(gentle music) >> Narrator: "TheCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Hello everybody, welcome back to day four of "theCUBE's" coverage of MWC '23. We're here live at the Fira in Barcelona. Wall-to-wall coverage, John Furrier is in our Palo Alto studio, banging out all the news. Really, the whole week we've been talking about the disaggregation of the telco network, the new opportunities in telco. We're really excited to have AT&T and Snowflake here. Dave Whittington is the AVP, at the Chief Data Office at AT&T. Roddy Tranum is the Assistant Vice President, for Channel Performance Data and Tools at AT&T. And Phil Kippen, the Global Head Of Industry-Telecom at Snowflake, Snowflake's new telecom business. Snowflake just announced earnings last night. Typical Scarpelli, they beat earnings, very conservative guidance, stocks down today, but we like Snowflake long term, they're on that path to 10 billion. Guys, welcome to "theCUBE." Thanks so much >> Phil: Thank you. >> for coming on. >> Dave and Roddy: Thanks Dave. >> Dave, let's start with you. The data culture inside of telco, We've had this, we've been talking all week about this monolithic system. Super reliable. You guys did a great job during the pandemic. Everything shifting to landlines. We didn't even notice, you guys didn't miss a beat. Saved us. But the data culture's changing inside telco. Explain that. >> Well, absolutely. So, first of all IoT and edge processing is bringing forth new and exciting opportunities all the time. So, we're bridging the world between a lot of the OSS stuff that we can do with edge processing. But bringing that back, and now we're talking about working, and I would say traditionally, we talk data warehouse. Data warehouse and big data are now becoming a single mesh, all right? And the use cases and the way you can use those, especially I'm taking that edge data and bringing it back over, now I'm running AI and ML models on it, and I'm pushing back to the edge, and I'm combining that with my relational data. So that mesh there is making all the difference. We're getting new use cases that we can do with that. And it's just, and the volume of data is immense. >> Now, I love ChatGPT, but I'm hoping your data models are more accurate than ChatGPT. I never know. Sometimes it's really good, sometimes it's really bad. But enterprise, you got to be clean with your AI, don't you? >> Not only you have to be clean, you have to monitor it for bias and be ethical about it. We're really good about that. First of all with AT&T, our brand is Platinum. We take care of that. So, we may not be as cutting-edge risk takers as others, but when we go to market with an AI or an ML or a product, it's solid. >> Well hey, as telcos go, you guys are leaning into the Cloud. So I mean, that's a good starting point. Roddy, explain your role. You got an interesting title, Channel Performance Data and Tools, what's that all about? >> So literally anything with our consumer, retail, concenters' channels, all of our channels, from a data perspective and metrics perspective, what it takes to run reps, agents, all the way to leadership levels, scorecards, how you rank in the business, how you're driving the business, from sales, service, customer experience, all that data infrastructure with our great partners on the CDO side, as well as Snowflake, that comes from my team. >> And that's traditionally been done in a, I don't mean the pejorative, but we're talking about legacy, monolithic, sort of data warehouse technologies. >> Absolutely. >> We have a love-hate relationship with them. It's what we had. It's what we used, right? And now that's evolving. And you guys are leaning into the Cloud. >> Dramatic evolution. And what Snowflake's enabled for us is impeccable. We've talked about having, people have dreamed of one data warehouse for the longest time and everything in one system. Really, this is the only way that becomes a reality. The more you get in Snowflake, we can have golden source data, and instead of duplicating that 50 times across AT&T, it's in one place, we just share it, everybody leverages it, and now it's not duplicated, and the process efficiency is just incredible. >> But it really hinges on that separation of storage and compute. And we talk about the monolithic warehouse, and one of the nightmares I've lived with, is having a monolithic warehouse. And let's just go with some of my primary, traditional customers, sales, marketing and finance. They are leveraging BSS OSS data all the time. For me to coordinate a deployment, I have to make sure that each one of these units can take an outage, if it's going to be a long deployment. With the separation of storage, compute, they own their own compute cluster. So I can move faster for these people. 'Cause if finance, I can implement his code without impacting finance or marketing. This brings in CI/CD to more reality. It brings us faster to market with more features. So if he wants to implement a new comp plan for the field reps, or we're reacting to the marketplace, where one of our competitors has done something, we can do that in days, versus waiting weeks or months. >> And we've reported on this a lot. This is the brilliance of Snowflake's founders, that whole separation >> Yep. >> from compute and data. I like Dave, that you're starting with sort of the business flexibility, 'cause there's a cost element of this too. You can dial down, you can turn off compute, and then of course the whole world said, "Hey, that's a good idea." And a VC started throwing money at Amazon, but Redshift said, "Oh, we can do that too, sort of, can't turn off the compute." But I want to ask you Phil, so, >> Sure. >> it looks from my vantage point, like you're taking your Data Cloud message which was originally separate compute from storage simplification, now data sharing, automated governance, security, ultimately the marketplace. >> Phil: Right. >> Taking that same model, break down the silos into telecom, right? It's that same, >> Mm-hmm. >> sorry to use the term playbook, Frank Slootman tells me he doesn't use playbooks, but he's not a pattern matcher, but he's a situational CEO, he says. But the situation in telco calls for that type of strategy. So explain what you guys are doing in telco. >> I think there's, so, what we're launching, we launched last week, and it really was three components, right? So we had our platform as you mentioned, >> Dave: Mm-hmm. >> and that platform is being utilized by a number of different companies today. We also are adding, for telecom very specifically, we're adding capabilities in marketplace, so that service providers can not only use some of the data and apps that are in marketplace, but as well service providers can go and sell applications or sell data that they had built. And then as well, we're adding our ecosystem, it's telecom-specific. So, we're bringing partners in, technology partners, and consulting and services partners, that are very much focused on telecoms and what they do internally, but also helping them monetize new services. >> Okay, so it's not just sort of generic Snowflake into telco? You have specific value there. >> We're purposing the platform specifically for- >> Are you a telco guy? >> I am. You are, okay. >> Total telco guy absolutely. >> So there you go. You see that Snowflake is actually an interesting organizational structure, 'cause you're going after verticals, which is kind of rare for a company of your sort of inventory, I'll say, >> Absolutely. >> I don't mean that as a negative. (Dave laughs) So Dave, take us through the data journey at AT&T. It's a long history. You don't have to go back to the 1800s, but- (Dave laughs) >> Thank you for pointing out, we're a 149-year-old company. So, Jesse James was one of the original customers, (Dave laughs) and we have no longer got his data. So, I'll go back. I've been 17 years singular AT&T, and I've watched it through the whole journey of, where the monolithics were growing, when the consolidation of small, wireless carriers, and we went through that boom. And then we've gone through mergers and acquisitions. But, Hadoop came out, and it was going to solve all world hunger. And we had all the aspects of, we're going to monetize and do AI and ML, and some of the things we learned with Hadoop was, we had this monolithic warehouse, we had this file-based-structured Hadoop, but we really didn't know how to bring this all together. And we were bringing items over to the relational, and we were taking the relational and bringing it over to the warehouse, and trying to, and it was a struggle. Let's just go there. And I don't think we were the only company to struggle with that, but we learned a lot. And so now as tech is finally emerging, with the cloud, companies like Snowflake, and others that can handle that, where we can create, we were discussing earlier, but it becomes more of a conducive mesh that's interoperable. So now we're able to simplify that environment. And the cloud is a big thing on that. 'Cause you could not do this on-prem with on-prem technologies. It would be just too cost prohibitive, and too heavy of lifting, going back and forth, and managing the data. The simplicity the cloud brings with a smaller set of tools, and I'll say in the data space specifically, really allows us, maybe not a single instance of data for all use cases, but a greatly reduced ecosystem. And when you simplify your ecosystem, you simplify speed to market and data management. >> So I'm going to ask you, I know it's kind of internal organizational plumbing, but it'll inform my next question. So, Dave, you're with the Chief Data Office, and Roddy, you're kind of, you all serve in the business, but you're really serving the, you're closer to those guys, they're banging on your door for- >> Absolutely. I try to keep the 130,000 users who may or may not have issues sometimes with our data and metrics, away from Dave. And he just gets a call from me. >> And he only calls when he has a problem. He's never wished me happy birthday. (Dave and Phil laugh) >> So the reason I asked that is because, you describe Dave, some of the Hadoop days, and again love-hate with that, but we had hyper-specialized roles. We still do. You've got data engineers, data scientists, data analysts, and you've got this sort of this pipeline, and it had to be this sequential pipeline. I know Snowflake and others have come to simplify that. My question to you is, how is that those roles, how are those roles changing? How is data getting closer to the business? Everybody talks about democratizing business. Are you doing that? What's a real use example? >> From our perspective, those roles, a lot of those roles on my team for years, because we're all about efficiency, >> Dave: Mm-hmm. >> we cut across those areas, and always have cut across those areas. So now we're into a space where things have been simplified, data processes and copying, we've gone from 40 data processes down to five steps now. We've gone from five steps to one step. We've gone from days, now take hours, hours to minutes, minutes to seconds. Literally we're seeing that time in and time out with Snowflake. So these resources that have spent all their time on data engineering and moving data around, are now freed up more on what they have skills for and always have, the data analytics area of the business, and driving the business forward, and new metrics and new analysis. That's some of the great operational value that we've seen here. As this simplification happens, it frees up brain power. >> So, you're pumping data from the OSS, the BSS, the OKRs everywhere >> Everywhere. >> into Snowflake? >> Scheduling systems, you name it. If you can think of what drives our retail and centers and online, all that data, scheduling system, chat data, call center data, call detail data, all of that enters into this common infrastructure to manage the business on a day in and day out basis. >> How are the roles and the skill sets changing? 'Cause you're doing a lot less ETL, you're doing a lot less moving of data around. There were guys that were probably really good at that. I used to joke in the, when I was in the storage world, like if your job is bandaging lungs, you need to look for a new job, right? So, and they did and people move on. So, are you able to sort of redeploy those assets, and those people, those human resources? >> These folks are highly skilled. And we were talking about earlier, SQL hasn't gone away. Relational databases are not going away. And that's one thing that's made this migration excellent, they're just transitioning their skills. Experts in legacy systems are now rapidly becoming experts on the Snowflake side. And it has not been that hard a transition. There are certainly nuances, things that don't operate as well in the cloud environment that we have to learn and optimize. But we're making that transition. >> Dave: So just, >> Please. >> within the Chief Data Office we have a couple of missions, and Roddy is a great partner and an example of how it works. We try to bring the data for democratization, so that we have one interface, now hopefully know we just have a logical connection back to these Snowflake instances that we connect. But we're providing that governance and cleansing, and if there's a business rule at the enterprise level, we provide it. But the goal at CDO is to make sure that business units like Roddy or marketing or finance, that they can come to a platform that's reliable, robust, and self-service. I don't want to be in his way. So I feel like I'm providing a sub-level of platform, that he can come to and anybody can come to, and utilize, that they're not having to go back and undo what's in Salesforce, or ServiceNow, or in our billers. So, I'm sort of that layer. And then making sure that that ecosystem is robust enough for him to use. >> And that self-service infrastructure is predominantly through the Azure Cloud, correct? >> Dave: Absolutely. >> And you work on other clouds, but it's predominantly through Azure? >> We're predominantly in Azure, yeah. >> Dave: That's the first-party citizen? >> Yeah. >> Okay, I like to think in terms sometimes of data products, and I know you've mentioned upfront, you're Gold standard or Platinum standard, you're very careful about personal information. >> Dave: Yeah. >> So you're not trying to sell, I'm an AT&T customer, you're not trying to sell my data, and make money off of my data. So the value prop and the business case for Snowflake is it's simpler. You do things faster, you're in the cloud, lower cost, et cetera. But I presume you're also in the business, AT&T, of making offers and creating packages for customers. I look at those as data products, 'cause it's not a, I mean, yeah, there's a physical phone, but there's data products behind it. So- >> It ultimately is, but not everybody always sees it that way. Data reporting often can be an afterthought. And we're making it more on the forefront now. >> Yeah, so I like to think in terms of data products, I mean even if the financial services business, it's a data business. So, if we can think about that sort of metaphor, do you see yourselves as data product builders? Do you have that, do you think about building products in that regard? >> Within the Chief Data Office, we have a data product team, >> Mm-hmm. >> and by the way, I wouldn't be disingenuous if I said, oh, we're very mature in this, but no, it's where we're going, and it's somewhat of a journey, but I've got a peer, and their whole job is to go from, especially as we migrate from cloud, if Roddy or some other group was using tables three, four and five and joining them together, it's like, "Well look, this is an offer for data product, so let's combine these and put it up in the cloud, and here's the offer data set product, or here's the opportunity data product," and it's a journey. We're on the way, but we have dedicated staff and time to do this. >> I think one of the hardest parts about that is the organizational aspects of it. Like who owns the data now, right? It used to be owned by the techies, and increasingly the business lines want to have access, you're providing self-service. So there's a discussion about, "Okay, what is a data product? Who's responsible for that data product? Is it in my P&L or your P&L? Somebody's got to sign up for that number." So, it sounds like those discussions are taking place. >> They are. And, we feel like we're more the, and CDO at least, we feel more, we're like the guardians, and the shepherds, but not the owners. I mean, we have a role in it all, but he owns his metrics. >> Yeah, and even from our perspective, we see ourselves as an enabler of making whatever AT&T wants to make happen in terms of the key products and officers' trade-in offers, trade-in programs, all that requires this data infrastructure, and managing reps and agents, and what they do from a channel performance perspective. We still ourselves see ourselves as key enablers of that. And we've got to be flexible, and respond quickly to the business. >> I always had empathy for the data engineer, and he or she had to service all these different lines of business with no business context. >> Yeah. >> Like the business knows good data from bad data, and then they just pound that poor individual, and they're like, "Okay, I'm doing my best. It's just ones and zeros to me." So, it sounds like that's, you're on that path. >> Yeah absolutely, and I think, we do have refined, getting more and more refined owners of, since Snowflake enables these golden source data, everybody sees me and my organization, channel performance data, go to Roddy's team, we have a great team, and we go to Dave in terms of making it all happen from a data infrastructure perspective. So we, do have a lot more refined, "This is where you go for the golden source, this is where it is, this is who owns it. If you want to launch this product and services, and you want to manage reps with it, that's the place you-" >> It's a strong story. So Chief Data Office doesn't own the data per se, but it's your responsibility to provide the self-service infrastructure, and make sure it's governed properly, and in as automated way as possible. >> Well, yeah, absolutely. And let me tell you more, everybody talks about single version of the truth, one instance of the data, but there's context to that, that we are taking, trying to take advantage of that as we do data products is, what's the use case here? So we may have an entity of Roddy as a prospective customer, and we may have a entity of Roddy as a customer, high-value customer over here, which may have a different set of mix of data and all, but as a data product, we can then create those for those specific use cases. Still point to the same data, but build it in different constructs. One for marketing, one for sales, one for finance. By the way, that's where your data engineers are struggling. >> Yeah, yeah, of course. So how do I serve all these folks, and really have the context-common story in telco, >> Absolutely. >> or are these guys ahead of the curve a little bit? Or where would you put them? >> I think they're definitely moving a lot faster than the industry is generally. I think the enabling technologies, like for instance, having that single copy of data that everybody sees, a single pane of glass, right, that's definitely something that everybody wants to get to. Not many people are there. I think, what AT&T's doing, is most definitely a little bit further ahead than the industry generally. And I think the successes that are coming out of that, and the learning experiences are starting to generate momentum within AT&T. So I think, it's not just about the product, and having a product now that gives you a single copy of data. It's about the experiences, right? And now, how the teams are getting trained, domains like network engineering for instance. They typically haven't been a part of data discussions, because they've got a lot of data, but they're focused on the infrastructure. >> Mm. >> So, by going ahead and deploying this platform, for platform's purpose, right, and the business value, that's one thing, but also to start bringing, getting that experience, and bringing new experience in to help other groups that traditionally hadn't been data-centric, that's also a huge step ahead, right? So you need to enable those groups. >> A big complaint of course we hear at MWC from carriers is, "The over-the-top guys are killing us. They're riding on our networks, et cetera, et cetera. They have all the data, they have all the client relationships." Do you see your client relationships changing as a result of sort of your data culture evolving? >> Yes, I'm not sure I can- >> It's a loaded question, I know. >> Yeah, and then I, so, we want to start embedding as much into our network on the proprietary value that we have, so we can start getting into that OTT play, us as any other carrier, we have distinct advantages of what we can do at the edge, and we just need to start exploiting those. But you know, 'cause whether it's location or whatnot, so we got to eat into that. Historically, the network is where we make our money in, and we stack the services on top of it. It used to be *69. >> Dave: Yeah. >> If anybody remembers that. >> Dave: Yeah, of course. (Dave laughs) >> But you know, it was stacked on top of our network. Then we stack another product on top of it. It'll be in the edge where we start providing distinct values to other partners as we- >> I mean, it's a great business that you're in. I mean, if they're really good at connectivity. >> Dave: Yeah. >> And so, it sounds like it's still to be determined >> Dave: Yeah. >> where you can go with this. You have to be super careful with private and for personal information. >> Dave: Yep. >> Yeah, but the opportunities are enormous. >> There's a lot. >> Yeah, particularly at the edge, looking at, private networks are just an amazing opportunity. Factories and name it, hospital, remote hospitals, remote locations. I mean- >> Dave: Connected cars. >> Connected cars are really interesting, right? I mean, if you start communicating car to car, and actually drive that, (Dave laughs) I mean that's, now we're getting to visit Xen Fault Tolerance people. This is it. >> Dave: That's not, let's hold the traffic. >> Doesn't scare me as much as we actually learn. (all laugh) >> So how's the show been for you guys? >> Dave: Awesome. >> What're your big takeaways from- >> Tremendous experience. I mean, someone who doesn't go outside the United States much, I'm a homebody. The whole experience, the whole trip, city, Mobile World Congress, the technologies that are out here, it's been a blast. >> Anything, top two things you learned, advice you'd give to others, your colleagues out in general? >> In general, we talked a lot about technologies today, and we talked a lot about data, but I'm going to tell you what, the accelerator that you cannot change, is the relationship that we have. So when the tech and the business can work together toward a common goal, and it's a partnership, you get things done. So, I don't know how many CDOs or CIOs or CEOs are out there, but this connection is what accelerates and makes it work. >> And that is our audience Dave. I mean, it's all about that alignment. So guys, I really appreciate you coming in and sharing your story in "theCUBE." Great stuff. >> Thank you. >> Thanks a lot. >> All right, thanks everybody. Thank you for watching. I'll be right back with Dave Nicholson. Day four SiliconANGLE's coverage of MWC '23. You're watching "theCUBE." (gentle music)

(atmospheric music) >> Welcome to theCUBE Coverage of Data Citizens 2022 Collibra's Customer event. My name is Dave Vellante. With us is Kirk Haslbeck, who's the Vice President of Data Quality of Collibra. Kirk, good to see you, welcome. >> Thanks for having me, Dave. Excited to be here. >> You bet. Okay, we're going to discuss data quality, observability. It's a hot trend right now. You founded a data quality company, OwlDQ, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >> Yeah, absolutely. It's definitely exciting times for data quality which you're right, has been around for a long time. So why now? And why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before, and the variety has changed and the volume has grown. And while I think that remains true there are a couple other hidden factors at play that everyone's so interested in as to why this is becoming so important now. And I guess you could kind of break this down simply and think about if Dave you and I were going to build a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, what the ramifications could be, what those incidents would look like. Or maybe better yet, we try to build a new trading algorithm with a crossover strategy where the 50 day crosses the 10 day average. And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, kind of starts there, where everybody's realizing that we're all data companies, and if we are using bad data we're likely making incorrect business decisions. But I think there's kind of two other things at play. I bought a car not too long ago and my dad called and said, "How many cylinders does it have?" And I realized in that moment, I might have failed him cause I didn't know. And I used to ask those types of questions about any lock breaks and cylinders, and if it's manual or automatic. And I realized, I now just buy a car that I hope works. And it's so complicated with all the computer chips. I really don't know that much about it. And that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the individuals loading and consuming all of this data for the company actually may not know that much about the data itself and that's not even their job anymore. So, we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >> You know, the other thing too about data quality, and for years we did the MIT, CDO, IQ event. We didn't do it last year at COVID, messed everything up. But the observation I would make there, your thoughts is, data quality used to be information quality, used to be this back office function, and then it became sort of front office with financial services, and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well they sort of flipped the bit from sort of a data as a risk to data as an asset. And now as we say, we're going to talk about observability. And so it's really become front and center, just the whole quality issue because data's so fundamental, hasn't it? >> Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my favorite stock ticker app, and I check out the Nasdaq market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And that's kind of what's going on. There's so many numbers and they're coming from all of these different sources, and data providers, and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before Collibra. And what's been so exciting is, we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting, and why I think the CDO is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale, and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's not ever going to be based on one or two domain experts anymore. >> So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they cousins? What's your perspective on that? >> Yeah, it's super interesting. It's an emerging market. So the language is changing, a lot of the topic and areas changing. The way that I like to say it or break it down because the lingo is constantly moving, as a target on the space is really breaking records versus breaking trends. And I could write a condition when this thing happens it's wrong, and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. Everybody's talking about fresh data and stale data, and why would that matter? Well, if your data never arrived, or only part of it arrived, or didn't arrive on time, it's likely stale, and there will not be a condition that you could write that would show you all the good and the bads. That was kind of your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data. But it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there, there's more than a couple of these happening every day. >> So what's the Collibra angle on all this stuff? Made the acquisition, you got data quality, observability coming together. You guys have a lot of expertise in this area, but you hear providence of data. You just talked about stale data, the whole trend toward realtime. How is Collibra approaching the problem and what's unique about your approach? >> Well I think where we're fortunate is with our background. Myself and team, we sort of lived this problem for a long time in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with, before it was called data observability or reliability, was basically the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution. It's more advanced than some of the observation techniques that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights. And they want to see break records and breaking trends together, so they can correlate the root cause. And we hear that all the time. "I have so many things going wrong just show me the big picture. Help me find the thing that if I were to fix it today would make the most impact." So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows you can actually achieve total data governance. At this point with the acquisition of what was a Lineage company years ago, and then my company OwlDQ, now Collibra Data Quality. Collibra may be the best positioned for total data governance and intelligence in the space. >> Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was. They would just say, "Oh, it's a glitch." So they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you got to announce new products, right? It is your yearly event. What's new? Give us a sense as to what products are coming out but specifically around data quality and observability. >> Absolutely. There's this, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and BigQuery, and Databricks, Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook into these databases, and while we've always worked with the same databases in the past they're supported today. We're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now? Is everyone's concerned with something called Egress. Did my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? And with these native integrations that we're building and about to unveil here as kind of a sneak peak for next week at Data Citizens, we're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >> So this is interesting because what you just described, you mentioned Snowflake, you mentioned Google, oh actually you mentioned yeah, Databricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool. But then Google's got the open data cloud. If you heard, Google next. And now Databricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way, up until now I'm hearing, to really understand the relationships between all those and have confidence across, it's like yamarket AMI, you should just be a note on the mesh. I don't care if it's a data warehouse or a data lake, or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And that's what you're bringing to the table. Is that right? Did I get that right? >> Yeah, that's right. And it's, for us, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now we can send them the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network cost, zero egress cost, zero latency of time. And so when you were to log into BigQuery tomorrow using our tool, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage in access, privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there just like all of the major brands that you mentioned but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And we think that this positions us to be the leader there. >> I love this example because, we've got talks about well the cloud guys you're going to own the world. And of course now we're seeing that the ecosystem is finding so much white space to add value connect across cloud. Sometimes we call it super cloud and so, or inter clouding. Alright, Kirk, give us your final thoughts on the trends that we've talked about and data Citizens 22. >> Absolutely. Well I think, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there they want to know where everything is, where their sensitive data is, if it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're going to see more one click solutions, more SaaS based solutions, and solutions that hopefully prove faster time to value on all of these modern cloud platforms. >> Excellent. All right, Kirk Haslbeck, thanks so much for coming on theCUBE and previewing Data Citizens 22. Appreciate it. >> Thanks for having me, Dave. >> You're welcome. All right. And thank you for watching. Keep it right there for more coverage from theCUBE. (atmospheric music)

