the code from Warner this morning that, hey, you really architecture it well. So architected the product right from the beginning to not only deliver the performance but also to be scalable, deliver performance at a scale. To be secure and then in order to be reliable, fault ordering. So those things are inherently built into the product but then putting a patch on top of the product. >> We're hearing so much at this conference that many enterprises have really had the ah-ha moment. I need to go to the cloud. The security, the governance, those concerns are really falling by the wayside. So what's next? I mean, now that we have so many companies migrating, where do we go from here? >> I think, what we are seeing is a lot of companies are still in the process of migrating. So they've had on-premises infrastructures. Now they're moving to a hybrid cloud and then moving to potentially everything in the cloud. So delivering a seamless experience to the business user is extremely important. Business users shouldn't have to care whether the data is on premises or in a hybrid cloud or in the cloud itself. They should get that same interactive response, the same familiar user interface, and that's what our BI layer provides. By delivering that consumption layer that sits the same way on premises as it was in the cloud. It's a completely seamless experience for the user. >> And I think the performance or the skills still presents a problem. The thing is, how can you make it easy to use for the user? How can I make it smarter? So I think that's where we are going towards with our latest releases, with Kyvos 5. We're bring certain capabilities into the product so that the user doesn't have to bother about how do you really create that semantic layer. The product is smart enough to tell there what should be included in there and what to leave out of it. So smartness is one area which we are moving towards so that we can help the business user to get the performance at a scale with a lot of ease of use. >> I assume you guys have been here for a day or two, correct? >> Yes. >> Right, you met with a lot of customers. I again would assume, right? >> Right. >> So what is your take-away going to be from those direct conversations you've had here in terms of what you take back to Kyvos and maybe start putting into practice? What are you hearing about, this is my next roadblock, this is my next barrier, this is what I'm going to come to you to help me fix. >> We heard Andy's talk this morning or was it maybe Warner. >> Yesterday, Warner this morning, yeah. >> So Warner's talk where they talk about, 95% of what goes into AWS comes from feedback from their customers, and that's true with us to a large extent. We learn from our customers, as they deploy these cubes and their environments, but what's important to them. What are the critical areas that we need to overcome. Really understanding their business use cases and making sure that we build that smartness into the product so we can see what kind of intelligence are they looking to gather, what kind of analysis are they looking to do. And then we use that to build the smartness into the cube. So that the user doesn't need to figure this out themselves. So that's one of the new capabilities that we are providing and we're continuing to work on, is to build more and more smartness into the product. So it helps the user go where they want to go. >> And I think as we go to cloud, specifically AWS, how can we really use the services required by the cloud and then how can we really provide a layer of extraction on top of what is already there, so that then it becomes really easy for the user to use whatever we are providing. >> Right. >> Great. Yeah, just, and I don't want to convolute this with things that I don't need and time and effort. It's all about money at the end of the day, right? Save me money, save me time. >> Well, it's not just saving money but really the topline benefit, right. So expanding the business opportunity. So, we've got a bank that's doing risk analysis as they look for new investments. It used to take them days to do that risk analysis before they could make a decision. Now they can do it in seconds. So their ability to make a decision much faster and react to market conditions, really opens the door for them for much greater business opportunity and revenue. So it's not just cost savings that's driving this. It's taking advantage of the opportunity. >> You bet. >> Because if the queries don't really come fast. Let's say you as a person sitting here and you fire a query and then it takes a lot of time, and you go back and then have a cup of coffee and then come back. Your chain of thought's actually broken. So you cannot explore from the data otherwise you could integrate it'll actually come within seconds. >> Gentlemen, thank you for being here with us. I hope the show's gone well for you. It sure does sound like it's been a success, and we look forward to seeing you down the road. >> Great. >> Thank you. >> Good to be here. >> Thanks. >> From Kyvos. >> Back with more in just a bit here on theCUBE. You're watching AWS re:Invent. (bright music)