>>Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions and they were largely confined to regulatory regulated industries that had to comply with public policy mandates. But as the cloud went mainstream, the tech giants showed us how valuable data could become and the value proposition for data quality and trust. It evolved from primarily a compliance driven issue to becoming a lynchpin of competitive advantage. But data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper specialized skills to develop data architectures and processes to serve the myriad data needs of organizations. >>And it resulted in a lot of frustration with data initiatives for most organizations that didn't have the resources of the cloud guys and the social media giants to really attack their data problems and turn data into gold. This is why today for example, this quite a bit of momentum to rethinking monolithic data architectures. You see, you hear about initiatives like data mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business Uni users, you hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that, but also how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. >>In other words, while it's enticing to experiment and run fast and loose with data initiatives kinda like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated. And intelligence governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is gonna use data that isn't trusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. >>Hello and welcome to the Cube's coverage of Data Citizens made possible by Calibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Ante and I'm one of the hosts of our program, which is running in parallel to data citizens. Now at the Cube we like to say we extract the signal from the noise, and over the, the next couple of days, we're gonna feature some of the themes from the keynote speakers at Data Citizens and we'll hear from several of the executives. Felix Von Dala, who is the co-founder and CEO of Collibra, will join us along with one of the other founders of Collibra, Stan Christians, who's gonna join my colleague Lisa Martin. I'm gonna also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the, the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Hasselbeck. >>He's the vice president of Data quality at Collibra. He's an amazingly smart dude who founded Owl dq, a company that he sold to Col to Collibra last year. Now many companies, they didn't make it through the Hado era, you know, they missed the industry waves and they became Driftwood. Collibra, on the other hand, has evolved its business. They've leveraged the cloud, expanded its product portfolio, and leaned in heavily to some major partnerships with cloud providers, as well as receiving a strategic investment from Snowflake earlier this year. So it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. >>Last year, the Cube Covered Data Citizens Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hado movement, we had data lakes, we'd spark the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of ai, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you know, more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights for data, trust the data, and begin to use that data in new ways, fueling data, products, monetization and insights data citizens 2022 is back and we're pleased to have Felix Van Dema, who is the founder and CEO of Collibra. He's on the cube or excited to have you, Felix. Good to see you again. >>Likewise Dave. Thanks for having me again. >>You bet. All right, we're gonna get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever and get current on what Collibra has been up to over the past year and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >>Yeah, absolutely. And, and I think you said it well, Dave, and and the intro that that rising complexity and fragmentation in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use has only gotten kinda more, more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well, which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. >>So it's become much more acute. And, and to your earlier point, we do live in a different world and and the the past couple of years we could probably just kind of brute for it, right? We could focus on, on the top line. There was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are, are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, How do we truly get value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale it data, not just from a a technology and infrastructure perspective, but how do you actually scale data from an organizational perspective, right? You said at the the people and process, how do we do that at scale? And that's only, only only becoming much more important. And we do believe that the, the economic environment that we find ourselves in today is gonna be catalyst for organizations to really dig out more seriously if, if, if, if you will, than they maybe have in the have in the best. >>You know, I don't know when you guys founded Collibra, if, if you had a sense as to how complicated it was gonna get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >>Yeah, absolutely. We, we started Colli in 2008. So in some sense and the, the last kind of financial crisis, and that was really the, the start of Colli where we found product market fit, working with large finance institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis and kind of here we are again in a very different environment, of course 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it data citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we are still relatively early in that, in that journey. >>Well, that's interesting because, you know, in my observation it takes seven to 10 years to actually build a company and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your, your current momentum? >>Yeah, absolutely. Again, there's, there's a lot of tail organizations that are only maturing the data practices and we've seen it kind of transform or, or, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world where it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in, in the, in the markets with some of the cloud partners like Google, Amazon, Snowflake, data bricks and, and others, right? As those kind of new modern data infrastructures, modern data architectures that are definitely all moving to the cloud, a great opportunity for us, our partners and of course our customers to help them kind of transition to the cloud even faster. >>And so we see a lot of excitement and momentum there within an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course, data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging ai, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architecture arch architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical so that they're really excited about about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believe. Then federated focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations. And so that aligns really well with our vision and, and from a product perspective, we've seen a lot of momentum with our customers there as well. >>Yeah, you know, a couple things there. I mean, the acquisition of i l dq, you know, Kirk Hasselbeck and, and their team, it's interesting, you know, the whole data quality used to be this back office function and, and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh. You mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So let's chat a little bit about the, the products. We're gonna go deeper in into products later on at, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the, the the under the covers in security, sort of making data more accessible for people just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >>Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission, either customers are still start, are just starting on that, on that journey. We wanna make it as easy as possible for the, for our organization to actually get started because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for really to, to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving. >>A lot of kind of ease of adoption, ease of use, but also then how do we make sure that lio becomes this kind of mission critical enterprise platform from a security performance architecture scale supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme from an innovation perspective, From a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction, how to make it easy. How do we make, how do we make available to true kind of shopping experience that anybody in your organization can, in a very easy search first way, find the right data product, find the right dataset, that data can then consume usage analytics. How do you, how do we help organizations drive adoption, tell them where they're working really well and where they have opportunities homepages again to, to make things easy for, for people, for anyone in your organization to kind of get started with ppia, you mentioned workflow designer, again, we have a very powerful enterprise platform. >>One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around K Bear Protect, which in partnership with Snowflake, which has been a strategic investor in kib, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data is managed as much more effective, effective rate, really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. >>So we launch more data quality cloud product as well as making use of those, those native compute capabilities in platforms like Snowflake, Data, Bricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down. So actually pushing down the computer and data quality, the monitoring into the underlying platform, which again, from a scale performance and ease of use perspective is gonna make a massive difference. And then more broadly, we, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So there's a lot coming out. The, the team has been work at work really hard and we are really, really excited about what we are coming, what we're bringing to markets. >>Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you, you talked about, you know, the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard. So how do you see sort of the future and, you know, give us the, your closing thoughts please? >>Yeah, absolutely. And I, and I think we we're really at this pivotal moment, and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not gonna fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can. It's kind of, of our, it's our mission. And so I'm really, really excited to see what we, what we are gonna, how the marks gonna evolve over the next, next few quarters and years. I think the trend is clearly there when we talk about data mesh, this kind of federated approach folks on data products is just another signal that we believe that a lot of our organization are now at the time. >>The understanding need to go beyond just the technology. I really, really think about how do we actually scale data as a business function, just like we've done with it, with, with hr, with, with sales and marketing, with finance. That's how we need to think about data. I think now is the time given the economic environment that we are in much more focus on control, much more focused on productivity efficiency and now's the time. We need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >>Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much and good luck in, in San Diego. I know you're gonna crush it out there. >>Thank you Dave. >>Yeah, it's a great spot for an in-person event and, and of course the content post event is gonna be available@collibra.com and you can of course catch the cube coverage@thecube.net and all the news@siliconangle.com. This is Dave Valante for the cube, your leader in enterprise and emerging tech coverage. >>Hi, I'm Jay from Collibra's Data Office. Today I want to talk to you about Collibra's data intelligence cloud. We often say Collibra is a single system of engagement for all of your data. Now, when I say data, I mean data in the broadest sense of the word, including reference and metadata. Think of metrics, reports, APIs, systems, policies, and even business processes that produce or consume data. Now, the beauty of this platform is that it ensures all of your users have an easy way to find, understand, trust, and access data. But how do you get started? Well, here are seven steps to help you get going. One, start with the data. What's data intelligence? Without data leverage the Collibra data catalog to automatically profile and classify your enterprise data wherever that data lives, databases, data lakes or data warehouses, whether on the cloud or on premise. >>Two, you'll then wanna organize the data and you'll do that with data communities. This can be by department, find a business or functional team, however your organization organizes work and accountability. And for that you'll establish community owners, communities, make it easy for people to navigate through the platform, find the data and will help create a sense of belonging for users. An important and related side note here, we find it's typical in many organizations that data is thought of is just an asset and IT and data offices are viewed as the owners of it and who are really the central teams performing analytics as a service provider to the enterprise. We believe data is more than an asset, it's a true product that can be converted to value. And that also means establishing business ownership of data where that strategy and ROI come together with subject matter expertise. >>Okay, three. Next, back to those communities there, the data owners should explain and define their data, not just the tables and columns, but also the related business terms, metrics and KPIs. These objects we call these assets are typically organized into business glossaries and data dictionaries. I definitely recommend starting with the topics that are most important to the business. Four, those steps that enable you and your users to have some fun with it. Linking everything together builds your knowledge graph and also known as a metadata graph by linking or relating these assets together. For example, a data set to a KPI to a report now enables your users to see what we call the lineage diagram that visualizes where the data in your dashboards actually came from and what the data means and who's responsible for it. Speaking of which, here's five. Leverage the calibra trusted business reporting solution on the marketplace, which comes with workflows for those owners to certify their reports, KPIs, and data sets. >>This helps them force their trust in their data. Six, easy to navigate dashboards or landing pages right in your platform for your company's business processes are the most effective way for everyone to better understand and take action on data. Here's a pro tip, use the dashboard design kit on the marketplace to help you build compelling dashboards. Finally, seven, promote the value of this to your users and be sure to schedule enablement office hours and new employee onboarding sessions to get folks excited about what you've built and implemented. Better yet, invite all of those community and data owners to these sessions so that they can show off the value that they've created. Those are my seven tips to get going with Collibra. I hope these have been useful. For more information, be sure to visit collibra.com. >>Welcome to the Cube's coverage of Data Citizens 2022 Collibra's customer event. My name is Dave Valante. With us is Kirk Hasselbeck, who's the vice president of Data Quality of Collibra Kirk, good to see you. Welcome. >>Thanks for having me, Dave. Excited to be here. >>You bet. Okay, we're gonna discuss data quality observability. It's a hot trend right now. You founded a data quality company, OWL dq, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >>Yeah, absolutely. It's, it's definitely exciting times for data quality, which you're right, has been around for a long time. So why now and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And, and while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as, as to why this is becoming so important now. And, and I guess you could kind of break this down simply and think about if Dave, you and I were gonna build, you know, a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, you know, what the ramifications could be, what, what those incidents would look like, or maybe better yet, we try to build a, a new trading algorithm with a crossover strategy where the 50 day crosses the, the 10 day average. >>And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, you know, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. You know, I, I bought a car not too long ago and my dad called and said, How many cylinders does it have? And I realized in that moment, you know, I might have failed him because, cause I didn't know. And, and I used to ask those types of questions about any lock brakes and cylinders and, and you know, if it's manual or, or automatic and, and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips, I, I really don't know that much about it. >>And, and that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the, the individuals loading and consuming all of this data for the company actually may not know that much about the data itself, and that's not even their job anymore. So we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >>You know, the other thing too about data quality, and for years we did the MIT CDO IQ event, we didn't do it last year, Covid messed everything up. But the observation I would make there thoughts is, is it data quality? Used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a, a risk to data as a, as an asset. And now as we say, we're gonna talk about observability. And so it's really become front and center just the whole quality issue because data's so fundamental, hasn't it? >>Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my, my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And, and that's kind of what's going on. There's, there's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before calibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is, is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, you know, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's, it's not ever going to be based on one or two domain experts anymore. >>So, So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they, are they cousins? What's your perspective on that? >>Yeah, it's, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the, the lingo is constantly moving is, you know, as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens, it's wrong and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. You know, everybody's talking about fresh data and stale data and, and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good in the bads. That was kind of your, your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data, but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >>So what's the Collibra angle on all this stuff made the acquisition, you got data quality observability coming together, you guys have a lot of expertise in, in this area, but you hear providence of data, you just talked about, you know, stale data, you know, the, the whole trend toward real time. How is Calibra approaching the problem and what's unique about your approach? >>Well, I think where we're fortunate is with our background, myself and team, we sort of lived this problem for a long time, you know, in, in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the, the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution, it's more advanced than some of the observation techniques that that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights, and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong, just show me the big picture, help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows, you can actually achieve total data governance at this point with the acquisition of what was a Lineage company years ago, and then my company Ldq now Collibra, Data quality Collibra may be the best positioned for total data governance and intelligence in the space. >>Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, you know, they just said, Oh, it's a glitch, you know, so they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you gotta announce new products, right? You're yearly event what's, what's new. Give us a sense as to what products are coming out, but specifically around data quality and observability. >>Absolutely. There's this, you know, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks is Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook in to these databases. And while we've always worked with the the same databases in the past, they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did your, my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? >>And with these native integrations that we're building and about to unveil, here's kind of a sneak peek for, for next week at Data Citizens. We're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration, you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress, cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >>So this is interesting because what you just described, you know, you mentioned Snowflake, you mentioned Google, Oh actually you mentioned yeah, data bricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool, but then Google's got the open data cloud. If you heard, you know, Google next and now data bricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm, I'm hearing to, to really understand the relationships between all those and have confidence across, you know, it's like Jak Dani, you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And, and, and that's what you're bringing to the table, Is that right? Did I get that right? >>Yeah, that's right. And it's, for us, it's, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now, we can send them the, the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network costs, zero egress cost, zero latency of time. And so when you were to log into Big Query tomorrow using our tool or like, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there, just like all of the major brands that you mentioned, but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And, and we think that this positions us to be the leader there. >>I love this example because, you know, Barry talks about, wow, the cloud guys are gonna own the world and, and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. All right, Kirk, give us your, your final thoughts and on on the trends that we've talked about and Data Citizens 22. >>Absolutely. Well, I think, you know, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there, they wanna know where everything is, where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're gonna see more one click solutions, more SAS based solutions and solutions that hopefully prove faster time to value on, on all of these modern cloud platforms. >>Excellent. All right, Kurt Hasselbeck, thanks so much for coming on the Cube and previewing Data Citizens 22. Appreciate it. >>Thanks for having me, Dave. >>You're welcome. Right, and thank you for watching. Keep it right there for more coverage from the Cube. Welcome to the Cube's virtual Coverage of Data Citizens 2022. My name is Dave Valante and I'm here with Laura Sellers, who's the Chief Product Officer at Collibra, the host of Data Citizens. Laura, welcome. Good to see you. >>Thank you. Nice to be here. >>Yeah, your keynote at Data Citizens this year focused on, you know, your mission to drive ease of use and scale. Now when I think about historically fast access to the right data at the right time in a form that's really easily consumable, it's been kind of challenging, especially for business users. Can can you explain to our audience why this matters so much and what's actually different today in the data ecosystem to make this a reality? >>Yeah, definitely. So I think what we really need and what I hear from customers every single day is that we need a new approach to data management and our product teams. What inspired me to come to Calibra a little bit a over a year ago was really the fact that they're very focused on bringing trusted data to more users across more sources for more use cases. And so as we look at what we're announcing with these innovations of ease of use and scale, it's really about making teams more productive in getting started with and the ability to manage data across the entire organization. So we've been very focused on richer experiences, a broader ecosystem of partners, as well as a platform that delivers performance, scale and security that our users and teams need and demand. So as we look at, Oh, go ahead. >>I was gonna say, you know, when I look back at like the last 10 years, it was all about getting the technology to work and it was just so complicated. But, but please carry on. I'd love to hear more about this. >>Yeah, I, I really, you know, Collibra is a system of engagement for data and we really are working on bringing that entire system of engagement to life for everyone to leverage here and now. So what we're announcing from our ease of use side of the world is first our data marketplace. This is the ability for all users to discover and access data quickly and easily shop for it, if you will. The next thing that we're also introducing is the new homepage. It's really about the ability to drive adoption and have users find data more quickly. And then the two more areas of the ease of use side of the world is our world of usage analytics. And one of the big pushes and passions we have at Collibra is to help with this data driven culture that all companies are trying to create. And also helping with data literacy, with something like usage analytics, it's really about driving adoption of the CLE platform, understanding what's working, who's accessing it, what's not. And then finally we're also introducing what's called workflow designer. And we love our workflows at Libra, it's a big differentiator to be able to automate business processes. The designer is really about a way for more people to be able to create those workflows, collaborate on those workflow flows, as well as people to be able to easily interact with them. So a lot of exciting things when it comes to ease of use to make it easier for all users to find data. >>Y yes, there's definitely a lot to unpack there. I I, you know, you mentioned this idea of, of of, of shopping for the data. That's interesting to me. Why this analogy, metaphor or analogy, I always get those confused. I let's go with analogy. Why is it so important to data consumers? >>I think when you look at the world of data, and I talked about this system of engagement, it's really about making it more accessible to the masses. And what users are used to is a shopping experience like your Amazon, if you will. And so having a consumer grade experience where users can quickly go in and find the data, trust that data, understand where the data's coming from, and then be able to quickly access it, is the idea of being able to shop for it, just making it as simple as possible and really speeding the time to value for any of the business analysts, data analysts out there. >>Yeah, I think when you, you, you see a lot of discussion about rethinking data architectures, putting data in the hands of the users and business people, decentralized data and of course that's awesome. I love that. But of course then you have to have self-service infrastructure and you have to have governance. And those are really challenging. And I think so many organizations, they're facing adoption challenges, you know, when it comes to enabling teams generally, especially domain experts to adopt new data technologies, you know, like the, the tech comes fast and furious. You got all these open source projects and get really confusing. Of course it risks security, governance and all that good stuff. You got all this jargon. So where do you see, you know, the friction in adopting new data technologies? What's your point of view and how can organizations overcome these challenges? >>You're, you're dead on. There's so much technology and there's so much to stay on top of, which is part of the friction, right? It's just being able to stay ahead of, of and understand all the technologies that are coming. You also look at as there's so many more sources of data and people are migrating data to the cloud and they're migrating to new sources. Where the friction comes is really that ability to understand where the data came from, where it's moving to, and then also to be able to put the access controls on top of it. So people are only getting access to the data that they should be getting access to. So one of the other things we're announcing with, with all of the innovations that are coming is what we're doing around performance and scale. So with all of the data movement, with all of the data that's out there, the first thing we're launching in the world of performance and scale is our world of data quality. >>It's something that Collibra has been working on for the past year and a half, but we're launching the ability to have data quality in the cloud. So it's currently an on-premise offering, but we'll now be able to carry that over into the cloud for us to manage that way. We're also introducing the ability to push down data quality into Snowflake. So this is, again, one of those challenges is making sure that that data that you have is d is is high quality as you move forward. And so really another, we're just reducing friction. You already have Snowflake stood up. It's not another machine for you to manage, it's just push down capabilities into Snowflake to be able to track that quality. Another thing that we're launching with that is what we call Collibra Protect. And this is that ability for users to be able to ingest metadata, understand where the PII data is, and then set policies up on top of it. So very quickly be able to set policies and have them enforced at the data level. So anybody in the organization is only getting access to the data they should have access to. >>Here's Topica data quality is interesting. It's something that I've followed for a number of years. It used to be a back office function, you know, and really confined only to highly regulated industries like financial services and healthcare and government. You know, you look back over a decade ago, you didn't have this worry about personal information, g gdpr, and, you know, California Consumer Privacy Act all becomes, becomes so much important. The cloud is really changed things in terms of performance and scale and of course partnering for, for, with Snowflake it's all about sharing data and monetization, anything but a back office function. So it was kind of smart that you guys were early on and of course attracting them and as a, as an investor as well was very strong validation. What can you tell us about the nature of the relationship with Snowflake and specifically inter interested in sort of joint engineering or, and product innovation efforts, you know, beyond the standard go to market stuff? >>Definitely. So you mentioned there were a strategic investor in Calibra about a year ago. A little less than that I guess. We've been working with them though for over a year really tightly with their product and engineering teams to make sure that Collibra is adding real value. Our unified platform is touching pieces of our unified platform or touching all pieces of Snowflake. And when I say that, what I mean is we're first, you know, able to ingest data with Snowflake, which, which has always existed. We're able to profile and classify that data we're announcing with Calibra Protect this week that you're now able to create those policies on top of Snowflake and have them enforce. So again, people can get more value out of their snowflake more quickly as far as time to value with, with our policies for all business users to be able to create. >>We're also announcing Snowflake Lineage 2.0. So this is the ability to take stored procedures in Snowflake and understand the lineage of where did the data come from, how was it transformed with within Snowflake as well as the data quality. Pushdown, as I mentioned, data quality, you brought it up. It is a new, it is a, a big industry push and you know, one of the things I think Gartner mentioned is people are losing up to $15 million without having great data quality. So this push down capability for Snowflake really is again, a big ease of use push for us at Collibra of that ability to, to push it into snowflake, take advantage of the data, the data source, and the engine that already lives there and get the right and make sure you have the right quality. >>I mean, the nice thing about Snowflake, if you play in the Snowflake sandbox, you, you, you, you can get sort of a, you know, high degree of confidence that the data sharing can be done in a safe way. Bringing, you know, Collibra into the, into the story allows me to have that data quality and, and that governance that I, that I need. You know, we've said many times on the cube that one of the notable differences in cloud this decade versus last decade, I mean ob there are obvious differences just in terms of scale and scope, but it's shaping up to be about the strength of the ecosystems. That's really a hallmark of these big cloud players. I mean they're, it's a key factor for innovating, accelerating product delivery, filling gaps in, in the hyperscale offerings cuz you got more stack, you know, mature stack capabilities and you know, it creates this flywheel momentum as we often say. But, so my question is, how do you work with the hyperscalers? Like whether it's AWS or Google, whomever, and what do you see as your role and what's the Collibra sweet spot? >>Yeah, definitely. So, you know, one of the things I mentioned early on is the broader ecosystem of partners is what it's all about. And so we have that strong partnership with Snowflake. We also are doing more with Google around, you know, GCP and kbra protect there, but also tighter data plex integration. So similar to what you've seen with our strategic moves around Snowflake and, and really covering the broad ecosystem of what Collibra can do on top of that data source. We're extending that to the world of Google as well and the world of data plex. We also have great partners in SI's Infosys is somebody we spoke with at the conference who's done a lot of great work with Levi's as they're really important to help people with their whole data strategy and driving that data driven culture and, and Collibra being the core of it. >>Hi Laura, we're gonna, we're gonna end it there, but I wonder if you could kind of put a bow on, you know, this year, the event your, your perspectives. So just give us your closing thoughts. >>Yeah, definitely. So I, I wanna say this is one of the biggest releases Collibra's ever had. Definitely the biggest one since I've been with the company a little over a year. We have all these great new product innovations coming to really drive the ease of use to make data more valuable for users everywhere and, and companies everywhere. And so it's all about everybody being able to easily find, understand, and trust and get access to that data going forward. >>Well congratulations on all the pro progress. It was great to have you on the cube first time I believe, and really appreciate you, you taking the time with us. >>Yes, thank you for your time. >>You're very welcome. Okay, you're watching the coverage of Data Citizens 2022 on the cube, your leader in enterprise and emerging tech coverage. >>So data modernization oftentimes means moving some of your storage and computer to the cloud where you get the benefit of scale and security and so on. But ultimately it doesn't take away the silos that you have. We have more locations, more tools and more processes with which we try to get value from this data. To do that at scale in an organization, people involved in this process, they have to understand each other. So you need to unite those people across those tools, processes, and systems with a shared language. When I say customer, do you understand the same thing as you hearing customer? Are we counting them in the same way so that shared language unites us and that gives the opportunity for the organization as a whole to get the maximum value out of their data assets and then they can democratize data so everyone can properly use that shared language to find, understand, and trust the data asset that's available. >>And that's where Collibra comes in. We provide a centralized system of engagement that works across all of those locations and combines all of those different user types across the whole business. At Collibra, we say United by data and that also means that we're united by data with our customers. So here is some data about some of our customers. There was the case of an online do it yourself platform who grew their revenue almost three times from a marketing campaign that provided the right product in the right hands of the right people. In other case that comes to mind is from a financial services organization who saved over 800 K every year because they were able to reuse the same data in different kinds of reports and before there was spread out over different tools and processes and silos, and now the platform brought them together so they realized, oh, we're actually using the same data, let's find a way to make this more efficient. And the last example that comes to mind is that of a large home loan, home mortgage, mortgage loan provider where they have a very complex landscape, a very complex architecture legacy in the cloud, et cetera. And they're using our software, they're using our platform to unite all the people and those processes and tools to get a common view of data to manage their compliance at scale. >>Hey everyone, I'm Lisa Martin covering Data Citizens 22, brought to you by Collibra. This next conversation is gonna focus on the importance of data culture. One of our Cube alumni is back, Stan Christians is Collibra's co-founder and it's Chief Data citizens. Stan, it's great to have you back on the cube. >>Hey Lisa, nice to be. >>So we're gonna be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really re requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. >>Right. So as you know, our event is called Data Citizens because we believe that in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organizations, you have a lot of people, most of the employees in an organization are somehow gonna to be a data citizen, right? So you need to make sure that these people are aware of it. You need that. People have skills and competencies to do with data what necessary and that's on, all right? So what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss, we need to make this decision that your boss is also open to and able to interpret, you know, the data presented in dashboard to actually make that decision and take that action. Right? >>And once you have that why to the organization, that's when you have a good data culture. Now that's continuous effort for most organizations because they're always moving, somehow they're hiring new people and it has to be continuous effort because we've seen that on the hand. Organizations continue challenged their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other set hand of the equation, you have the benefit. You know, you might look at regulatory drivers like, we have to do this, right? But it's, it's much better right now to consider the competitive drivers, for example, and we did an IDC study earlier this year, quite interesting. I can recommend anyone to it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity. >>So the, the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, don't have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, ok, I'm doing this, you know, data culture for everyone, awakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this re reasons you're trying to bring both of those together and the ones that get data intelligence right, are successful and competitive. That's, and that's what we're seeing out there in the market. >>Absolutely. We know that just generally stand right, the organizations that are, are really creating a, a data culture and enabling everybody within the organization to become data citizens are, We know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Collibra advises customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. >>Of course, of course it's difficult for an organization to adapt but it's also necessary, as you just said, imagine that, you know, you're a modern day organization, laptops, what have you, you're not using those, right? Or you know, you're delivering them throughout organization, but not enabling your colleagues to actually do something with that asset. Same thing as through with data today, right? If you're not properly using the data asset and competitors are, they're gonna to get more advantage. So as to how you get this done, establish this. There's angles to look at, Lisa. So one angle is obviously the leadership whereby whoever is the boss of data in the organization, you typically have multiple bosses there, like achieve data officers. Sometimes there's, there's multiple, but they may have a different title, right? So I'm just gonna summarize it as a data leader for a second. >>So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data. And that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now that's one part because then you can leadership in the organization and also the business value. And that's important. Cause those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that right, is it's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You, I really have to win them over. And if you have those two combined and obviously a good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like s then the in place to really start upgrading that culture inch by inch if you'll, >>Yes, I like that. The recipe for success. So you are the co-founder of Collibra. You've worn many different hats along this journey. Now you're building Collibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Collibra and what maybe some of the specific projects are that Collibra's data office is working on. >>Yes, and it is indeed data citizens. There are a ton of speaks here, are very excited. You know, we have Barb from m MIT speaking about data monetization. We have Dilla at the last minute. So really exciting agen agenda. Can't wait to get back out there essentially. So over the years at, we've doing this since two and eight, so a good years and I think we have another decade of work ahead in the market, just to be very clear. Data is here to stick around as are we. And myself, you know, when you start a company, we were for people in a, if you, so everybody's wearing all sorts of hat at time. But over the years I've run, you know, presales that sales partnerships, product cetera. And as our company got a little bit biggish, we're now thousand two. Something like people in the company. >>I believe systems and processes become a lot important. So we said you CBRA isn't the size our customers we're getting there in of organization structure, process systems, et cetera. So we said it's really time for us to put our money where is and to our own data office, which is what we were seeing customers', organizations worldwide. And they organizations have HR units, they have a finance unit and over time they'll all have a department if you'll, that is responsible somehow for the data. So we said, ok, let's try to set an examples that other people can take away with it, right? Can take away from it. So we set up a data strategy, we started building data products, took care of the data infrastructure. That's sort of good stuff. And in doing all of that, ISA exactly as you said, we said, okay, we need to also use our product and our own practices and from that use, learn how we can make the product better, learn how we make, can make the practice better and share that learning with all the, and on, on the Monday mornings, we sometimes refer to eating our dog foods on Friday evenings. >>We referred to that drinking our own champagne. I like it. So we, we had a, we had the driver to do this. You know, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now how, how do we organize this? We have three pillars, and by no means is this a template that everyone should, this is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science. The data product builders, if you'll or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow and you know, the quality can be checked. >>And then we have a data intelligence or data governance builders where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the CBRA approach, which is, well, what are the challenges that our business stakeholders have in hr, finance, sales, marketing all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a map and started execution use of the use case. And a important ones are very simple. We them with our, our customers as well, people talking about the cata, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and privacy. So they have their process registry and they can see how the data flows. >>So that's a starting place and that turns into a marketplace so that if new analysts and data citizens join kbra, they immediately have a place to go to, to look at, see, ok, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access data. And another one that we is around trusted business. We're seeing that since, you know, self-service BI allowed everyone to make beautiful dashboards, you know, pie, pie charts. I always, my pet pee is the pie chart because I love buy and you shouldn't always be using pie charts. But essentially there's become proliferation of those reports. And now executives don't really know, okay, should I trust this report or that report the reporting on the same thing. But the numbers seem different, right? So that's why we have trusted this reporting. So we know if a, the dashboard, a data product essentially is built, we not that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either, Right. And that silver browser, right? Absolutely >>Decay. >>Exactly. Yes, >>Absolutely. Talk a little bit about some of the, the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? >>KPIs and measuring is a big topic in the, in the data chief data officer profession, I would say, and again, it always varies with to your organization, but there's a few that we use that might be of interest. Use those pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is gonna be more related to that uptime, right? Are the, is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption's a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data sciences and products. Are people using them? Are they getting value from it? >>Can we calculate that value in ay perspective, right? Yeah. So that we can to the rest of the business continue to say we're tracking all those numbers and those numbers indicate that value is generated and how much value estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in a data mesh. People talk about being the owner of a data domain, for example, like product or, or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open closed? How many data products are built according to process? And so and so forth. So these are an set of examples of, of KPIs. There's a, there's a lot more, but hopefully those can already inspire the audience. >>Absolutely. So we've, we've talked about the rise cheap data offices, it's only accelerating. You mentioned this is like a 10 year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? >>So we, we've seen indeed the, the role sort of grow up, I think in, in thousand 10 there may have been like 10 achieve data officers or something. Gartner has exact numbers on them, but then they grew, you know, industries and the number is estimated to be about 20,000 right now. Wow. And they evolved in a sort of stack of competencies, defensive data strategy, because the first chief data officers were more regulatory driven, offensive data strategy support for the digital program. And now all about data products, right? So as a data leader, you now need all of those competences and need to include them in, in your strategy. >>How is that going to evolve for the next couple of years? I wish I had one of those balls, right? But essentially I think for the next couple of years there's gonna be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the chief data. So you'll see over the years that's gonna evolve more digital and more data products. So for next years, my, my prediction is it's all products because it's an immediate link between data and, and the essentially, right? Right. So that's gonna be important and quite likely a new, some new things will be added on, which nobody can predict yet. But we'll see those pop up in a few years. I think there's gonna be a continued challenge for the chief officer role to become a real executive role as opposed to, you know, somebody who claims that they're executive, but then they're not, right? >>So the real reporting level into the board, into the CEO for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful and the ones who get that will the ones that do it on the basis of data monetization, right? Connecting value to the data and making that value clear to all the data citizens in the organization, right? And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned of course. And they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization and you make everyone in the organization think about data as an asset. >>Absolutely. Because there's so much value that can be extracted. Organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective. From the data citizen perspective. And as the data show that you mentioned in that IDC study, you mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the cube at Data Citizens 22. We appreciate it. >>Thanks for having me over >>From Data Citizens 22, I'm Lisa Martin, you're watching The Cube, the leader in live tech coverage. >>Okay, this concludes our coverage of Data Citizens 2022, brought to you by Collibra. Remember, all these videos are available on demand@thecube.net. And don't forget to check out silicon angle.com for all the news and wiki bod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner ETR Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to collibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on the Cube, your leader in enterprise and emerging tech coverage. We'll see you soon.

(bright upbeat music) >> Welcome to theCUBE's Coverage of Data Citizens 2022 Collibra's Customer event. My name is Dave Vellante. With us is Kirk Hasselbeck, who's the Vice President of Data Quality of Collibra. Kirk, good to see you. Welcome. >> Thanks for having me, Dave. Excited to be here. >> You bet. Okay, we're going to discuss data quality, observability. It's a hot trend right now. You founded a data quality company, OwlDQ and it was acquired by Collibra last year. Congratulations! And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >> Yeah, absolutely. It's definitely exciting times for data quality which you're right, has been around for a long time. So why now, and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as to why this is becoming so important now. And I guess you could kind of break this down simply and think about if Dave, you and I were going to build, you know a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, what the ramifications could be? What those incidents would look like? Or maybe better yet, we try to build a new trading algorithm with a crossover strategy where the 50 day crosses the 10 day average. And imagine if the data underlying the inputs to that is incorrect. We'll probably have major financial ramifications in that sense. So, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. I bought a car not too long ago and my dad called and said, "How many cylinders does it have?" And I realized in that moment, I might have failed him because 'cause I didn't know. And I used to ask those types of questions about any lock brakes and cylinders and if it's manual or automatic and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips. I really don't know that much about it. And that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the individuals loading and consuming all of this data for the company actually may not know that much about the data itself and that's not even their job anymore. So, we'll talk more about that in a minute but that's really what's setting the foreground for this observability play and why everybody's so interested, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >> You know, the other thing too about data quality and for years we did the MIT CDOIQ event we didn't do it last year at COVID, messed everything up. But the observation I would make there love thoughts is it data quality used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a a risk to data as an asset. And now, as we say, we're going to talk about observability. And so it's really become front and center, just the whole quality issue because data's fundamental, hasn't it? >> Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And that's kind of what's going on. There's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor. But with the scale that we've achieved in early days, even before Collibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is listening right intently nowadays to this topic is so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's not ever going to be based on one or two domain experts anymore. >> So, how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they cousins? What's your perspective on that? >> Yeah, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the lingo is constantly moving as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens it's wrong and when it doesn't, it's correct. Or I could look for a trend and I'll give you a good example. Everybody's talking about fresh data and stale data and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good and the bads. That was kind of your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >> So what's the Collibra angle on all this stuff made the acquisition you got data quality observability coming together, you guys have a lot of expertise in this area but you hear providence of data you just talked about stale data, the whole trend toward real time. How is Collibra approaching the problem and what's unique about your approach? >> Well, I think where we're fortunate is with our background, myself and team we sort of lived this problem for a long time in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution. It's more advanced than some of the observation techniques that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong just show me the big picture. Help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact connecting it with lineage and catalog, metadata. And as that grows, you can actually achieve total data governance. At this point, with the acquisition of what was a lineage company years ago and then my company OwlDQ, now Collibra Data Quality, Collibra may be the best positioned for total data governance and intelligence in the space. >> Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, they just said, "Oh, it's a glitch." So they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens '22 that you're announcing you got to announce new products, right? Your yearly event, what's new? Give us a sense as to what products are coming out but specifically around data quality and observability. >> Absolutely. There's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks, Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a salike model. And we've started to hook in to these databases. And while we've always worked with the same databases in the past they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did my data that I've spent all this time and money with my security team securing ever leave my hands? Did it ever leave my secure VPC as they call it? And with these native integrations that we're building and about to unveil here as kind of a sneak peek for next week at Data Citizens, we're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration you could log into the Collibra Data Quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >> So this is interesting because what you just described you mentioned Snowflake, you mentioned Google, oh actually you mentioned yeah, the Data Bricks. Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool but then Google's got the open data cloud. If you heard Google Nest and now Data Bricks doesn't call it the data cloud but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm hearing to really understand the relationships between all those and have confidence across, it's like (indistinct) you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And that's what you're bringing to the table. Is that right? Did I get that right? >> Yeah, that's right. And for us, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now we can send them the operating ability to crunch all of the calculations, the governance, the quality and get the answers. And what that's doing, it's basically zero network cost, zero egress cost, zero latency of time. And so when you were to log into Big BigQuery tomorrow using our tool or let or say Snowflake, for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls things of that nature that just become less onerous. What we're seeing is there's so much technology out there just like all of the major brands that you mentioned but how do we make it easier? The future is about less clicks, faster time to value faster scale, and eventually lower cost. And we think that this positions us to be the leader there. >> I love this example because every talks about wow the cloud guys are going to own the world and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. Alright, Kirk, give us your final thoughts and on the trends that we've talked about and Data Citizens '22. >> Absolutely. Well I think, one big trend is discovery and classification. Seeing that across the board people used to know it was a zip code and nowadays with the amount of data that's out there, they want to know where everything is where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases, how fast they can get controls and insights out of their tools. So I think we're going to see more one click solutions, more SAS-based solutions and solutions that hopefully prove faster time to value on all of these modern cloud platforms. >> Excellent, all right. Kurt Hasselbeck, thanks so much for coming on theCUBE and previewing Data Citizens '22. Appreciate it. >> Thanks for having me, Dave. >> You're welcome. All right, and thank you for watching. Keep it right there for more coverage from theCUBE.

(intro upbeat music) >> TheCUBE presents UiPath Forward5, Brought to you by UiPath. >> Welcome to day two of Forward5 UiPath Customer Conference. You're watching theCUBE. My name is Dave Vellante. My co-host is David Nicholson. Yesterday, Dave, we heard about the extension into an enterprise platform. We heard about, from the two CEOs, a new go-to-market strategy. We heard from a lot of customers how they're implementing UiPath generally and automation, specifically, scaling, hyper-automation, and all the buzzwords you hear. Todd Foley is the CDO and CSO of Lydonia Technologies and Devika Saharya is the director of ERP and RPA at MongoDB. Folks, welcome to theCUBE. Thanks for taking time out of your busy day and coming on. >> Thank you Dave. >> Thank you so much. >> So let's start with the roles. So Devika, ERP and RPA. >> Yes. >> It's like peanut butter and jelly, or how do those things relate? What's your, what's your role? >> Absolutely. So I started at Mongo as an ERP manager, and you know, as we were growing, the one thing that came out of, you know, the every year goals for the company, one big goal that came out was how we have to scale. There are so many barriers to scale. How can we become a billion dollar company? What do we need to do? And when we started drilling down into, you know, different areas, we figured it out that people do a lot of stuff manually. It's like comparing sheets, you know, copying data from one place to the other, and so on and so forth. So one thing that we realized was we definitely need some kind of automation. At that time, we didn't know about automation, but we did our own market research and here we are. >> Let's automate. Yeah, right. (Devika laughs) Sounds easy. All right, thank you. Todd, CDO, Chief Data or Chief Dig, and CSO, I'm assuming Chief Data? >> Chief Data. >> And the Chief Information Security Officer. Tell us about Lydonia and also your role. >> Sure, Lydonia, we started just over three years ago. We looked at the RPA market. We saw great opportunity, but we also saw a challenge. We saw that a lot of people had deployed RPA but weren't getting the promised, you know, immediate ROI, rapid deployment that was out there. And when we looked at it, we saw that it really wasn't a technical challenge. Sometimes it was how technology was applied, but there were a lot of things that people were doing in their process and how they were treating RPA, often as if it were traditional technology that slowed them down. So we built our practice, our company, around the idea of being able to help people scale very quickly and drive that faster. And we're finding now with the RPA being pretty ubiquitous, that it's the one thing that's in the greatest demand among our clients. >> Okay, so you're the implementation partner for Mongo, is that right? >> We are. >> Okay, so relatively new. Very new actually, but a specialist. Why'd you choose Lydonia? >> So, that's an interesting question. When we came last year to UiPath Forward, we were looking for, you know, the right kind of people who can, you know, put us on track. We had the technology, we had everything in place, we did the POC, everybody liked it, but we didn't know how to, you know, basically go in that direction. We were missing that direction. And then we, you know, we were doing our homework here, we found, we accidentally stumbled with Lydonia, and I had follow up conversations with Todd, and they were just so tapered. I knew exactly what Todd was explaining me, and we knew we are, we are in safe hands. >> So, where did you start? >> So we, the first thing that we did was a POC for the finance side of business. And right after that POC, we realized that, you know, how much time people were actually investing manually, like things that were done in three to four days was turning into a 30 minute process. And that gave us, you know, the idea that we should start drilling down into different departments and try to find where there are, you know, areas where we can improve. And we did all of that. And then we met with Todd, and Todd explained that how his Reignite process works. So we took Reignite as our first step and, you know, took it from there. We chose one department, we worked with them. We had about 10 processes highlighted, thanks to Todd, he worked with them, and he literally drilled and nailed it down that what we need to do. And as of today, all those 10 are automated. >> Wow. Okay. >> Todd, does this interaction between Lydonia and MongoDB, as a customer, apply equally in the field when you're going out and talking to clients that might be running MongoDB, they might be customers of MongoDB, they may have financial applications that are backended with MongoDB, is there a synergy there that you've been able to gain? >> I think there is. I think there's one thing that's kind of unique about RPA, and that the traditional questions around integration and applicability aren't as important when you have a platform that can work with anything that people can use. I think also, you know, when we look at what we typically do with people, some of the things we see at Mongo are very common use cases you know, across all of our clients. So I, there's definitely the ability for us to take things we've done and have clients get leverage out of them. At the same time, the platform itself is, makes it different than a traditional model where, you know if somebody has worked in a particular area or built an automation for a particular application, there's some kind of utility to do it faster for another client. What we find is that that's not really the case. And that oftentimes we'll compete with people who use different tool sets than UiPath who have that kind of value story around having done it before, we come in and we do it twice as fast as they could. >> So you've, you're a veteran of complex integrations. >> Oh yeah. (Todd laughs) >> I know that from our paths have crossed in the past. So you're saying that in this world of RPA, that this tool set like UiPath as a platform, we've been talking a lot about the difference between being a tool set and being a platform. >> Right. >> That this platform can sort of hover above things without that same layer of complexity, or level of complexity, that you've experienced in the past. Because that speaks to the idea that UiPath, as a platform, is going to work moving forward in a big way. >> Exactly, right. I think we've seen for years and years that regardless of the type of development environment you're using, a developer's value sometimes is based on what reusable libraries they've created, what they have to cut and paste from their old code to be able to do things faster. The challenge with that is it has to be maintained, when things change, they've got to update those libraries. It's a value prop that's very high touch. With UiPath, they've created the ultimate in reusability. The platform, especially since they acquired cloud elements and built all of those API integrations into their platform. The platform maintains the reusability and the libraries in such a way where they're drag and drop from a development standpoint and you don't have to maintain them. It's the ultimate expression of reusability as a platform. >> Yeah, cloud elements, API automation, obviously a key pick by UiPath. Devika, what's the scale of your operation today? Like how many bots and where do you see it going? >> Yes. So we, we started with one bot. Last year we experimented a lot that, you know, we were just trying to make our footprint in the company, trying to understand that, you know, people understand what RPA is, what UiPath is. Initially we got a lot of pushback. We got a pushback from our security team as well, because they could not understand, you know, that what UiPath is and how secure it is. And we had to explain them that how we would host it over AWS, how we will work, how we will not save passwords, et cetera. When we did all of that and they got comfort, we started picking, you know, very small processes around to show, you know, people the capability of RPA and UiPath per se. When we did that, people started just coming with bigger processes, and one specific team that I can think of came that we do, you know, fuzzy logic in Excel, and we do it twice a week, but it takes a lot of time. We automated it, they run it daily, every single day, two times now. And the exponential growth that we saw just with that one automation was mind boggling. I couldn't believe that, you know. We were tracking our insights and we were like, oh my God, what happened? It just blew out of proportion. >> Okay. So then did you need more bots? Are you still running one bot, or? >> Nope. Now at the moment we have nine. >> Okay. >> And we are still looking to grow. >> Okay. So the initial friction, you said there was some, you know, concern, it was primarily security or were there others, people afraid they're going to lose their jobs? Was there any of that? >> There was no risk of losing the job. The major, you know, pushback was, one was from security, the other one was from different system owners because a lot of people were not sure why we want UI access, or why we want API access, and why are we accessing their systems? What type of information we are trying to gather out of their systems. Are we writing into their system? Because a lot of people have issues when we start saying that we will write or override data. So most of the processes that we are working around are either writing, comparing, and reading and comparing, and if it is writing, we take special permission that this is what we are going to do. >> So what did you have to do to get through the security mottle, a AWS SOC 2 report, did you have to show them the UiPath pen test? >> Absolutely. >> Did you have to change any of your processes? What was that sort of punch list like? >> Everything. >> Yeah. >> So we had to start from pen test. We had to start, we had to explain that UiPath is in the process of, you know, acquiring SOC. We also explained that how things are hosted on AWS. We had to, you know, bring our consultants in who explained that how on, on AWS, this will be a very secured way of doing things. And when we did our first process, which was actually for the auditors, which is, you know, interesting. >> Yeah. >> What we did was we did segregation of duties, which I think is very important in every field and every sphere we work in. So for example, the the writeup that we were building for auditors, we made sure that it is approved by a physical or a human, you know, and not everything is done by the bot. The biggest piece of the puzzle was writing, you know, because it was taking a lot of time. People were going into different systems, gathering information, putting it on Excel, and then you know, comparing and submitting it to PWC. >> When you say write, you mean any update to a system of record? >> Correct. >> Required some scrutiny? >> Some scrutiny, yes, yes. >> Okay, initially by a human until there was comfort level and then it's like these bots know what they're doing. >> Correct, correct. >> Okay. And now you're a NetSuite customer, correct? >> Yes. >> That's your ERP? >> That's right. >> Now we were talking about Oracle is going to acquire OCR capabilities. Will that, and we've been talking, Dave and I, a week about, okay well ServiceNow has, you know, RPA, and Salesforce, and SAP, et cetera. How will that affect your thinking about adopting UiPath? >> I don't think it should matter because I think all these systems kind of coexist in a bigger ecosystem, you know, and I also feel that all these systems have their own plus points and minus points. Not one system in, per se, can do everything within a company. So it could be that, for example, NetSuite might be very strong for financials in the space we are in, but not extremely good around sales and marketing. So for that company chose Salesforce. So you know, you have those smaller smaller multiple systems that build into a bigger ecosystem, right. And I think the other piece of the puzzle is that UiPath helps bridge that gap between these systems. You know, it could happen that certain things can get integrated, certain things cannot because of the nature of business, the nature of work that the teams are trying to do. And I think UiPath is leveraging that gap, you know, and putting, you know, those strings together. >> As you scale - >> Mm hmm. >> How will, and Todd I presume you're going to assist in this process, but how will you decide what processes to prioritize, and is that a process driven decision? Is it data led? Both? If so, what kind of data? Can you describe how you guys are going to approach that? >> Yep. Todd, would you like to take that first before I start? >> Sure, yeah. >> Maybe some best practices and then we can maybe get specific to Mongo. >> Absolutely. Our guidance is always that it should be a business decision, right? And it should be data driven, based on a business defined metric around the business case for that particular automation. Our guidance to customers is don't automate it unless you know why you're automating it, and what the value is. We see sometimes there are challenges with people being able to articulate the business case for an automation, and it can almost always be resolved by having that business case be the first step, and qualifying and identifying an automation candidate. >> And how does that apply to Mongo? Do you, where are you thinking about scaling, in your opinion? >> It's interesting because, you know, initially we thought that we will, you know, explore one area in MongoDB. And the other thing that we did was we did road shows. So because we had to create some awareness in the company that we have UiPath there's something called bots. There's something called, you know, automation that we can do, so we created a presentation with small demos inside it and, you know, circulated it within the company. Different departments tried to explain what we can achieve. And based off of that, you know, we came up with a laundry list of all the automations that different departments needed. And out of that, you know, we started doing the business case, the value, you know, trying to come up with complexity, effort. We did a full estimation matrix and based off of that we came, okay, these are the top 20 that we should build first. And as soon as we built those top 20, we saw a skyrocket, you know, growth and - >> And you're looking for hard dollars, right? >> Yes, yes. Absolutely. >> Okay, just to be clear. >> Devika, I think Mongo also is great at taking a data driven approach to looking at their program. Do you want to share how you do that? >> Yes, absolutely. So one thing that we were very sure was we have to talk in terms of numbers because that's the only solid way to see growth. And what we did was, you know, we got insights, we started doing full metrics in terms of dollar saved, hour saved, and we are trying to track how every process is impacting, you know, in the grand scheme of things. Like say for example, for finance, are we shortening the close cycle in any shape or form by doing these two or three automations that we are doing? And I'm happy to report that we have really shortened our close cycle from where we started. >> Your quarter end or month end close. >> Correct, yes. >> Daily? You at the daily close yet, (all laugh) or the "John Chambers"? >> Drive everyone nuts. First I have to say, I could feel the audience sort of smiling as they see, as they hear from MongoDB, disruptor of legacy databases being cautious in their internal approach to change. As everyone else is. >> Exactly, yeah. >> But Todd, just sort of, double clicking on this idea of kind of stove pipes of capabilities in the RPA space. I mean OCR, being added to NetSuite, I'm not sure if that's the greatest example, but the point is Lydonia will work with all of those technologies to synthesize something. Is that correct? Or are you a UiPath only? >> Both. So we exclusively use UiPath with our customers. We don't use other RPA platforms. >> Okay. >> And we don't because, not because we can't, but because we don't believe that anything else is going to be as quick or as effective. Also, it's the only platform that is as broad and comprehensive as it needs to be to deliver outcomes to our customers. We have partnerships with other companies that have gaps where UiPath isn't currently playing, but the number of companies and the number of gaps has shrunk down to almost nothing these days. And we're well placed as UiPath continues to grow their platform to take advantage of that and leverage that to deliver outcomes to customers. >> It was a great story of starting small, being careful. >> Yes. >> And prudent, from a security standpoint, especially as a public company. And then it sounds like there's virtually unlimited opportunity. >> Yes, absolutely, absolutely. >> For you guys. Great story, thank you very much for sharing it. Appreciate it. >> Thank you. >> All right, good luck. All right, thank you for watching. Keep it right there. Dave Nicholson and Dave Vellante will be back from UiPath Forward5 from the Venetian in Las Vegas. Be right back. (upbeat music playing)

>>Good morning, everyone. Welcome back to the Cube's coverage of snowflake summit 22 live from Caesar's forum in Las Vegas. Lisa Martin, here with Dave Valante. This is day three of our coverage. We've had an amazing, amazing time. Great conversations talking with snowflake executives, partners, customers. We're gonna be digging into data mesh with data.world. Please welcome John loins, the chief product officer. Great to have you on the program, John, >>Thank you so much for, for having me here. I mean, the summit, like you said, has been incredible, so many great people, so such a good time, really, really nice to be back in person with folks. >>It is fabulous to be back in person. The fact that we're on day four for, for them. And this is the, the solution showcase is as packed as it is at 10 11 in the morning. Yeah. Is saying something >>Yeah. Usually >>Chopping at the bit to hear what they're doing and innovate. >>Absolutely. Usually those last days of conferences, everybody starts getting a little tired, but we're not seeing that at all here, especially >>In Vegas. This is impressive. Talk to the audience a little bit about data.world, what you guys do and talk about the snowflake relationship. >>Absolutely data.world is the only true cloud native enterprise data catalog. We've been an incredible snowflake partner and Snowflake's been an incredible partner to us really since 2018. When we became the first data catalog in the snowflake partner connect experience, you know, snowflake and the data cloud make it so possible. And it's changed so much in terms of being able to, you know, very easily transition data into the cloud to break down those silos and to have a platform that enables folks to be incredibly agile with data from an engineering and infrastructure standpoint, data out world is able to provide a layer of discovery and governance that matches that agility and the ability for a lot of different stakeholders to really participate in the process of data management and data governance. >>So data mesh basically Jamma, Dani lays out the first of all, the, the fault domains of existing data and big data initiatives. And she boils it down to the fact that it's just this monolithic architecture with hyper specialized teams that you have to go through and it just slows everything down and it doesn't scale. They don't have domain context. So she came up with four principles if I may, yep. Domain ownership. So push it out to the businesses. They have the context they should own the data. The second is data as product. We're certainly hearing a lot about that today this week. The third is that. So that makes it sounds good. Push out the, the data great, but it creates two problems. Self-serve infrastructure. Okay. But her premises infrastructure should be an operational detail. And then the fourth is computational governance. So you talked about data CA where do you fit in those four principles? >>You know, honestly, we are able to help teams realize the data mesh architecture. And we know that data mesh is really, it's, it's both a process in a culture change, but then when you want to enact a process in a culture change like this, you also need to select the appropriate tools to match the culture that you're trying to build the process in the architecture that you're trying to build. And the data world data catalog can really help along all four of those axes. When you start thinking first about, let's say like, let's take the first one, you know, data as a product, right? We even like very meta of us from metadata management platform at the end of the day. But very meta of us. When you talk about data as a product, we track adoption and usage of all your data assets within your organization and provide program teams and, you know, offices of the CDO with incredible evented analytics, very detailed that gives them the right audit trail that enables them to direct very scarce data engineering, data architecture resources, to make sure that their data assets are getting adopted and used properly. >>On the, on the domain driven side, we are entirely knowledge graph and open standards based enabling those different domains. We have, you know, incredible joint snowflake customers like Prologis. And we chatted a lot about this in our session here yesterday, where, because of our knowledge graph underpinnings, because of the flexibility of our metadata model, it enables those domains to actually model their assets uniquely from, from group to group, without having to, to relaunch or run different environments. Like you can do that all within one day catalog platform without having to have separate environments for each of those domains, federated governance. Again, the amount of like data exhaust that we create that really enables ambient governance and participatory governance as well. We call it agile data governance, really the adoption of agile and open principles applied to governance to make it more inclusive and transparent. And we provide that in a way that Confederate across those means and make it consistent. >>Okay. So you facilitate across that whole spectrum of, of principles. And so what in the, in the early examples of data mesh that I've studied and actually collaborated with, like with JPMC, who I don't think is who's not using your data catalog, but hello, fresh who may or may not be, but I mean, there, there are numbers and I wanna get to that. But what they've done is they've enabled the domains to spin up their own, whatever data lakes, data, warehouses, data hubs, at least in, in concept, most of 'em are data lakes on AWS, but still in concept, they wanna be inclusive and they've created a master data catalog. And then each domain has its sub catalogue, which feeds into the master and that's how they get consistency and governance and everything else is, is that the right way to think about it? And or do you have a different spin on that? >>Yeah, I, I, you know, I have a slightly different spin on it. I think organizationally it's the right way to think about it. And in absence of a catalog that can truly have multiple federated metadata models, multiple graphs in one platform, I, that is really kind of the, the, the only way to do it, right with data.world. You don't have to do that. You can have one platform, one environment, one instance of data.world that spans all of your domains, enable them to operate independently and then federate across. So >>You just answered my question as to why I should use data.world versus Amazon glue. >>Oh, absolutely. >>And that's a, that's awesome that you've done now. How have you done that? What, what's your secret >>Sauce? The, the secret sauce era is really an all credit to our CTO. One of my closest friends who was a true student of knowledge graph practices and principles, and really felt that the right way to manage metadata and knowledge about the data analytics ecosystem that companies were building was through federated linked data, right? So we use standards and we've built a, a, an open and extensible metadata model that we call costs that really takes the best parts of existing open standards in the semantics space. Things like schema.org, DCA, Dublin core brings them together and models out the most typical enterprise data assets providing you with an ontology that's ready to go. But because of the graph nature of what we do is instantly accessible without having to rebuild environments, without having to do a lot of management against it. It's, it's really quite something. And it's something all of our customers are, are very impressed with and, and, and, and, you know, are getting a lot of leverage out of, >>And, and we have a lot of time today, so we're not gonna shortchange this topic. So one last question, then I'll shut up and let you jump in. This is an open standard. It's not open source. >>No, it's an open built on open standards, built on open standards. We also fundamentally believe in extensibility and openness. We do not want to vertically like lock you into our platform. So everything that we have is API driven API available. Your metadata belongs to you. If you need to export your graph, you know, instantly available in open machine readable formats. That's really, we come from the open data community. That was a lot of the founding of data.world. We, we worked a lot in with the open data community and we, we fundamentally believe in that. And that's enabled a lot of our customers as well to truly take data.world and not have it be a data catalog application, but really an entire metadata management platform and extend it even further into their enterprise to, to really catalog all of their assets, but also to build incredible integrations to things like corporate search, you know, having data assets show up in corporate Wiki search, along with all the, the descriptive metadata that people need has been incredibly powerful and an incredible extension of our platform that I'm so happy to see our customers in. >>So leasing. So it's not exclusive to, to snowflake. It's not exclusive to AWS. You can bring it anywhere. Azure GCP, >>Anytime. Yeah. You know where we are, where we love snowflake, look, we're at the snowflake summit. And we've always had a great relationship with snowflake though, and really leaned in there because we really believe Snowflake's principles, particularly around cloud and being cloud native and the operating advantages that it affords companies that that's really aligned with what we do. And so snowflake was really the first of the cloud data catalogs that we ultimately or say the cloud data warehouses that we integrated with and to see them transition to building really out the data cloud has been awesome. >>Talk about how data world and snowflake enable companies like per lodges to be data companies. These days, every company has to be a data company, but they, they have to be able to do so quickly to be competitive and to, to really win. How do you help them if we like up level the conversation to really impacting the overall business? >>That's a great question, especially right now, everybody knows. And pro is a great example. They're a logistics and supply chain company at the end of the day. And we know how important logistics and supply chain is nowadays and for them and for a lot of our customers. I think one of the advantages of having a data catalog is the ability to build trust, transparency and inclusivity into their data analytics practice by adopting agile principles, by adopting a data mesh, you're able to extend your data analytics practice to a much broader set of stakeholders and to involve them in the process while the work is getting done. One of the greatest things about agile software development, when it became a thing in the early two thousands was how inclusive it was. And that inclusivity led to a much faster ROI on software projects. And we see the same thing happening in data analytics, people, you know, we have amazing data scientists and data analysts coming up with these insights that could be business changing that could make their company significantly more resilient, especially in the face of economic uncertainty. >>But if you have to sit there and argue with your business stakeholders about the validity of the data, about the, the techniques that were used to do the analysis, and it takes you three months to get people to trust what you've done, that opportunity's passed. So how do we shorten those cycles? How do we bring them closer? And that's, that's really a huge benefit that like Prologis has, has, has realized just tightening that cycle time, building trust, building inclusion, and making sure ultimately humans learn by doing, and if you can be inclusive, it, even, it even increases things like that. We all want to, to, to, to help cuz Lord knows the world needs it. Things like data literacy. Yeah. Right. >>So data.world can inform me as to where on the spectrum of data quality, my data set lives. So I can say, okay, this is usable, shareable, you know, exactly of gold standard versus fix this. Right. Okay. Yep. >>Yep. >>That's yeah. Okay. And you could do that with one data catalog, not a bunch of >>Yeah. And trust trust is really a multifaceted and multi multi-angle idea, right? It's not just necessarily data quality or data observability. And we have incredible partnerships in that space, like our partnership with, with Monte Carlo, where we can ingest all their like amazing observability information and display that in a really like a really consumable way in our data catalog. But it also includes things like the lineage who touch it, who is involved in the process of a, can I get a, a, a question answered quickly about this data? What's it been used for previously? And do I understand that it's so multifaceted that you have to be able to really model and present that in a way that's unique to any given organization, even unique within domains within a single organization. >>If you're not, that means to suggest you're a data quality. No, no supplier. Absolutely. But your partner with them and then that you become the, the master catalog. >>That's brilliant. I love it. Exactly. And you're >>You, you just raised your series C 15 million. >>We did. Yeah. So, you know, really lucky to have incredible investors like Goldman Sachs, who, who led our series C it really, I think, communicates the trust that they have in our vision and what we're doing and the impact that we can have on organization's ability to be agile and resilient around data analytics, >>Enabling customers to have that single source of truth is so critical. You talked about trust. That is absolutely. It's no joke. >>Absolutely. >>That is critical. And there's a tremendous amount of business impact, positive business impact that can come from that. What are some of the things that are next for data.world that we're gonna see? >>Oh, you know, I love this. We have such an incredibly innovative team. That's so dedicated to this space and the mission of what we're doing. We're out there trying to fundamentally change how people get data analytics work done together. One of the big reasons I founded the company is I, I really truly believe that data analytics needs to be a team sport. It needs to go from, you know, single player mode to team mode and everything that we've worked on in the last six years has leaned into that. Our architecture being cloud native, we do, we've done over a thousand releases a year that nobody has to manage. You don't have to worry about upgrading your environment. It's a lot of the same story that's made snowflake. So great. We are really excited to have announced in March on our own summit. And we're rolling this suite of features out over the course of the year, a new package of features that we call data.world Eureka, which is a suite of automations and, you know, knowledge driven functionality that really helps you leverage a knowledge graph to make decisions faster and to operationalize your data in, in the data ops way with significantly less effort, >>Big, big impact there. John, thank you so much for joining David, me unpacking what data world is doing. The data mesh, the opportunities that you're giving to customers and every industry. We appreciate your time and congratulations on the news and the funding. >>Ah, thank you. It's been a, a true pleasure. Thank you for having me on and, and I hope, I hope you guys enjoy the rest of, of the day and, and your other guests that you have. Thank you. >>We will. All right. For our guest and Dave ante, I'm Lisa Martin. You're watching the cubes third day of coverage of snowflake summit, 22 live from Vegas, Dave and I will be right back with our next guest. So stick around.

>>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.

>>So we're going to take a hard pivot now and go from football to Ternopil Chernobyl. What went wrong? 1986, as the reactors were melting down, they had the data to say, this is going to be catastrophic. And yet the culture said, no, we're perfect. Hide it. Don't dare tell anyone which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, the additional thousands, getting cancer and 20,000 years before the ground around there and even be inhabited again, this is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with, and this is why I want you to focus on having fostering a data driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. >>So I'll talk about culture and technology. Isn't really two sides of the same coin, real world impacts, and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, you know, Cindy, I actually think this is two sides of the same coin. One reflects the other. What do you think? Let me walk you through this. So let's take a laggard. What is the technology look like? Is it based on 1990s BI and reporting largely parameterized reports on premises, data, warehouses, or not even that operational reports at best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change complacency. >>And sometimes that complacency it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, no we're measured least cost to serve. So ticks and distrust there it's between business and it or individual stakeholders is the norm. So data is hoarded. Let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics search and AI driven insights, not on premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data Lake and in a data warehouse, a logical data warehouse, the collaboration is via newer methods, whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish. >>There is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. There's none of this. Oh, well, I didn't invent that. I'm not going to look at that. There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, to fail fast. And they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact what we like to call the new decision makers or really the frontline workers. So Harvard business review partnered with us to develop this study to say, just how important is this? >>They've been working at BI and analytics as an industry for more than 20 years. Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor, 87% said they would be more successful if frontline workers were empowered with data driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture and technology. How did we get here? It's because state of the art keeps changing. So the first generation BI and analytics platforms were deployed on premises on small datasets, really just taking data out of ERP systems that were also on premises and state of the art was maybe getting a management report, an operational report over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data sometimes coming from a data warehouse, the current state of the art though, Gartner calls it augmented analytics at ThoughtSpot, we call it search and AI driven analytics. >>And this was pioneered for large scale data sets, whether it's on premises or leveraging the cloud data warehouses. And I think this is an important point. Oftentimes you, the data and analytics leaders will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody's hard coding of report, it's typing in search keywords and very robust keywords contains rank top bottom, getting to a visual visualization that then can be pinned to an existing Pinboard that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves modernizing the data and analytics portfolio is hard because the pace of change has accelerated. >>You used to be able to create an investment place. A bet for maybe 10 years, a few years ago, that time horizon was five years now, it's maybe three years and the time to maturity has also accelerated. So you have these different, the search and AI tier the data science, tier data preparation and virtualization. But I would also say equally important is the cloud data warehouse and pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So thoughts about was the first to market with search and AI driven insights, competitors have followed suit, but be careful if you look at products like power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like snowflake, Amazon Redshift, or, or Azure synapse or Google big query, they do not. >>They re require you to move it into a smaller in memory engine. So it's important how well these new products inter operate the pace of change. It's acceleration Gartner recently predicted that by 2020 to 65% of analytical queries will be generated using search or NLP or even AI. And that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you read any of my books or used any of the maturity models out there, whether the Gardner it score that I worked on, or the data warehousing Institute also has the maturity model. We talk about these five pillars to really become data driven. As Michelle spoke about it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology, and also the processes. >>And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders, you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great, but if you don't have the right culture, there's devastating impacts. And I will say I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data, it said, Hey, we're not doing good cross selling customers do not have both a checking account and a credit card and a savings account and a mortgage. >>The opened fake accounts, basing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive effects, samples, Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker spinal implant diabetes, you know, this brand and at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture or Verizon, a major telecom organization looking at late payments of their customers. And even though the us federal government said, well, you can't turn them off. >>He said, we'll extend that even beyond the mandated guidelines and facing a slow down in the business because of the tough economy, he said, you know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent, identify the relevance, or I like to call it with them and organize for collaboration. So the CDO, whatever your title is, chief analytics, officer chief, digital officer, you are the most important change agent. And this is where you will hear that. Oftentimes a change agent has to come from outside organization. So this is where, for example, in Europe, you have the CDO of just eat a takeout food delivery organization coming from the airline industry or in Australia, national Australian bank, taking a CDO within the same sector from TD bank going to NAB. >>So these change agents come in disrupt. It's a hard job. As one of you said to me, it often feels like Sisyphus. I make one step forward and I get knocked down again. I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is with them, what is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor, okay. We could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your seventies or eighties for the teachers, teachers, you ask them about data. They'll say we don't, we don't need that. I care about the student. So if you can use data to help a student perform better, that is with them. And sometimes we spend so much time talking the technology, we forget what is the value we're trying to deliver with it? And we forget the impact on the people that it does require change. In fact, the Harvard business review study found that 44% said lack of change. Management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data driven insights. >>The third point organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then in bed, these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact the most leaders. So as we look ahead said to the months ahead to the year ahead and exciting time, because data is helping organizations better navigate a tough economy, lock in the customer loyalty. And I look forward to seeing how you foster that culture. That's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at thoughtless.

>> Narrator: From around the globe, it's "theCUBE" with digital coverage of Smart Data Marketplaces brought to you by Io-Tahoe. >> We're back. We're talking about smart data and have been for several weeks now. Really it's all about injecting intelligence and automation into the data life cycle of the data pipeline. And today we're drilling into Smart Data Marketplaces, really trying to get to that self-serve, unified, trusted, secured, and compliant data models. And this is not trivial. And with me to talk about some of the nuances involved in actually getting there with folks that have experienced doing that. They'd send a series of digital evangelist with Tata Consultancy Services, TCS. And Ajay Vohora is back, he's the CEO of Io-Tahoe. Guys, great to see you, thanks so much for coming on. >> Good to see you, Dave. >> Hey Dave. >> Ajay, let's start with you. Let's set up the sort of smart data concept. What's that all about? What's your perspective? >> Yeah, so I mean, our way of thinking about this is you you've got data, it has latent value, and it's really about discovering what the properties of that data. Does it have value? Can you put that data to work? And the way we go about that with algorithms and machine learning, to generate signals in that data identified patterns, that means we can start to discover how can we apply that data to down stream? What value can we unlock for a customer and business? >> Well, so you've been on this, I mean, really like a laser, why? I mean, why this issue? Did you see a gap in the marketplace in terms of talking to customers and maybe you can help us understand the origin? >> Yeah, I think that the gap has always been there. They've been, it's become more apparent over recent times with big data. So the ability to manually work with volumes of data in petabytes is prohibitively complex and expensive. So you need the different routes, you need different set of tools and methods to do that. Metadata are data that you can understand about data. That's what we at Io-Tahoe focus on, discovering and generating that metadata. That ready, that analogy to automate those data ops processes. So the gap David, is being felt by a business owner prizes and all sectors, healthcare, telecoms, and putting that data to work. >> So Ved, Let's talk a little bit about your role. You work with a lot of customers. I see you as an individual in a company who's really trying to transform what is a very challenging industry. That's sort of ripe for transformation, but maybe you could give us your perspective on this, what kind of signals you're looking for from the data pipeline and we'll get into how you are helping transform healthcare? >> Thanks, David. You know I think this year has been one of those years where we've all realized about this idea of unknown unknowns, where something comes around the corner that you're completely not expecting. And that's really hard to plan for obviously. And I think what we need is the ability to find early signals and be able to act on things as soon as you can. Sometimes, and you know, the COVID-19 scenario of course, is hopefully once in a generation thing, but most businesses struggle with the idea that they may have the data there in their systems, but they still don't know which bit of that is really valuable and what are the signals they should be watching for. And I think the interesting thing here is the ability for us to extract from a massive data, the most critical and important signals. And I think that's where we want to focus on. >> And so, talk a little bit about healthcare in particular and sort of your role there, and maybe at a high level. How Tata and your eco-system are helping transform healthcare? >> So if you look at healthcare, you've got the bit where people need active intervention from a medical professional. And then you've got this larger body of people, typically elderly people who aren't unwell, but they have frailties. They have underlying conditions and they're very vulnerable, especially in the world that we're in now in the post-COVID-19 scenario. And what we were trying to look at is how do we keep people who are elderly, frail and vulnerable? How can we keep them safe in their own homes rather than moving to care homes, where there has been an incredibly high level of infection for things like COVID-19. So the world works better if you can keep people safe in their own homes, if you can see the slide we've got. We're also talking about a world where care is expensive. In most Western countries, especially in Western Europe, the number of elderly people is increasing as a percentage of the population, quite significantly, and resources just are not keeping up. We don't have enough people. We don't have enough funding to look after them effectively. And the care industry that used to do that job has been struggling of late. So it's kind of a perfect storm for the need for technology intervention there. And in that space, what we're saying is the data signal that we want to receive are exactly what as a relative, or a son or daughter you might want from a parent to say, "Everything's okay. "We know that today's been just like every other day "there are no anomalies in your daily living." If you could get the signals that might tell us that something's wrong, something not quite right. We don't need very complex diagnostics. We just need to know something's not quite right, that my dad hasn't woken up as has always at seven o'clock, but till nine o'clock there's no movement. Maybe he's a bit unwell. It's that kind of signal that if we can generate, can make a dramatic difference to how we can look out for these people, whether through professional carers or through family members. So what we're looking to do is to sensor-enable homes of vulnerable people so that those data signals can come through to us in a curated manner, in a way that protects privacy and security of the individual, but gives the right people, which is carers or chosen family members the access to the signals, which is alerts that might tell you there was too much movement at night, or the front door was been left open, things like that that would give you a reason to call him and check. Everybody has spoken to in this always has an example of an uncle or a relative or parent that they've looked after. And all they're looking for is a signal. Even stories like my father's neighbor calls me when he doesn't open his curtain by 11 o'clock, that actually, if you think about it is a data signal that something might be all right. And I think what we're trying to do with technology is create those kinds of data signals because ultimately, the healthcare system works much better if you can prevent rather than cure. So every dollar that you put into prevention saves maybe $3 to $5 downstream. The economic summit also are working our favor. >> And those signals give family members the confidence to act. Ajay, it is interesting to hear what Ved was talking about in terms of the unknowns, because when you think about the early days of the computer industry, there were a lot of knowns, the processes were known. It was like the technology was the big mystery. Now, I feel like it's flipped. We've certainly seen that with COVID. The technology is actually quite well understood and quite mature and reliable. One of the examples is automated data discovery, which is something that you guys have been been focused on at Io-Tahoe. Why is automated data discovery such an important component of a smart data life cycle? >> Yeah. I mean, if we look David at the schematic and this one moves from left to right where right at the outset with that latent data, the value is late because you don't know. Does it have? Can it be applied? Can that data be put to work or not? And the objective really is about driving some form of exchange or monetization of data. If you think about it in insurance or healthcare, you've got lots of different parties, providers, payers, patients, everybody's looking to make some kind of an exchange of information. The difficulty is in all of those organizations, that data sits within its own system. So data discovery, if we drill into the focus itself that, it's about understanding which data has value, classifying that data so that it can be applied and being able to tag it so that it can then be put to use it's the real enabler for DataOps. >> So maybe talk a little bit more about this. We're trying to get to self-service. It's something that we hear a lot about. You mentioned putting data to work. It seems to me that if the business can have access to that data and serve themselves, that's the way to put data to work. Do you have thoughts on that? >> Yeah, I mean, thinking back in terms of what IT and the IT function in a business could provide, there have been limitations around infrastructure, around scaling, around compute. Now that we're in an economy that is digital driven by API's your infrastructure, your data, your business rules, your intelligence, your models, all of those on the back of an API. So the options become limitless. How you can drive value and exchange that data. What that allows us to do is to be more creative, if we can understand what data has value for what use case. >> Ved, Let's talk a little bit about the US healthcare system. It's a good use case. I was recently at a chief data officer conference and listening to the CDO of Johns Hopkins, talk about the multiple different formats that they had to ingest to create that COVID map. They even had some PDFs, they had different definitions, and that's sort of underscored to me, the state of the US healthcare industry. I'm not as familiar with the UK and Europe generally, but I am familiar with the US healthcare system and the diversity that's there, the duplication of information and the like, maybe you could sort of summarize your perspectives and give us kind of the before and your vision of the after, if you will? >> The use of course, is particularly large and complex system. We all know that. We also know, I think there is some research that suggests that in the US the per-capita spend on healthcare is among the highest in the world. I think it's like 70%, and that compares to what just under 9%, which is going to be European, typical European figure. So it's almost double of that, but the outcomes are still vastly poor. When Ajay and I were talking earlier, I think we believe that there is a concept of a data friction. When you've got multiple players in an eco-system, trying to provide a single service as a patient, you're receiving a single health care service. There are probably a dozen up to 20 different organizations that have to collaborate to make sure you get that top of the line health care service. That kind of investment deserves. And what prevents it from happening very often is what we would call data friction, which is the ability to effectively share data. Something as simple as a healthcare record, which says, "This is Dave, this is Ved, this is Ajay." And when we go to hospital for anything, whatever happens, that healthcare record can capture all the information and tie to us as an individual. And if you go to a different hospital, then that record will follow you. This is how you would expect that to be implemented, but I think we're still on that journey. There are lots and lots of challenges. I've seen anecdotal data around people who suffered because they weren't carrying a card when they went into hospital, because that card has the critical elements of data, but in today's world, should you need to carry a piece of paper or can the entire thing be a digital data flow that can easily be, can certainly navigate through lack of paper and those kinds of things. So the vision that I think we need to be looking at is an effective data exchange or marketplace back with a kind of a backbone model where people agree and sign off a data standard, where each individual's data is always tied to the individual. So if you were to move States, if you would move providers, change insurance companies, none of that would impact your medical history, your data, and the ability to have the other care and medical professionals to access the data at the point of need and at the point of healthcare delivery. So I think that's the vision we're looking at, but as you rightly you said that there are enormous number of challenges, partly because of the history, of healthcare, I think it was technology enablement of healthcare started early. So there's a lot of legacy as well. So we shouldn't trivialize the challenges that the industry faces, but that I think is the way we want to go. >> Well, privacy is obviously a huge one, and a lot of the processes are built around non-digital processes and what you're describing as a flip for digital first. I mean, as a consumer, as a patient, I want an app for that. So I can see my own data. I can see price, price transparency, give access to people that I think need it. And that is a daunting task, isn't it? >> Absolutely. And I think the implicit idea and what you just said, which is very powerful is also on the app you want to control. >> Yes. >> And sometimes you want to be able to change access on data at that point. Right now, I'm at the hospital. I would like to access my data. And when I walk away or maybe three days later, I want to revoke that access. It's that level of control. And absolutely, it is by no means a trivial problem, but I think that's where you need the data automation tools. If you try to do any of this manually, we'd be here for another decade trying to solve this, but that's where tools like Io-Tahoe come in because to do this, a lot of the heavy lifting behind the scenes has to be automated. There has to be a machine churning that and presenting the simpler options. And I know you were talking about it just a little while ago Ajay. I was reminded of the example of a McDonald's or a Coke, because the sales store idea that you can go in and you can do your own ordering off a menu, or you can go in and select five different flavors from a Coke machine and choose your own particular blend of Coke. It's a very trivial example, but I think that's the word we want to get to with access of data as well. If it was that simple for consumers, for enterprise, business people, for doctors, then that's where we ultimately want to be able to arrive. But of course, to make something very simple for the end-user, somebody has to solve for complexity behind the scenes. >> So Ajay, it seems to me Ajay there're two major outcomes here. One is of course, the most important I guess, is patient outcomes, and the other is cost. I mean, they talked about the cost issues, we all, US especially understand the concerns about rising costs of healthcare. My question is this, how does a Smart Data Marketplace fit into achieving those two very important outcomes? >> When we think about how automation is enabling that, where we've got different data formats, the manual tasks are involved, duplication of information. The administrative overhead of that alone and the work, the rework, and the cycles of work that generates. That's really what we're trying to help with data is to eliminate that wasted effort. And with that wasted effort comes time and money to employ people to work through those siloed systems. So getting to the point where there is an exchange in a marketplace just as they would be for banking or insurance is really about automating the classification of data to make it available to a system that can pick it up through an API and to run a machine learning model and to manage a workflow, a process. >> Right, so you mentioned backing insurance, you're right. I mean, we've actually come a long way and just in terms of, know the customer and applying that to know the patient would be very powerful. I'm interested in what you guys are doing together, just in terms of your vision. Are you going to market together, kind of what you're seeing in terms of promoting or enabling this self-service, self-care. Maybe you could talk a little bit about Io-Tahoe and Tata, the intersection at the customer? >> Sure. I think we've been very impressed with the TCS vision of 4.0, how the re-imagining traditional industries, whether it's insurance, banking, healthcare, and bringing together automation, agile processes, robotics, AI, and once those enablers, technology may have brought together to re-imagine how those services can be delivered digitally. All of those are dependent on data. So we see that there's a really good fit here to enable understanding the legacy, the historic situation that has built up over time in an organization, a business and to help shine a light on what's meaningful in that to migrate to the cloud or to drive a digital twin, data science project. >> Ved, anything you can add to that? >> Sure. I mean, we do take the business 4.0 model quite seriously in terms of a lens with which you look at any industry, and what I talked about in healthcare was an example of that. And for us business 4.0, means a few very specific things. The technology that we use in today's verse should be agile, automated, intelligent, and cloud-based. These have become kind of hygiene factors now. On top of that, the businesses we build should be mass customized. They should be risk embracing. They should engage ecosystems, and they should strive for exponential value, not 10% growth year on year, but doubling, tripling every three, four years, because that's the competition that most businesses are facing today. And within that, the Tata group itself, is an extremely purpose-driven business. We really believe that we exist to serve communities, not just one specific set, i.e. shareholders, but the broader community in which we live and work. And I think this framework also allows us to apply that to things like healthcare, to education and to a whole vast range of areas where, everybody has a vision of using data science or doing really clever stuff at the gradients. But what becomes clear is, to do any of that, the first thing you need is a foundational piece. And as a foundation isn't right, then no matter how much you invest in the data science tools you won't get the answers you want. And the work we're doing with the Io-Tahoe really, for me, is particularly exciting because it sorts out that foundational piece. And at the end of it, to make all of this, again, I will repeat that, to make it simple and easy to use for the end user, whoever that is. And I realized that I'm probably the first person who's used fast food as a shining example for healthcare in this discussion, but you can make a lot of different examples. And today, if you press a button and start a car, that's simplicity, but someone has solved for that. And that's what we want to do with data as well. >> Yeah, that makes a lot of sense to me. We talk a lot about digital transformation and a digital business, and I would observe that a digital business puts data at the core. And you can certainly be the best example. There is, of course, Google is an all digital business, but take a company like Amazon, Who's got obviously a massive physical component to its business. Data is at the core. And that's exactly my takeaway from this discussion. Both of you are talking about putting data at the core, simplifying it, making sure that it's compliant, and healthcare it's taking longer, 'cause it's such a high risk industry, but it's clearly happening, COVID I guess, was an accelerant. Guys, Ajay, I'll start with you. Any final thoughts that you want to leave the audience with? _ Yeah, we're really pleased to be working with TCS. We've been able to explore how we're able to put dates to work in a range of different industries. Ved has mentioned healthcare, telecoms, banking and insurance are others. And the same impact they speak to whenever we see the exciting digital transformations that are being planned, being able to accelerate those, unlock the value from data is where we're having a purpose. And it's good that we can help patients in the healthcare sector, consumers in banking realize a better experience through having a more joined up marketplace with their data. >> Ved, you know what excites me about this conversation is that, as a patient or as a consumer, if I'm helping loved ones, I can go to the web and I can search, and I can find a myriad of possibilities. What you're envisioning here is really personalizing that with real time data. And that to me is a game changer. Your final thoughts? >> Thanks, David. I absolutely agree with you that the idea of data centricity and simplicity are absolutely forefront, but I think if we were to design an organization today, you might design it very differently to how most companies today are structured. And maybe Google and Amazon are probably better examples of that because you almost have to think of a business as having a data engine room at its core. A lot of businesses are trying to get to that stage, whereas what we call digital natives, are people who have started life with that premise. So I absolutely agree with you on that, but extending that a little bit. If you think of most industries as eco-systems that have to collaborate, then you've got multiple organizations who will also have to exchange data to achieve some shared outcomes. Whether you look at supply chains of automobile manufacturers or insurance companies or healthcares we've been talking about. So I think that's the next level of change we want to be able to make, which is to be able to do this at scale across organizations at industry level or in population scheme for healthcare. >> Yeah, Thank you for that. Go ahead Ajay. >> David that's where it comes back to again, the origination where we've come from in big data. The volume of data combined with the specificity of individualizing, personalizing a service around an individual amongst that massive data from different providers is where is exciting, that we're able to have an impact. >> Well, and you know Ajay, I'm glad you brought that up because in the early days of big data, there were only a handful of companies, the biggest financial institutions. Obviously, the internet giants who had all these engineers that were able to take advantage of it. But with companies like Io-Tahoe and others, and the investments that the industry has made in terms of providing the tools and simplifying that, especially with machine intelligence and AI and machine learning, these are becoming embedded into the tooling so that everybody can have access to them, small, medium, and large companies. That's really, to me, the exciting part of this new era that we're entering. >> Yeah, and we have placed those, take it down to the level of not-for-profits and smaller businesses that want to innovate and leapfrog into, to growing their digital delivery of their service. >> And I know a lot of time, but Ved, what you were saying about TCS's responsibility to society, I think is really, really important. Large companies like yours, I believe, and you clearly do as well, have a responsibility to society more than just a profit. And I think, Big Tech it's a better app in a lot of cases, but so thank you for that and thank you gentlemen for this great discussion. I really appreciate it. >> Thanks David. >> Thank you. >> All right, keep it right there. I'll be right back right after this short break. This is Dave Vellante for theCUBE. (calm music)

>>from around the globe. It's the Cube with digital coverage of smart data. Marketplace is brought to You by Io Tahoe Digital transformation is really gone from buzzword to a mandate. Additional businesses, a data business. And for the last several months, we've been working with Iot Tahoe on an ongoing content. Serious, serious, focused on smart data and automation to drive better insights and outcomes, essentially putting data to work. And today we're gonna do a deeper dive on automating data Discovery. And one of the thought leaders in this space is a J ahora who is the CEO of Iot. Tahoe's once again joining Me A J Good to see you. Thanks for coming on. >>A great to be here, David. Thank you. >>So let's start by talking about some of the business realities. And what are the economics that air? That air driving, automated data Discovery? Why is that so important? >>Yeah, and on this one, David, it's It's a number of competing factors we've got. The reality is data which may be sensitive, so this control on three other elements are wanting to drive value from that data. So innovation, you can't really drive a lot of value without exchanging data. So the ability to exchange data and to manage those costs, overheads and data discovery is at the roots of managing that in an automated way to classify that data in sets and policies to put that automation in place. >>Yeah. Okay, look, we have a picture of this. We could bring it up, guys, because I want oh, A j help the audience. Understand? Unaware data Discovery fits in here. This is as we talked about this, a complicated situation for a lot of customers. They got a variety of different tools, and you really laid it out nicely here in this diagram. So take us through. Sort of where that he spits. >>Yeah. I mean, where at the right hand side, This exchange. You know, we're really now in a data driven economy that is, everything's connected through AP, eyes that we consume on mine free mobile relapse. And what's not a parent is the chain of activities and tasks that have to go into serving that data two and eight p. I. At the outset, there may be many legacy systems, technologies, platforms on premise and cloud hybrids. You name it. Andi across those silos. Getting to a unified view is the heavy lifting. I think we've seen Cem some great impacts that be I titles such as Power Bi I tableau looker on DSO on in Clear. Who had Andi there in our ecosystem on visualising Data and CEO's managers, people that are working in companies day to day get a lot of value from saying What's the was the real time activity? What was the trend over this month? First his last month. The tools to enable that you know, we here, Um, a lot of good things are work that we're doing with snowflake mongo db on the public cloud platforms gcpd as your, um, about enabling building those pay planes to feed into those analytics. But what often gets hidden is have you sauce that data that could be locked into a mainframe, a data warehouse? I ot data on DPA, though, that all of that together that is the reality of that is it's it's, um, it's a lot of heavy lifting It z hands on what that, um, can be time consuming on the issue There is that data may have value. It might have potential to have an impact on the on the top line for a business on outcomes for consumers. But you never any sure unless you you've done the investigation discovered it unified that Onda and be able to serve that through to other technologies. >>Guys have. You would bring that picture back up again because A. J, you made a point, and I wanna land on that for a second. There's a lot of manual curating. Ah, an example would be the data catalogue if they decide to complain all the time that they're manually wrangling data. So you're trying to inject automation in the cycle, and then the other piece that I want you to addresses the importance of AP eyes. You really can't do this without an architecture that allows you to connect things together. That sort of enables some of the automation. >>Yeah, I mean, I don't take that in two parts. They would be the AP eyes so virtual machines connected by AP eyes, um, business rules and business logic driven by AP eyes applications. So everything across the stack from infrastructure down to the network um, hardware is all connected through AP eyes and the work of serving data three to an MP I Building these pipelines is is often, um, miscalculated. Just how much manual effort that takes and that manual ever. We've got a nice list here of what we automate down at the bottom. Those tasks of indexing, labeling, mapping across different legacy systems. Um, all of that takes away from the job of a data scientist today to engineer it, looking to produce value monetize data on day two to help their business day to conceive us. >>Yes. So it's that top layer that the business sees, of course, is a lot of work that has to go went into achieving that. I want to talk about some of the key tech trends that you're seeing and one of the things that we talked about a lot of metadata at the importance of metadata. It can't be understated. What are some of the big trends that you're seeing metadata and others? >>Yeah, I'll summarize. It is five. There's trains now, look, a metadata more holistically across the enterprise, and that really makes sense from trying. Teoh look across different data silos on apply, um, a policy to manage that data. So that's the control piece. That's that lever the other side's on. Sometimes competing with that control around sense of data around managing the costs of data is innovation innovation, being able to speculate on experiment and trying things out where you don't really know what the outcome is. If you're a data scientist and engineer, you've got a hypothesis. And now, before you got that tension between control over data on innovation and driving value from it. So enterprise wide manage data management is really helping to enough. Where might that latent value be across that sets of data? The other piece is adaptive data governance. Those controls that that that stick from the data policemen on day to steer its where they're trying to protect the organization, protect the brand, protect consumers data is necessary. But in different use cases, you might want to nuance and apply a different policy to govern that data run of into the context where you may have data that is less sensitive. Um, that can me used for innovation. Andi. Adapting the style of governance to fit the context is another trend that we're seeing coming up here. A few others is where we're sitting quite extensively and working with automating data discovery. We're now breaking that down into what can we direct? What do we know is a business outcome is a known up front objective on direct that data discovery to towards that. And that means applying around with Dems run technology and our tools towards solving a known problem. The other one is autonomous data discovery. And that means, you know, trying to allow background processes do winds down what changes are happening with data over time flagging those anomalies. And the reason that's important is when you look over a length of time to see different spikes, different trends and activity that's really giving a day drops team the ability to to manage and calibrate how they're applying policies and controls today. There, in the last two David that we're seeing is this huge drive towards self service so reimagining how to play policy data governance into the hands off, um, a day to consumer inside a business or indeed, the consumer themselves. The South service, um, if their banking customer or healthcare customer and the policies and the controls and rules, making sure that those are all in place to adaptive Lee, um, serve those data marketplaces that, um when they're involved in creating, >>I want to ask you about the autonomous data discovering the adaptive data. Governance is the is the problem where addressing their one of quality. In other words, machines air better than humans are doing this. Is that one of scale that humans just don't don't scale that well, is it? Is it both? Can you add some color to that >>yet? Honestly, it's the same equation that existed 10 years ago, 20 years ago. It's It's being exacerbated, but it's that equation is how do I control both things that I need to protect? How do we enable innovation where it is going to deliver business value? Had to exchange data between a customer, somebody in my supply chains safely. And all of that was managing the fourth that leg, which is cost overheads. You know, there's no no can checkbook here. I've got a figure out. If only see io and CDO how I do all of this within a fixed budget so that those aspects have always been there. Now, with more choices. Infrastructure in the cloud, um, NPR driven applications own promise. And that is expanding the choices that a a business has and how they put mandated what it's also then creating a layer off management and data governance that really has to now, uh, manage those full wrath space control, innovation, exchange of data on the cost overhead. >>That that top layer of the first slide that we showed was all about business value. So I wonder if we could drill into the business impact a little bit. What do your customers seeing you know, specifically in terms of the impact of all this automation on their business? >>Yeah, so we've had some great results. I think view the biggest Have Bean helping customers move away from manually curating their data in their metadata. It used to be a time where for data quality initiatives or data governance initiative that be teams of people manually feeding a data Cavallo. And it's great to have the inventory of classified data to be out to understand single version of the trees. But in a having 10 15 people manually process that keep it up to date when it's moving feet. The reality of it is what's what's true about data today? and another few sources in a few months. Time to your business on start collaborating with new partners. Suddenly the landscape has changed. The amount of work is gonna But the, um, what we're finding is through automating creating that data discovery feeding a dent convoke that's releasing a lot more time for our CAS. Mr Spend on innovating and managing their data. A couple of others is around cell service data and medics moving the the choices of what data might have business value into the hands of business users and and data consumers to They're faster cycle times around generating insights. Um, we really helping that by automating the creation of those those data sets that are needed for that. And in the last piece, I'd have to say where we're seeing impacts. A more recently is in the exchange of data. There are a number of marketplaces out there who are now being compelled to become more digital to rewire their business processes. Andi. Everything from an r p a initiative. Teoh automation involving digital transformation is having, um, see iose Chief data officers Andi Enterprise architects rethink how do they how they re worthy pipelines? But they dated to feed that additional transformation. >>Yeah, to me, it comes down to monetization. Of course, that's for for profit in industry, from if nonprofits, for sure, the cost cutting or, in the case of healthcare, which we'll talk about in a moment. I mean, it's patient outcomes. But you know, the the job of ah, chief data officer has gone from your data quality and governance and compliance to really figuring out how data and be monetized, not necessarily selling the data, but how it contributes for the monetization of the company and then really understanding specifically for that organization how to apply that. And that is a big challenge. We chatted about it 10 years ago in the early days of a Duke. And then, you know, 1% of the companies had enough engineers to figure it out. But now the tooling is available, the technology is there and the the practices air there, and that really to me, is the bottom line. A. J is it says to show me the money. >>Absolutely. It's is definitely then six sing links is focusing in on the saying over here, that customer Onda, where we're helping there is dio go together. Those disparities siloed source of data to understand what are the needs of the patient of the broker of the if it's insurance? Ah, one of the needs of the supply chain manager If its manufacturing onda providing that 3 60 view of data, um is helping to see helping that individual unlock the value for the business. Eso data is providing the lens, provided you know which data it is that can God assist in doing that? >>And you know, you mentioned r p A. Before an r p A customer tell me she was a six Sigma expert and she told me we would never try to apply six segment to a business process. But with our P A. We can do so very cheaply. Well, what that means is lower costs means better employee satisfaction and, really importantly, better customer satisfaction and better customer outcomes. Let's talk about health care for a minute because it's a really important industry. It's one that is ripe for disruption on has really been up until recently, pretty slow. Teoh adopt ah, lot of the major technologies that have been made available, but come, what are you seeing in terms of this theme, we're using a putting data to work in health care. Specific. >>Yeah, I mean, healthcare's Havlat thrown at it. There's been a lot of change in terms of legislation recently. Um, particularly in the U. S. Market on in other economies, um, healthcare ease on a path to becoming more digital on. Part of that is around transparency of price, saying to be operating effectively as a health care marketplace, being out to have that price transparency, um, around what an elective procedure is going to cost before taking that that's that forward. It's super important to have an informed decision around there. So we look at the US, for example. We've seen that health care costs annually have risen to $4 trillion. But even with all of that on cost, we have health care consumers who are reluctant sometimes to take up health care if they even if they have symptoms on a lot of that is driven through, not knowing what they're opening themselves up to. Andi and I think David, if you are, I want to book, travel, holiday, maybe, or trip. We want to know what what we're in for what we're paying for outfront, but sometimes in how okay, that choice, the option might be their plan, but the cost that comes with it isn't so recent legislation in the US Is it certainly helpful to bring for that tryst price, transparency, the underlying issue there? There is the disparity. Different formats, types of data that being used from payers, patients, employers, different healthcare departments try and make that make that work. And when we're helping on that aspect in particular related to track price transparency is to help make that date of machine readable. So sometimes with with data, the beneficiary might be on a person. I've been a lot of cases now we're seeing the ability to have different systems, interact and exchange data in order to process the workflow. To generate online at lists of pricing from a provider that's been negotiated with a payer is, um, is really a neighboring factor. >>So, guys, I wonder if you bring up the next slide, which is kind of the Nirvana. So if you if you saw the previous slide that the middle there was all different shapes and presumably to disparage data, this is that this is the outcome that you want to get. Everything fits together nicely and you've got this open exchange. It's not opaque as it is today. It's not bubble gum band aids and duct tape, but but but described this sort of outcome the trying to achieve and maybe a little bit about what gonna take to get there. >>Yeah, that's a combination of a number of things. It's making sure that the data is machine readable. Um, making it available to AP eyes that could be our ph toes. We're working with technology companies that employ R P. A full health care. I'm specifically to manage that patient and pay a data. Teoh, bring that together in our data Discovery. What we're able to do is to classify that data on having made available to eight downstream tour technology or person to imply that that workflow to to the data. So this looks like nirvana. It looks like utopia. But it's, you know, the end objective of a journey that we can see in different economies there at different stages of maturity, in turning healthcare into a digital service, even so that you could consume it from when you live from home when telling medicine. Intellicast >>Yes, so And this is not just health care but you wanna achieve that self service doing data marketplace in virtually any industry you working with TCS, Tata Consultancy Services Toe Achieve this You know, if you are a company like Iota has toe have partnerships with organizations that have deep industry expertise Talk about your relationship with TCS and what you guys are doing specifically in this regard. >>Yeah, we've been working with TCS now for room for a long while. Andi will be announcing some of those initiatives here where we're now working together to reach their customers where they've got a a brilliant framework of business for that zero when there re imagining with their clients. Um, how their business cause can operate with ai with automation on, become more agile in digital. Um, our technology, the dreams of patients that we have in our portfolio being out to apply that at scale on the global scale across industries such as banking, insurance and health care is is really allowing us to see a bigger impact on consumer outcomes. Patient outcomes And the feedback from TCS is that we're really helping in those initiatives remove that friction. They talk a lot about data. Friction. Um, I think that's a polite term for the the image that we just saw with the disparity technologies that the legacy that has built up. So if we want to create a transformation, Um, having a partnership with TCS across Industries is giving us that that reach and that impacts on many different people's day to day jobs and knives. >>Let's talk a little bit about the cloud. It's It's a topic that we've hit on quite a bit here in this in this content Siri's. But But you know, the cloud companies, the big hyper scale should put everything into the cloud, right? But but customers are more circumspect than that. But at the same time, machine intelligence M. L. A. The cloud is a place to do a lot of that. That's where a lot of the innovation occurs. And so what are your thoughts on getting to the cloud? Ah, putting dated to work, if you will, with machine learning stuff you're doing with aws. What? You're fit there? >>Yeah, we we and David. We work with all of the cloud platforms. Mike stuffed as your G, c p IBM. Um, but we're expanding our partnership now with AWS Onda we really opening up the ability to work with their Greenfield accounts, where a lot of that data that technology is in their own data centers at the customer, and that's across banking, health care, manufacturing and insurance. And for good reason. A lot of companies have taken the time to see what works well for them, with the technologies that the cloud providers ah, are offered a offering in a lot of cases testing services or analytics using the cloud to move workloads to the cloud to drive Data Analytics is is a real game changer. So there's good reason to maintain a lot of systems on premise. If that makes sense from a cost from a liability point of view on the number of clients that we work with, that do have and we will keep their mainframe systems within kobo is is no surprise to us, but equally they want to tap into technologies that AWS have such a sage maker. The issue is as a chief data officer, I don't have the budget to me, everything to the cloud day one, I might want to show some results. First upfront to my business users Um, Onda worked closely with my chief marketing officer to look at what's happening in terms of customer trains and customer behavior. What are the customer outcomes? Patient outcomes and partner at comes I can achieve through analytics data signs. So I, working with AWS and with clients to manage that hybrid topology of some of that data being, uh, in the cloud being put to work with AWS age maker on night, I hope being used to identify where is the data that needs to bay amalgamated and curated to provide the data set for machine learning advanced and medics to have an impact for the business. >>So what are the critical attributes of what you're looking at to help customers decide what what to move and what to keep, if you will. >>Well, what one of the quickest outcomes that we help custom achieve is to buy that business blustery. You know that the items of data that means something to them across those different silos and pour all of that together into a unified view once they've got that for a data engineer working with a a business manager to think through how we want to create this application. There was the turn model, the loyalty or the propensity model that we want to put in place here. Um, how do we use predictive and medics to understand what needs are for a patient, that sort of innovation is what we're looking applying the tools such a sagemaker, uh, night to be west. So they do the the computation and to build those models to deliver the outcome is is across that value chain, and it goes back to the first picture that we put up. David, you know the outcome Is that a P I On the back of it, you've got the machine learning model that's been developed in That's always such as data breaks. But with Jupiter notebook, that data has to be sourced from somewhere. Somebody has to say that yet you've got permission to do what you're trying to do without falling foul of any compliance around data. Um, it'll goes back to discovering that data, classifying it, indexing it in an automated way to cut those timelines down two hours and days. >>Yeah, it's the it's the innovation part of your data portfolio, if you will, that you're gonna put into the cloud. Apply tools like sage maker and others. You told the jury. Whatever your favorite tool is, you don't care. The customer's gonna choose that and hear the cloud vendors. Maybe they want you to use their tool, but they're making their marketplaces available to everybody. But it's it's that innovation piece, the ones that you where you want to apply that self service data marketplace to and really drive. As I said before monetization. All right, give us your final thoughts. A. J bring us home. >>So final thoughts on this David is that at the moment we're seeing, um, a lot of value in helping customers discover that day the using automation automatically curating a data catalogue, and that unified view is then being put to work through our A B. I's having an open architecture to plug in whatever tool technology our clients have decided to use, and that open architecture is really feeding into the reality of what see Iose in Chief Data Officers of Managing, which is a hybrid on premise cloud approach. Do you suppose to breed Andi but business users wanting to use a particular technology to get their business outcome having the flexibility to do that no matter where you're dating. Sitting on Premise on Cloud is where self service comes in that self service. You of what data I can plug together, Dr Exchange. Monetizing that data is where we're starting to see some real traction. Um, with customers now accelerating becoming more digital, uh, to serve their own customers, >>we really have seen a cultural mind shift going from sort of complacency. And obviously, cove, it has accelerated this. But the combination of that cultural shift the cloud machine intelligence tools give give me a lot of hope that the promises of big data will ultimately be lived up to ah, in this next next 10 years. So a J ahora thanks so much for coming back on the Cube. You're you're a great guest. And ah, appreciate your insights. >>Appreciate, David. See you next time. >>All right? And keep it right there. Very right back. Right after this short break

>> Narrator: From around the globe, it's "theCUBE" with digital coverage of Smart Data Marketplaces brought to you by Io-Tahoe. >> We're back. We're talking about smart data and have been for several weeks now. Really it's all about injecting intelligence and automation into the data life cycle of the data pipeline. And today we're drilling into Smart Data Marketplaces, really trying to get to that self-serve, unified, trusted, secured, and compliant data models. And this is not trivial. And with me to talk about some of the nuances involved in actually getting there with folks that have experienced doing that. They'd send a series of digital evangelist with Tata Consultancy Services, TCS. And Ajay Vohora is back, he's the CEO of Io-Tahoe. Guys, great to see you, thanks so much for coming on. >> Good to see you, Dave. >> Hey Dave. >> Ajay, let's start with you. Let's set up the sort of smart data concept. What's that all about? What's your perspective? >> Yeah, so I mean, our way of thinking about this is you you've got data, it has latent value, and it's really about discovering what the properties of that data. Does it have value? Can you put that data to work? And the way we go about that with algorithms and machine learning, to generate signals in that data identified patterns, that means we can start to discover how can we apply that data to down stream? What value can we unlock for a customer and business? >> Well, so you've been on this, I mean, really like a laser, why? I mean, why this issue? Did you see a gap in the marketplace in terms of talking to customers and maybe you can help us understand the origin? >> Yeah, I think that the gap has always been there. They've been, it's become more apparent over recent times with big data. So the ability to manually work with volumes of data in petabytes is prohibitively complex and expensive. So you need the different routes, you need different set of tools and methods to do that. Metadata are data that you can understand about data. That's what we at Io-Tahoe focus on, discovering and generating that metadata. That ready, that analogy to automate those data ops processes. So the gap David, is being felt by a business owner prizes and all sectors, healthcare, telecoms, and putting that data to work. >> So Ved, Let's talk a little bit about your role. You work with a lot of customers. I see you as an individual in a company who's really trying to transform what is a very challenging industry. That's sort of ripe for transformation, but maybe you could give us your perspective on this, what kind of signals you're looking for from the data pipeline and we'll get into how you are helping transform healthcare? >> Thanks, David. You know I think this year has been one of those years where we've all realized about this idea of unknown unknowns, where something comes around the corner that you're completely not expecting. And that's really hard to plan for obviously. And I think what we need is the ability to find early signals and be able to act on things as soon as you can. Sometimes, and you know, the COVID-19 scenario of course, is hopefully once in a generation thing, but most businesses struggle with the idea that they may have the data there in their systems, but they still don't know which bit of that is really valuable and what are the signals they should be watching for. And I think the interesting thing here is the ability for us to extract from a massive data, the most critical and important signals. And I think that's where we want to focus on. >> And so, talk a little bit about healthcare in particular and sort of your role there, and maybe at a high level. How Tata and your eco-system are helping transform healthcare? >> So if you look at healthcare, you've got the bit where people need active intervention from a medical professional. And then you've got this larger body of people, typically elderly people who aren't unwell, but they have frailties. They have underlying conditions and they're very vulnerable, especially in the world that we're in now in the post-COVID-19 scenario. And what we were trying to look at is how do we keep people who are elderly, frail and vulnerable? How can we keep them safe in their own homes rather than moving to care homes, where there has been an incredibly high level of infection for things like COVID-19. So the world works better if you can keep people safe in their own homes, if you can see the slide we've got. We're also talking about a world where care is expensive. In most Western countries, especially in Western Europe, the number of elderly people is increasing as a percentage of the population, quite significantly, and resources just are not keeping up. We don't have enough people. We don't have enough funding to look after them effectively. And the care industry that used to do that job has been struggling of late. So it's kind of a perfect storm for the need for technology intervention there. And in that space, what we're saying is the data signal that we want to receive are exactly what as a relative, or a son or daughter you might want from a parent to say, "Everything's okay. "We know that today's been just like every other day "there are no anomalies in your daily living." If you could get the signals that might tell us that something's wrong, something not quite right. We don't need very complex diagnostics. We just need to know something's not quite right, that my dad hasn't woken up as has always at seven o'clock, but till nine o'clock there's no movement. Maybe he's a bit unwell. It's that kind of signal that if we can generate, can make a dramatic difference to how we can look out for these people, whether through professional carers or through family members. So what we're looking to do is to sensor-enable homes of vulnerable people so that those data signals can come through to us in a curated manner, in a way that protects privacy and security of the individual, but gives the right people, which is carers or chosen family members the access to the signals, which is alerts that might tell you there was too much movement at night, or the front door was been left open, things like that that would give you a reason to call him and check. Everybody has spoken to in this always has an example of an uncle or a relative or parent that they've looked after. And all they're looking for is a signal. Even stories like my father's neighbor calls me when he doesn't open his curtain by 11 o'clock, that actually, if you think about it is a data signal that something might be all right. And I think what we're trying to do with technology is create those kinds of data signals because ultimately, the healthcare system works much better if you can prevent rather than cure. So every dollar that you put into prevention saves maybe $3 to $5 downstream. The economic summit also are working our favor. >> And those signals give family members the confidence to act. Ajay, it is interesting to hear what Ved was talking about in terms of the unknowns, because when you think about the early days of the computer industry, there were a lot of knowns, the processes were known. It was like the technology was the big mystery. Now, I feel like it's flipped. We've certainly seen that with COVID. The technology is actually quite well understood and quite mature and reliable. One of the examples is automated data discovery, which is something that you guys have been been focused on at Io-Tahoe. Why is automated data discovery such an important component of a smart data life cycle? >> Yeah. I mean, if we look David at the schematic and this one moves from left to right where right at the outset with that latent data, the value is late because you don't know. Does it have? Can it be applied? Can that data be put to work or not? And the objective really is about driving some form of exchange or monetization of data. If you think about it in insurance or healthcare, you've got lots of different parties, providers, payers, patients, everybody's looking to make some kind of an exchange of information. The difficulty is in all of those organizations, that data sits within its own system. So data discovery, if we drill into the focus itself that, it's about understanding which data has value, classifying that data so that it can be applied and being able to tag it so that it can then be put to use it's the real enabler for that per day drops. >> So maybe talk a little bit more about this. We're trying to get to self-service. It's something that we hear a lot about. You mentioned putting data to work. It seems to me that if the business can have access to that data and serve themselves, that's the way to put data to work. Do you have thoughts on that? >> Yeah, I mean, thinking back in terms of what IT and the IT function in a business could provide, there have been limitations around infrastructure, around scaling, around compute. Now that we're in an economy that is digital driven by API's your infrastructure, your data, your business rules, your intelligence, your models, all of those on the back of an API. So the options become limitless. How you can drive value and exchange that data. What that allows us to do is to be more creative, if we can understand what data has value for what use case. >> Ved, Let's talk a little bit about the US healthcare system. It's a good use case. I was recently at a chief data officer conference and listening to the CDO of Johns Hopkins, talk about the multiple different formats that they had to ingest to create that COVID map. They even had some PDFs, they had different definitions, and that's sort of underscored to me, the state of the US healthcare industry. I'm not as familiar with the UK and Europe generally, but I am familiar with the US healthcare system and the diversity that's there, the duplication of information and the like, maybe you could sort of summarize your perspectives and give us kind of the before and your vision of the after, if you will? >> The use of course, is particularly large and complex system. We all know that. We also know, I think there is some research that suggests that in the US the per-capita spend on healthcare is among the highest in the world. I think it's like 70%, and that compares to what just under 9%, which is going to be European, typical European figure. So it's almost double of that, but the outcomes are still vastly poor. When Ajay and I were talking earlier, I think we believe that there is a concept of a data friction. When you've got multiple players in an eco-system, trying to provide a single service as a patient, you're receiving a single health care service. There are probably a dozen up to 20 different organizations that have to collaborate to make sure you get that top of the line health care service. That kind of investment deserves. And what prevents it from happening very often is what we would call data friction, which is the ability to effectively share data. Something as simple as a healthcare record, which says, "This is Dave, this is Ved, this is Ajay." And when we go to hospital for anything, whatever happens, that healthcare record can capture all the information and tie to us as an individual. And if you go to a different hospital, then that record will follow you. This is how you would expect that to be implemented, but I think we're still on that journey. There are lots and lots of challenges. I've seen anecdotal data around people who suffered because they weren't carrying a card when they went into hospital, because that card has the critical elements of data, but in today's world, should you need to carry a piece of paper or can the entire thing be a digital data flow that can easily be, can certainly navigate through lack of paper and those kinds of things. So the vision that I think we need to be looking at is an effective data exchange or marketplace back with a kind of a backbone model where people agree and sign off a data standard, where each individual's data is always tied to the individual. So if you were to move States, if you would move providers, change insurance companies, none of that would impact your medical history, your data, and the ability to have the other care and medical professionals to access the data at the point of need and at the point of healthcare delivery. So I think that's the vision we're looking at, but as you rightly you said that there are enormous number of challenges, partly because of the history, of healthcare, I think it was technology enablement of healthcare started early. So there's a lot of legacy as well. So we shouldn't trivialize the challenges that the industry faces, but that I think is the way we want to go. >> Well, privacy is obviously a huge one, and a lot of the processes are built around non-digital processes and what you're describing as a flip for digital first. I mean, as a consumer, as a patient, I want an app for that. So I can see my own data. I can see price, price transparency, give access to people that I think need it. And that is a daunting task, isn't it? >> Absolutely. And I think the implicit idea and what you just said, which is very powerful is also on the app you want to control. >> Yes. >> And sometimes you want to be able to change access on data at that point. Right now, I'm at the hospital. I would like to access my data. And when I walk away or maybe three days later, I want to revoke that access. It's that level of control. And absolutely, it is by no means a trivial problem, but I think that's where you need the data automation tools. If you try to do any of this manually, we'd be here for another decade trying to solve this, but that's where tools like Io-Tahoe come in because to do this, a lot of the heavy lifting behind the scenes has to be automated. There has to be a machine churning that and presenting the simpler options. And I know you were talking about it just a little while ago Ajay. I was reminded of the example of a McDonald's or a Coke, because the sales store idea that you can go in and you can do your own ordering off a menu, or you can go in and select five different flavors from a Coke machine and choose your own particular blend of Coke. It's a very trivial example, but I think that's the word we want to get to with access of data as well. If it was that simple for consumers, for enterprise, business people, for doctors, then that's where we ultimately want to be able to arrive. But of course, to make something very simple for the end-user, somebody has to solve for complexity behind the scenes. >> So Ajay, it seems to me Ajay there're two major outcomes here. One is of course, the most important I guess, is patient outcomes, and the other is cost. I mean, they talked about the cost issues, we all, US especially understand the concerns about rising costs of healthcare. My question is this, how does a Smart Data Marketplace fit into achieving those two very important outcomes? >> When we think about how automation is enabling that, where we've got different data formats, the manual tasks are involved, duplication of information. The administrative overhead of that alone and the work, the rework, and the cycles of work that generates. That's really what we're trying to help with data is to eliminate that wasted effort. And with that wasted effort comes time and money to employ people to work through those siloed systems. So getting to the point where there is an exchange in a marketplace just as they would be for banking or insurance is really about automating the classification of data to make it available to a system that can pick it up through an API and to run a machine learning model and to manage a workflow, a process. >> Right, so you mentioned backing insurance, you're right. I mean, we've actually come a long way and just in terms of, know the customer and applying that to know the patient would be very powerful. I'm interested in what you guys are doing together, just in terms of your vision. Are you going to market together, kind of what you're seeing in terms of promoting or enabling this self-service, self-care. Maybe you could talk a little bit about Io-Tahoe and Tata, the intersection at the customer? >> Sure. I think we've been very impressed with the TCS vision of 4.0, how the re-imagining traditional industries, whether it's insurance, banking, healthcare, and bringing together automation, agile processes, robotics, AI, and once those enablers, technology may have brought together to re-imagine how those services can be delivered digitally. All of those are dependent on data. So we see that there's a really good fit here to enable understanding the legacy, the historic situation that has built up over time in an organization, a business and to help shine a light on what's meaningful in that to migrate to the cloud or to drive a digital twin, data science project. >> Ved, anything you can add to that? >> Sure. I mean, we do take the business 4.0 model quite seriously in terms of a lens with which you look at any industry, and what I talked about in healthcare was an example of that. And for us business 4.0, means a few very specific things. The technology that we use in today's verse should be agile, automated, intelligent, and cloud-based. These have become kind of hygiene factors now. On top of that, the businesses we build should be mass customized. They should be risk embracing. They should engage ecosystems, and they should strive for exponential value, not 10% growth year on year, but doubling, tripling every three, four years, because that's the competition that most businesses are facing today. And within that, the Tata group itself, is an extremely purpose-driven business. We really believe that we exist to serve communities, not just one specific set, i.e. shareholders, but the broader community in which we live and work. And I think this framework also allows us to apply that to things like healthcare, to education and to a whole vast range of areas where, everybody has a vision of using data science or doing really clever stuff at the gradients. But what becomes clear is, to do any of that, the first thing you need is a foundational piece. And as a foundation isn't right, then no matter how much you invest in the data science tools you won't get the answers you want. And the work we're doing with the Io-Tahoe really, for me, is particularly exciting because it sorts out that foundational piece. And at the end of it, to make all of this, again, I will repeat that, to make it simple and easy to use for the end user, whoever that is. And I realized that I'm probably the first person who's used fast food as a shining example for healthcare in this discussion, but you can make a lot of different examples. And today, if you press a button and start a car, that's simplicity, but someone has solved for that. And that's what we want to do with data as well. >> Yeah, that makes a lot of sense to me. We talk a lot about digital transformation and a digital business, and I would observe that a digital business puts data at the core. And you can certainly be the best example. There is, of course, Google is an all digital business, but take a company like Amazon, Who's got obviously a massive physical component to its business. Data is at the core. And that's exactly my takeaway from this discussion. Both of you are talking about putting data at the core, simplifying it, making sure that it's compliant, and healthcare it's taking longer, 'cause it's such a high risk industry, but it's clearly happening, COVID I guess, was an accelerant. Guys, Ajay, I'll start with you. Any final thoughts that you want to leave the audience with? _ Yeah, we're really pleased to be working with TCS. We've been able to explore how we're able to put dates to work in a range of different industries. Ved has mentioned healthcare, telecoms, banking and insurance are others. And the same impact they speak to whenever we see the exciting digital transformations that are being planned, being able to accelerate those, unlock the value from data is where we're having a purpose. And it's good that we can help patients in the healthcare sector, consumers in banking realize a better experience through having a more joined up marketplace with their data. >> Ved, you know what excites me about this conversation is that, as a patient or as a consumer, if I'm helping loved ones, I can go to the web and I can search, and I can find a myriad of possibilities. What you're envisioning here is really personalizing that with real time data. And that to me is a game changer. Your final thoughts? >> Thanks, David. I absolutely agree with you that the idea of data centricity and simplicity are absolutely forefront, but I think if we were to design an organization today, you might design it very differently to how most companies today are structured. And maybe Google and Amazon are probably better examples of that because you almost have to think of a business as having a data engine room at its core. A lot of businesses are trying to get to that stage, whereas what we call digital natives, are people who have started life with that premise. So I absolutely agree with you on that, but extending that a little bit. If you think of most industries as eco-systems that have to collaborate, then you've got multiple organizations who will also have to exchange data to achieve some shared outcomes. Whether you look at supply chains of automobile manufacturers or insurance companies or healthcares we've been talking about. So I think that's the next level of change we want to be able to make, which is to be able to do this at scale across organizations at industry level or in population scheme for healthcare. >> Yeah, Thank you for that. Go ahead Ajay. >> David that's where it comes back to again, the origination where we've come from in big data. The volume of data combined with the specificity of individualizing, personalizing a service around an individual amongst that massive data from different providers is where is exciting, that we're able to have an impact. >> Well, and you know Ajay, I'm glad you brought that up because in the early days of big data, there were only a handful of companies, the biggest financial institutions. Obviously, the internet giants who had all these engineers that were able to take advantage of it. But with companies like Io-Tahoe and others, and the investments that the industry has made in terms of providing the tools and simplifying that, especially with machine intelligence and AI and machine learning, these are becoming embedded into the tooling so that everybody can have access to them, small, medium, and large companies. That's really, to me, the exciting part of this new era that we're entering. >> Yeah, and we have placed those, take it down to the level of not-for-profits and smaller businesses that want to innovate and leapfrog into, to growing their digital delivery of their service. >> And I know a lot of time, but Ved, what you were saying about TCS's responsibility to society, I think is really, really important. Large companies like yours, I believe, and you clearly do as well, have a responsibility to society more than just a profit. And I think, Big Tech it's a better app in a lot of cases, but so thank you for that and thank you gentlemen for this great discussion. I really appreciate it. >> Thanks David. >> Thank you. >> All right, keep it right there. I'll be right back right after this short break. This is Dave Vellante for theCUBE. (calm music)

>> Announcer: From around the globe, it's theCube with digital coverage of MIT, Chief Data Officer and Information Quality Symposium brought to you by Silicon Angle Media. >> Hi, I'm Stu Miniman and this is the seventh year of theCubes coverage of the MIT, Chief Data Officer and Information Quality Symposium. We love getting to talk to these chief data officers and the people in this ecosystem, the importance of data, driving data-driven cultures, and really happy to welcome to the program, first time guests Eileen Vitrine, Eileen is the Chief Data Officer for the United States Air Force, Eileen, thank you so much for joining us. >> Thank you Stu really excited about being here today. >> All right, so the United States Air Force, I believe had it first CDO office in 2017, you were put in the CDO role in June of 2018. If you could, bring us back, give us how that was formed inside the Air force and how you came to be in that role. >> Well, Stu I like to say that we are a startup organization and a really mature organization, so it's really about culture change and it began by bringing a group of amazing citizen airman reservists back to the Air Force to bring their skills from industry and bring them into the Air Force. So, I like to say that we're a total force because we have active and reservists working with civilians on a daily basis and one of the first things we did in June was we stood up a data lab, that's based in the Jones building on Andrews Air Force Base. And there, we actually take small use cases that have enterprise focus, and we really try to dig deep to try to drive data insights, to inform senior leaders across the department on really important, what I would call enterprise focused challenges, it's pretty exciting. >> Yeah, it's been fascinating when we've dug into this ecosystem, of course while the data itself is very sensitive and I'm sure for the Air Force, there are some very highest level of security, the practices that are done as to how to leverage data, the line between public and private blurs, because you have people that have come from industry that go into government and people that are from government that have leveraged their experiences there. So, if you could give us a little bit of your background and what it is that your charter has been and what you're looking to build out, as you mentioned that culture of change. >> Well, I like to say I began my data leadership journey as an active duty soldier in the army, and I was originally a transportation officer, today we would use the title condition based maintenance, but back then, it was really about running the numbers so that I could optimize my truck fleet on the road each and every day, so that my soldiers were driving safely. Data has always been part of my leadership journey and so I like to say that one of our challenges is really to make sure that data is part of every airmans core DNA, so that they're using the right data at the right level to drive insights, whether it's tactical, operational or strategic. And so it's really about empowering each and every airman, which I think is pretty exciting. >> There's so many pieces of that data, you talk about data quality, there's obviously the data life cycle. I know your presentation that you're given here at the CDO, IQ talks about the data platform that your team has built, could you explain that? What are the key tenants and what maybe differentiates it from what other organizations might have done? >> So, when we first took the challenge to build our data lab, we really wanted to really come up. Our goal was to have a cross domain solution where we could solve data problems at the appropriate classification level. And so we built the VAULT data platform, VAULT stands for visible, accessible, understandable, linked, and trustworthy. And if you look at the DOD data strategy, they will also add the tenants of interoperability and secure. So, the first steps that we have really focused on is making data visible and accessible to airmen, to empower them, to drive insights from available data to solve their problems. So, it's really about that data empowerment, we like to use the hashtag built by airmen because it's really about each and every airman being part of the solution. And I think it's really an exciting time to be in the Air Force because any airman can solve a really hard challenge and it can very quickly wrap it up rapidly, escalate up with great velocity to senior leadership, to be an enterprise solution. >> Is there some basic training that goes on from a data standpoint? For any of those that have lived in data, oftentimes you can get lost in numbers, you have to have context, you need to understand how do I separate good from bad data, or when is data still valid? So, how does someone in the Air Force get some of that beta data competency? >> Well, we have taken a multitenant approach because each and every airman has different needs. So, we have quite a few pathfinders across the Air Force today, to help what I call, upscale our total force. And so I developed a partnership with the Air Force Institute of Technology and they now have a online graduate level data science certificate program. So, individuals studying at AFIT or remotely have the opportunity to really focus on building up their data touchpoints. Just recently, we have been working on a pathfinder to allow our data officers to get their ICCP Federal Data Sector Governance Certificate Program. So, we've been running what I would call short boot camps to prep data officers to be ready for that. And I think the one that I'm most excited about is that this year, this fall, new cadets at the U.S Air Force Academy will be able to have an undergraduate degree in data science and so it's not about a one prong approach, it's about having short courses as well as academe solutions to up skill our total force moving forward. >> Well, information absolutely is such an important differentiator(laughs) in general business and absolutely the military aspects are there. You mentioned the DOD talks about interoperability in their platform, can you speak a little bit to how you make sure that data is secure? Yet, I'm sure there's opportunities for other organizations, for there to be collaboration between them. >> Well, I like to say, that we don't fight alone. So, I work on a daily basis with my peers, Tom Cecila at the Department of Navy and Greg Garcia at the Department of Army, as well as Mr. David Berg in the DOD level. It's really important that we have an integrated approach moving forward and in the DOD we partner with our security experts, so it's not about us doing security individually, it's really about, in the Air Force we use a term called digital air force, and it's about optimizing and building a trusted partnership with our CIO colleagues, as well as our chief management colleagues because it's really about that trusted partnership to make sure that we're working collaboratively across the enterprise and whatever we do in the department, we also have to reach across our services so that we're all working together. >> Eileen, I'm curious if there's been much impact from the global pandemic. When I talk to enterprise companies, that they had to rapidly make sure that while they needed to protect data, when it was in their four walls and maybe for VPN, now everyone is accessing data, much more work from home and the like. I have to imagine some of those security measures you've already taken, but have there anything along those lines or anything else that this shift in where people are, and a little bit more dispersed has impacted your work? >> Well, the story that I like to say is, that this has given us velocity. So, prior to COVID, we built our VAULT data platform as a multitenancy platform that is also cross-domain solution, so it allows people to develop and do their problem solving in an appropriate classification level. And it allows us to connect or pushup if we need to into higher classification levels. The other thing that it has helped us really work smart because we do as much as we can in that unclassified environment and then using our cloud based solution in our gateways, it allows us to bring people in at a very scheduled component so that we maximize, or we optimize their time on site. And so I really think that it's really given us great velocity because it has really allowed people to work on the right problem set, on the right class of patient level at a specific time. And plus the other pieces, we look at what we're doing is that the problem set that we've had has really allowed people to become more data focused. I think that it's personal for folks moving forward, so it has increased understanding in terms of the need for data insights, as we move forward to drive decision making. It's not that data makes the decision, but it's using the insight to make the decision. >> And one of the interesting conversations we've been having about how to get to those data insights is the use of things like machine learning, artificial intelligence, anything you can share about, how you're looking at that journey, where you are along that discovery. >> Well, I love to say that in order to do AI and machine learning, you have to have great volumes of high quality data. And so really step one was visible, accessible data, but we in the Department of the Air Force stood up an accelerator at MIT. And so we have a group of amazing airmen that are actually working with MIT on a daily basis to solve some of those, what I would call opportunities for us to move forward. My office collaborates with them on a consistent basis, because they're doing additional use cases in that academic environment, which I'm pretty excited about because I think it gives us access to some of the smartest minds. >> All right, Eileen also I understand it's your first year doing the event. Unfortunately, we don't get, all come together in Cambridge, walking those hallways and being able to listen to some of those conversations and follow up is something we've very much enjoyed over the years. What excites you about being interact with your peers and participating in the event this year? >> Well, I really think it's about helping each other leverage the amazing lessons learned. I think that if we look collaboratively, both across industry and in the federal sector, there have been amazing lessons learned and it gives us a great forum for us to really share and leverage those lessons learned as we move forward so that we're not hitting the reboot button, but we actually are starting faster. So, it comes back to the velocity component, it all helps us go faster and at a higher quality level and I think that's really exciting. >> So, final question I have for you, we've talked for years about digital transformation, we've really said that having that data strategy and that culture of leveraging data is one of the most critical pieces of having gone through that transformation. For people that are maybe early on their journey, any advice that you'd give them, having worked through a couple of years of this and the experience you've had with your peers. >> I think that the first thing is that you have to really start with a blank slate and really look at the art of the possible. Don't think about what you've always done, think about where you want to go because there are many different paths to get there. And if you look at what the target goal is, it's really about making sure that you do that backward tracking to get to that goal. And the other piece that I tell my colleagues is celebrate the wins. My team of airmen, they are amazing, it's an honor to serve them and the reality is that they are doing great things and sometimes you want more. And it's really important to celebrate the victories because it's a very long journey and we keep moving the goalposts because we're always striving for excellence. >> Absolutely, it is always a journey that we're on, it's not about the destination. Eileen, thank you so much for sharing all that you've learned and glad you could participate. >> Thank you, STU, I appreciate being included today. Have a great day. >> Thanks and thank you for watching theCube. I'm Stu Miniman stay tuned for more from the MIT, CDO IQ event. (lively upbeat music)

>> From the Cube Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> Hi everyone, this is Dave Vellante and welcome back to the Cube's coverage of the MIT CDOIQ. God, we've been covering this show since probably 2013, really trying to understand the intersection of data and organizations and data quality and how that's evolved over time. And with me to discuss these issues is Krishna Cheriath, who's the Vice President and Chief Data Officer, Bristol-Myers Squibb. Krishna, great to see you, thanks so much for coming on. >> Thank you so much Dave for the invite, I'm looking forward to it. >> Yeah first of all, how are things in your part of the world? You're in New Jersey, I'm also on the East coast, how you guys making out? >> Yeah, I think these are unprecedented times all around the globe and whether it is from a company perspective or a personal standpoint, it is how do you manage your life, how do you manage your work in these unprecedented COVID-19 times has been a very interesting challenge. And to me, what is most amazing has been, I've seen humanity rise up and so to our company has sort of snap to be able to manage our work so that the important medicines that have to be delivered to our patients are delivered on time. So really proud about how we have done as a company and of course, personally, it has been an interesting journey with my kids from college, remote learning, wife working from home. So I'm very lucky and blessed to be safe and healthy at this time. So hopefully the people listening to this conversation are finding that they are able to manage through their lives as well. >> Obviously Bristol-Myers Squibb, very, very strong business. You guys just recently announced your quarter. There's a biologics facility near me in Devon's, Massachusetts, I drive by it all the time, it's a beautiful facility actually. But extremely broad portfolio, obviously some COVID impact, but you're managing through that very, very well, if I understand it correctly, you're taking a collaborative approach to a COVID vaccine, you're now bringing people physically back to work, you've been very planful about that. My question is from your standpoint, what role did you play in that whole COVID response and what role did data play? >> Yeah, I think it's a two part as you rightly pointed out, the Bristol-Myers Squibb, we have been an active partner on the the overall scientific ecosystem supporting many different targets that is, from many different companies I think. Across biopharmaceuticals, there's been a healthy convergence of scientific innovation to see how can we solve this together. And Bristol-Myers Squibb have been an active participant as our CEO, as well as our Chief Medical Officer and Head of Research have articulated publicly. Within the company itself, from a data and technology standpoint, data and digital is core to the response from a company standpoint to the COVID-19, how do we ensure that our work continues when the entire global workforce pivots to a kind of a remote setting. So that really calls on the digital infrastructure to rise to the challenge, to enable a complete global workforce. And I mean workforce, it is not just employees of the company but the all of the third-party partners and others that we work with, the whole ecosystem needs to work. And I think our digital infrastructure has proven to be extremely resilient than that. From a data perspective, I think it is twofold. One is how does the core book of business of data continue to drive forward to make sure that our companies key priorities are being advanced. Secondarily, we've been partnering with a research and development organization as well as medical organization to look at what kind of real world data insights can really help in answering the many questions around COVID-19. So I think it is twofold. Main summary; one is, how do we ensure that the data and digital infrastructure of the company continues to operate in a way that allows us to progress the company's mission even during a time when globally, we have been switched to a remote working force, except for some essential staff from lab and manufacturing standpoint. And secondarily is how do we look at the real-world evidence as well as the scientific data to be a good partner with other companies to look at progressing the societal innovations needed for this. >> I think it's a really prudent approach because let's face it, sometimes one shot all vaccine can be like playing roulette. So you guys are both managing your risk and just as I say, financially, a very, very successful company in a sound approach. I want to ask you about your organization. We've interviewed many, many Chief Data Officers over the years, and there seems to be some fuzziness as to the organizational structure. It's very clear with you, you report in to the CIO, you came out of a technical bag, you have a technical degree but you also of course have a business degree. So you're dangerous from that standpoint. You got both sides which is critical, I would think in your role, but let's start with the organizational reporting structure. How did that come about and what are the benefits of reporting into the CIO? >> I think the Genesis for that as Bristol-Myers Squibb and when I say Bristol-Myers Squibb, the new Bristol-Myers Squibb is a combination of Heritage Bristol-Myers Squibb and Heritage Celgene after the Celgene acquisition last November. So in the Heritage Bristol-Myers Squibb acquisition, we came to a conclusion that in order for BMS to be able to fully capitalize on our scientific innovation potential as well as to drive data-driven decisions across the company, having a robust data agenda is key. Now the question is, how do you progress that? Historically, we had approached a very decentralized mechanism that made a different data constituencies. We didn't have a formal role of a Chief Data Officer up until 2018 or so. So coming from that realization that we need to have an effective data agenda to drive forward the necessary data-driven innovations from an analytic standpoint. And equally importantly, from optimizing our execution, we came to conclusion that we need an enterprise-level data organization, we need to have a first among equals if you will, to be mandated by the CEO, his leadership team, to be the kind of an orchestrator of a data agenda for the company, because data agenda cannot be done individually by a singular CDO. It has to be done in partnership with many stakeholders, business, technology, analytics, et cetera. So from that came this notion that we need an enterprise-wide data organization. So we started there. So for awhile, I would joke around that I had all of the accountabilities of the CDO without the lofty title. So this journey started around 2016, where we create an enterprise-wide data organization. And we made a very conscious choice of separating the data organization from analytics. And the reason we did that is when we look at the bowl of Bristol-Myers Squibb, analytics for example, is core and part of our scientific discovery process, research, our clinical development, all of them have deep data science and analytic embedded in it. But we also have other analytics whether it is part of our sales and marketing, whether it is part of our finance and our enabling functions they catch all across global procurement et cetera. So the world of analytics is very broad. BMS did a separation between the world of analytics and from the world of data. Analytics at BMS is in two modes. There is a central analytics organization called Business Insights and Analytics that drive most of the enterprise-level analytics. But then we have embedded analytics in our business areas, which is research and development, manufacturing and supply chain, et cetera, to drive what needs to be closer to the business idea. And the reason for separating that out and having a separate data organization is that none of these analytic aspirations or the business aspirations from data will be met if the world of data is, you don't have the right level of data available, the velocity of data is not appropriate for the use cases, the quality of data is not great or the control of the data. So that we are using the data for the right intent, meeting the compliance and regulatory expectations around the data is met. So that's why we separated out that data world from the analytics world, which is a little bit of a unique construct for us compared to what we see generally in the world of CDOs. And from that standpoint, then the decision was taken to make that report for global CIO. At Bristol-Myers Squibb, they have a very strong CIO organization and IT organization. When I say strong, it is from this lens standpoint. A, it is centralized, we have centralized the budget as well as we have centralized the execution across the enterprise. And the CDO reporting to the CIO with that data-specific agenda, has a lot of value in being able to connect the world of data with the world of technology. So at BMS, their Chief Data Officer organization is a combination of traditional CDO-type accountabilities like data risk management, data governance, data stewardship, but also all of the related technologies around master data management, data lake, data and analytic engineering and a nascent AI data and technology lab. So that construct allows us to be a true enterprise horizontal, supporting analytics, whether it is done in a central analytics organization or embedded analytics teams in the business area, but also equally importantly, focus on the world of data from operational execution standpoint, how do we optimize data to drive operational effectiveness? So that's the construct that we have where CDO reports to the CIO, data organization separated from analytics to really focus around the availability but also the quality and control of data. And the last nuance that is that at BMS, the Chief Data Officer organization is also accountable to be the Data Protection Office. So we orchestrate and facilitate all privacy-related actions across because that allows us to make sure that all personal data that is collected, managed and consumed, meets all of the various privacy standards across the world, as well as our own commitments as a company from across from compliance principles standpoint. >> So that makes a lot of sense to me and thank you for that description. You're not getting in the way of R&D and the scientists, they know data science, they don't need really your help. I mean, they need to innovate at their own pace, but the balance of the business really does need your innovation, and that's really where it seems like you're focused. You mentioned master data management, data lakes, data engineering, et cetera. So your responsibility is for that enterprise data lifecycle to support the business side of things, and I wonder if you could talk a little bit about that and how that's evolved. I mean a lot has changed from the old days of data warehouse and cumbersome ETL and you mentioned, as you say data lakes, many of those have been challenging, expensive, slow, but now we're entering this era of cloud, real-time, a lot of machine intelligence, and I wonder if you could talk about the changes there and how you're looking at and thinking about the data lifecycle and accelerating the time to insights. >> Yeah, I think the way we think about it, we as an organization in our strategy and tactics, think of this as a data supply chain. The supply chain of data to drive business value whether it is through insights and analytics or through operation execution. When you think about it from that standpoint, then we need to get many elements of that into an effective stage. This could be the technologies that is part of that data supply chain, you reference some of them, the master data management platforms, data lake platforms, the analytics and reporting capabilities and business intelligence capabilities that plug into a data backbone, which is that I would say the technology, swim lane that needs to get right. Along with that, what we also need to get right for that effective data supply chain is that data layer. That is, how do you make sure that there is the right data navigation capability, probably you make sure that we have the right ontology mapping and the understanding around the data. How do we have data navigation? It is something that we have invested very heavily in. So imagine a new employee joining BMS, any organization our size has a pretty wide technology ecosystem and data ecosystem. How do you navigate that, how do we find the data? Data discovery has been a key focus for us. So for an effective data supply chain, then we knew that and we have instituted our roadmap to make sure that we have a robust technology orchestration of it, but equally important is an effective data operations orchestration. Both needs to go hand in hand for us to be able to make sure that that supply chain is effective from a business use case and analytic use standpoint. So that has led us on a journey from a cloud perspective, since you refer that in your question, is we have invested very heavily to move from very disparate set of data ecosystems to a more converse cloud-based data backbone. That has been a big focus at the BMS since 2016, whether it is from a research and development standpoint or from commercialization, it is our word for the sales and marketing or manufacturing and supply chain and HR, et cetera. How do we create a converged data backbone that allows us to use that data as a resource to drive many different consumption patterns? Because when you imagine an enterprise of our size, we have many different consumers of the data. So those consumers have different consumption needs. You have deep data science population who just needs access to the data and they have data science platforms but they are at once programmers as well, to the other end of the spectrum where executives need pre-packaged KPIs. So the effective orchestration of the data ecosystem at BMS through a data supply chain and the data backbone, there's a couple of things for us. One, it drives productivity of our data consumers, the scientific researchers, analytic community or other operational staff. And second, in a world where we need to make sure that the data consumption appalls ethical standards as well as privacy and other regulatory expectations, we are able to build it into our system and process the necessary controls to make sure that the consumption and the use of data meets our highest trust advancements standards. >> That makes a lot of sense. I mean, converging your data like that, people always talk about stove pipes. I know it's kind of a bromide but it's true, and allows you to sort of inject consistent policies. What about automation? How has that affected your data pipeline recently and on your journey with things like data classification and the like? >> I think in pursuing a broad data automation journey, one of the things that we did was to operate at two different speed points. In a historically, the data organizations have been bundled with long-running data infrastructure programs. By the time you complete them, their business context have moved on and the organization leaders are also exhausted from having to wait from these massive programs to reach its full potential. So what we did very intentionally from our data automation journey is to organize ourselves in two speed dimensions. First, a concept called Rapid Data Lab. The idea is that recognizing the reality that the data is not well automated and orchestrated today, we need a SWAT team of data engineers, data SMEs to partner with consumers of data to make sure that we can make effective data supply chain decisions here and now, and enable the business to answer questions of today. Simultaneously in a longer time horizon, we need to do the necessary work of moving the data automation to a better footprint. So enterprise data lake investments, where we built services based on, we had chosen AWS as the cloud backbone for data. So how do we use the AWS services? How do we wrap around it with the necessary capabilities so that we have a consistent reference and technical architecture to drive the many different function journeys? So we organized ourselves into speed dimensions; the Rapid Data Lab teams focus around partnering with the consumers of data to help them with data automation needs here and now, and then a secondary team focused around the convergence of data into a better cloud-based data backbone. So that allowed us to one, make an impact here and now and deliver value from data to the dismiss here and now. Secondly, we also learned a lot from actually partnering with consumers of data on what needs to get adjusted over a period of time in our automation journey. >> It makes sense, I mean again, that whole notion of converged data, putting data at the core of your business, you brought up AWS, I wonder if I could ask you a question. You don't have to comment on specific vendors, but there's a conversation we have in our community. You have AWS huge platform, tons of partners, a lot of innovation going on and you see innovation in areas like the cloud data warehouse or data science tooling, et cetera, all components of that data pipeline. As well, you have AWS with its own tooling around there. So a question we often have in the community is will technologists and technology buyers go for kind of best of breed and cobble together different services or would they prefer to have sort of the convenience of a bundled service from an AWS or a Microsoft or Google, or maybe they even go best of breeds for all cloud. Can you comment on that, what's your thinking? >> I think, especially for organizations, our size and breadth, having a converged to convenient, all of the above from a single provider does not seem practical and feasible, because a couple of reasons. One, the heterogeneity of the data, the heterogeneity of consumption of the data and we are yet to find a single stack provider who can meet all of the different needs. So I am more in the best of breed camp with a few caveats, a hybrid best of breed, if you will. It is important to have a converged the data backbone for the enterprise. And so whether you invest in a singular cloud or private cloud or a combination, you need to have a clear intention strategy around where are you going to host the data and how is the data is going to be organized. But you could have a lot more flexibility in the consumption of data. So once you have the data converged into, in our case, we converged on AWS-based backbone. We allow many different consumptions of the data, because I think the analytic and insights layer, data science community within R&D is different from a data science community in the supply chain context, we have business intelligence needs, we have a catered needs and then there are other data needs that needs to be funneled into software as service platforms like the sales forces of the world, to be able to drive operational execution as well. So when you look at it from that context, having a hybrid model of best of breed, whether you have a lot more convergence from a data backbone standpoint, but then allow for best of breed from an analytic and consumption of data is more where my heart and my brain is. >> I know a lot of companies would be excited to hear that answer, but I love it because it fosters competition and innovation. I wish I could talk for you forever, but you made me think of another question which is around self-serve. On your journey, are you at the point where you can deliver self-serve to the lines of business? Is that something that you're trying to get to? >> Yeah, I think it does. The self-serve is an absolutely important point because I think the traditional boundaries of what you consider the classical IT versus a classical business is great. I think there is an important gray area in the middle where you have a deep citizen data scientist in the business community who really needs to be able to have access to the data and I have advanced data science and programming skills. So self-serve is important but in that, companies need to be very intentional and very conscious of making sure that you're allowing that self-serve in a safe containment sock. Because at the end of the day, whether it is a cyber risk or data risk or technology risk, it's all real. So we need to have a balanced approach between promoting whether you call it data democratization or whether you call it self-serve, but you need to balance that with making sure that you're meeting the right risk mitigation strategy standpoint. So that's how then our focus is to say, how do we promote self-serve for the communities that they need self-serve, where they have deeper levels of access? How do we set up the right safe zones for those which may be the appropriate mitigation from a cyber risk or data risk or technology risk. >> Security pieces, again, you keep bringing up topics that I could talk to you forever on, but I heard on TV the other night, I heard somebody talking about how COVID has affected, because of remote access, affected security. And it's like hey, give everybody access. That was sort of the initial knee-jerk response, but the example they gave as well, if your parents go out of town and the kid has a party, you may have some people show up that you don't want to show up. And so, same issue with remote working, work from home. Clearly you guys have had to pivot to support that, but where does the security organization fit? Does that report separate alongside the CIO? Does it report into the CIO? Are they sort of peers of yours, how does that all work? >> Yeah, I think at Bristol-Myers Squibb, we have a Chief Information Security Officer who is a peer of mine, who also reports to the global CIO. The CDO and the CSO are effective partners and are two sides of the coin and trying to advance a total risk mitigation strategy, whether it is from a cyber risk standpoint, which is the focus of the Chief Information Security Officer and whether it is the general data consumption risk. And that is the focus from a Chief Data Officer in the capacities that I have. And together, those are two sides of a coin that the CIO needs to be accountable for. So I think that's how we have orchestrated it, because I think it is important in these worlds where you want to be able to drive data-driven innovation but you want to be able to do that in a way that doesn't open the company to unwanted risk exposures as well. And that is always a delicate balancing act, because if you index too much on risk and then high levels of security and control, then you could lose productivity. But if you index too much on productivity, collaboration and open access and data, it opens up the company for risks. So it is a delicate balance within the two. >> Increasingly, we're seeing that reporting structure evolve and coalesce, I think it makes a lot of sense. I felt like at some point you had too many seats at the executive leadership table, too many kind of competing agendas. And now your structure, the CIO is obviously a very important position. I'm sure has a seat at the leadership table, but also has the responsibility for managing that sort of data as an asset versus a liability which my view, has always been sort of the role of the Head of Information. I want to ask you, I want to hit the Escape key a little bit and ask you about data as a resource. You hear a lot of people talk about data is the new oil. We often say data is more valuable than oil because you can use it, it doesn't follow the laws of scarcity. You could use data in infinite number of places. You can only put oil in your car or your house. How do you think about data as a resource today and going forward? >> Yeah, I think the data as the new oil paradigm in my opinion, was an unhealthy, and it prompts different types of conversations around that. I think for certain companies, data is indeed an asset. If you're a company that is focused on information products and data products and that is core of your business, then of course there's monetization of data and then data as an asset, just like any other assets on the company's balance sheet. But for many enterprises to further their mission, I think considering data as a resource, I think is a better focus. So as a vital resource for the company, you need to make sure that there is an appropriate caring and feeding for it, there is an appropriate management of the resource and an appropriate evolution of the resource. So that's how I would like to consider it, it is a personal end of one perspective, that data as a resource that can power the mission of the company, the new products and services, I think that's a good, healthy way to look at it. At the center of it though, a lot of strategies, whether people talk about a digital strategy, whether the people talk about data strategy, what is important is a company to have a pool north star around what is the core mission of the company and what is the core strategy of the company. For Bristol-Myers Squibb, we are about transforming patients' lives through science. And we think about digital and data as key value levers and drivers of that strategy. So digital for the sake of digital or data strategy for the sake of data strategy is meaningless in my opinion. We are focused on making sure that how do we make sure that data and digital is an accelerant and has a value lever for the company's mission and company strategy. So that's why thinking about data as a resource, as a key resource for our scientific researchers or a key resource for our manufacturing team or a key resource for our sales and marketing, allows us to think about the actions and the strategies and tactics we need to deploy to make that effective. >> Yeah, that makes a lot of sense, you're constantly using that North star as your guideline and how data contributes to that mission. Krishna Cheriath, thanks so much for coming on the Cube and supporting the MIT Chief Data Officer community, it was a really pleasure having you. >> Thank you so much for Dave, hopefully you and the audience is safe and healthy during these times. >> Thank you for that and thank you for watching everybody. This is Vellante for the Cube's coverage of the MIT CDOIQ Conference 2020 gone virtual. Keep it right there, we'll right back right after this short break. (lively upbeat music)

>>from around the globe If the cube with digital coverage of M I t. Chief data officer and Information quality symposium brought to you by Silicon Angle Media >>Hello, everyone. This is Day Volonte and welcome back to our continuing coverage of the M I t. Chief Data Officer CDO I Q event Interpol Bhandari is here. He's a leading voice in the CDO community and a longtime Cubillan Interpol. Great to see you. Thanks for coming on for this. Especially >>program. My pleasure. >>So when you you and I first met, you laid out what I thought was, you know, one of the most cogent frameworks to understand what a CDO is job was where the priority should be. And one of those was really understanding how, how, how data contributes to the monetization of station aligning with lines of business, a number of other things. And that was several years ago. A lot of change since then. You know, we've been doing this conference since probably twenty thirteen and back then, you know, Hadoop was coming on strong. A lot of CEOs didn't want to go near the technology that's beginning to change. CDOs and cto Zehr becoming much more aligned at the hip. The reporting organizations have changed. But I love your perspective on what you've observed as changing in the CDO roll over the last half decade or so. >>Well, did you know that I became chief data officer in two thousand six? December two thousand and six And I have done this job four times four major overnight have created of the organization from scratch each time. Now, in December of two thousand six, when I became chief data officer, there were only four. Chief Data Officer, uh, boom and I was the first in health care, and there were three, three others, you know, one of the Internet one and credit guns one and banking. And I think I'm the only one actually left standing still doing this job. That's a good thing or a bad thing. But like, you know, it certainly has allowed me to love the craft and then also scripted down to the level that, you know, I actually do think of it purely as a craft. That is. I know, going into a mutual what I'm gonna do. They were on the central second. No, the interesting things that have unfolded. Obviously, the professions taken off There are literally thousands off chief data officers now, and there are plenty off changes. I think the main change, but the job is it's, I think, a little less daunting in terms off convincing the senior leadership that it's need it because I think the awareness at the CEO level is much, much, much better than what it waas in two thousand six. Across the world. Now, having said that, I think it is still only awareness and don't think that there's really a deep understanding of those levels. And so there's a lot off infusion, which is why you will. You kind of think this is my period. But you saw all these professions take off with C titles, right? Chief Data officer, chief analytics officer, chief digital officer and chief technology officer. See, I off course is being there for a long time. And but I think these newer see positions. They're all very, very related, and they all kind of went to the same need which had to do with enterprise transformation, digital transformation, that enterprises chief digital officer, that's another and and people were all trying to essentially feel the elephants and they could only see part of it at the senior levels, and they came up with which have a role you know, seemed most meaningful to them. But really, all of us are trying to do the same job, which is to accelerate digital transformation in the enterprise. Your comment about you kind of see that the seat eels and sea deals now, uh, partnering up much more than in the past, and I think that's in available the major driving force full. That is, in my view, anyway. It's is artificial intelligence as people try to infuse artificial intelligence. Well, then it's very technical field. Still, it's not something that you know you can just hand over to somebody who has the business jobs, but not the deep technical chops to pull that off. And so, in the case off chief data officers that do have the technical jobs, you'll see them also pretty much heading up the I effort in total and you know, as I do for the IBM case, will be building the Data and AI Enablement internal platform for for IBM. But I think in other cases you you've got Chief date officers who are coming in from a different angle. You know, they built Marghera but the CTO now, because they have to. Otherwise you cannot get a I infused into the organization. >>So there were a lot of other priorities, obviously certainly digital transformation. We've been talking about it for years, but still in many organisations, there was a sense of, well, not on my watch, maybe a sense of complacency or maybe just other priorities. Cove. It obviously has changed that now one hundred percent of the companies that we talked to are really putting this digital transformation on the front burner. So how has that changed the role of CDO? Has it just been interpolate an acceleration of that reality, or has it also somewhat altered the swim lanes? >>I think I think it's It's It's Bolt actually, so I have a way of looking at this in my mind, the CDO role. But if you look at it from a business perspective, they're looking for three things. The CEO is looking for three things from the CDO. One is you know this person is going to help with the revenue off the company by enabling the production of new products, new products of resulting in new revenue and so forth. That's kind of one aspect of the monetization. Another aspect is the CEO is going to help with the efficiency within the organization by making data a lot more accessible, as well as enabling insights that reduce into and cycle time for major processes. And so that's another way that they have monitor. And the last one is a risk reduction that they're going to reduce the risk, you know, as regulations. And as you have cybersecurity exposure on incidents that you know just keep keep accelerating as well. You're gonna have to also step in and help with that. So every CDO, the way their senior leadership looks at them is some mix off three. And in some cases, one has given more importance than the other, and so far, but that's how they are essentially looking at it now. I think what digital transformation has done is it's managed to accelerate, accelerate all three off these outcomes because you need to attend to all three as you move forward. But I think that the individual balance that's struck for individuals reveals really depends on their ah, their company, their situation, who their peers are, who is actually leading the transformation and so >>forth, you know, in the value pie. A lot of the early activity around CDO sort of emanated from the quality portions of the organization. It was sort of a compliance waited roll, not necessarily when you started your own journey here. Obviously been focused on monetization how data contributes to that. But But you saw that generally, organizations, even if they didn't have a CDO, they had this sort of back office alliance thing that has totally changed the the in the value equation. It's really much more about insights, as you mentioned. So one of the big changes we've seen in the organization is that data pipeline you mentioned and and cycle time. And I'd like to dig into that a little bit because you and I have talked about this. This is one of the ways that a chief data officer and the related organizations can add the most value reduction in that cycle time. That's really where the business value comes from. So I wonder if we could talk about that a little bit and how that the constituents in the stakeholders in that in that life cycle across that data pipeline have changed. >>That's a very good question. Very insightful questions. So if you look at ah, company like idea, you know, my role in totally within IBM is to enable Ibn itself to become an AI enterprise. So infuse a on into all our major business processes. You know, things like our supply chain lead to cash well, process, you know, our finance processes like accounts receivable and procurement that soulful every major process that you can think off is using Watson mouth. So that's the That's the That's the vision that's essentially what we've implemented. And that's how we are using that now as a showcase for clients and customers. One of the things that be realized is the data and Ai enablement spots off business. You know, the work that I do also has processes. Now that's the pipeline you refer to. You know, we're setting up the data pipeline. We're setting up the machine learning pipeline, deep learning blank like we're always setting up these pipelines, And so now you have the opportunity to actually turn the so called EI ladder on its head because the Islander has to do with a first You collected data, then you curated. You make sure that it's high quality, etcetera, etcetera, fit for EI. And then eventually you get to applying, you know, ai and then infusing it into business processes. And so far, But once you recognize that the very first the earliest creases of work with the data those themselves are essentially processes. You can infuse AI into those processes, and that's what's made the cycle time reduction. And although things that I'm talking about possible because it just makes it much, much easier for somebody to then implement ai within a lot enterprise, I mean, AI requires specialized knowledge. There are pieces of a I like deep learning, but there are, you know, typically a company's gonna have, like a handful of people who even understand what that is, how to apply it. You know how models drift when they need to be refreshed, etcetera, etcetera, and so that's difficult. You can't possibly expect every business process, every business area to have that expertise, and so you've then got to rely on some core group which is going to enable them to do so. But that group can't do it manually because I get otherwise. That doesn't scale again. So then you come down to these pipelines and you've got to actually infuse AI into these data and ai enablement processes so that it becomes much, much easier to scale across another. >>Some of the CEOs, maybe they don't have the reporting structure that you do, or or maybe it's more of a far flung organization. Not that IBM is not far flung, but they may not have the ability to sort of inject AI. Maybe they can advocate for it. Do you see that as a challenge for some CEOs? And how do they so to get through that, what's what's the way in which they should be working with their constituents across the organization to successfully infuse ai? >>Yeah, that's it's. In fact, you get a very good point. I mean, when I joined IBM, one of the first observations I made and I in fact made it to a senior leadership, is that I didn't think that from a business standpoint, people really understood what a I met. So when we talked about a cognitive enterprise on the I enterprise a zaydi em. You know, our clients don't really understand what that meant, which is why it became really important to enable IBM itself to be any I enterprise. You know that. That's my data strategy. Your you kind of alluded to the fact that I have this approach. There are these five steps, while the very first step is to come up with the data strategy that enables a business strategy that the company's on. And in my case, it was, Hey, I'm going to enable the company because it wants to become a cloud and cognitive company. I'm going to enable that. And so we essentially are data strategy became one off making IBM. It's something I enterprise, but the reason for doing that the reason why that was so important was because then we could use it as a showcase for clients and customers. And so But I'm talking with our clients and customers. That's my role. I'm really the only role I'm playing is what I call an experiential selling there. I'm saying, Forget about you know, the fact that we're selling this particular product or that particular product that you got GPU servers. We've got you know what's an open scale or whatever? It doesn't really matter. Why don't you come and see what we've done internally at scale? And then we'll also lay out for you all the different pain points that we have to work through using our products so that you can kind of make the same case when you when you when you apply it internally and same common with regard to the benefit, you know the cycle, time reduction, some of the cycle time reductions that we've seen in my process is itself, you know, like this. Think about metadata business metadata generating that is so difficult. And it's again, something that's critical if you want to scale your data because you know you can't really have a good catalogue of data if you don't have good business, meditate. Eso. Anybody looking at what's in your catalog won't understand what it is. They won't be able to use it etcetera. And so we've essentially automated business metadata generation using AI and the cycle time reduction that was like ninety five percent, you know, haven't actually argue. It's more than that, because in the past, most people would not. For many many data sets, the pragmatic approach would be. Don't even bother with the business matter data. Then it becomes just put somewhere in the are, you know, data architecture somewhere in your data leg or whatever, you have data warehouse, and then it becomes the data swamp because nobody understands it now with regard to our experience applying AI, infusing it across all our major business processes are average cycle time reduction is seventy percent, so just a tremendous amount of gains are there. But to your point, unless you're able to point to some application at scale within the enterprise, you know that's meaningful for the enterprise, Which is kind of what the what the role I play in terms of bringing it forward to our clients and customers. It's harder to argue. I'll make a case or investment into A I would then be enterprise without actually being able to point to those types of use cases that have been scaled where you can demonstrate the value. So that's extremely important part of the equation. To make sure that that happens on a regular basis with our clients and customers, I will say that you know your point is vomited a lot off. Our clients and customers come back and say, Tell me when they're having a conversation. I was having a conversation just last week with major major financial service of all nations, and I got the same point saying, If you're coming out of regulation, how do I convince my leadership about the value of a I and you know, I basically responded. He asked me about the scale use cases You can show that. But perhaps the biggest point that you can make as a CDO after the senior readership is can we afford to be left up? That is the I think the biggest, you know, point that the leadership has to appreciate. Can you afford to be left up? >>I want to come back to this notion of seventy percent on average, the cycle time reduction. That's astounding. And I want to make sure people understand the potential impacts. And, I would say suspected many CEOs, if not most understand sort of system thinking. It's obviously something that you're big on but often times within organisations. You might see them trying to optimize one little portion of the data lifecycle and you know having. Okay, hey, celebrate that success. But unless you can take that systems view and reduce that overall cycle time, that's really where the business value is. And I guess my we're real question around. This is Every organization has some kind of Northstar, many about profit, and you can increase revenue are cut costs, and you can do that with data. It might be saving lives, but ultimately to drive this data culture, you've got to get people thinking about getting insights that help you with that North Star, that mission of the company, but then taking a systems view and that's seventy percent cycle time reduction is just the enormous business value that that drives, I think, sometimes gets lost on people. And these air telephone numbers in the business case aren't >>yes, No, absolutely. It's, you know, there's just a tremendous amount of potential on, and it's it's not an easy, easy thing to do by any means. So we've been always very transparent about the Dave. As you know, we put forward this this blueprint right, the cognitive enterprise blueprint, how you get to it, and I kind of have these four major pillars for the blueprint. There's obviously does this data and you're getting the data ready for the consummation that you want to do but also things like training data sets. How do you kind of run hundreds of thousands of experiments on a regular basis, which kind of review to the other pillar, which is techology? But then the last two pillars are business process, change and the culture organizational culture, you know, managing organizational considerations, that culture. If you don't keep all four in lockstep, the transformation is usually not successful at an end to end level, then it becomes much more what you pointed out, which is you have kind of point solutions and the role, you know, the CEO role doesn't make the kind of strategic impact that otherwise it could do so and this also comes back to some of the only appointee of you to do. If you think about how do you keep those four pillars and lock sync? It means you've gotta have the data leader. You also gotta have the technology, and in some cases they might be the same people. Hey, just for the moment, sake of argument, let's say they're all different people and many, many times. They are so the data leader of the technology of you and the operations leaders because the other ones own the business processes as well as the organizational years. You know, they've got it all worked together to make it an effective conservation. And so the organization structure that you talked about that in some cases my peers may not have that. You know, that's that. That is true. If the if the senior leadership is not thinking overall digital transformation, it's going to be difficult for them to them go out that >>you've also seen that culturally, historically, when it comes to data and analytics, a lot of times that the lines of business you know their their first response is to attack the quality of the data because the data may not support their agenda. So there's this idea of a data culture on, and I want to ask you how self serve fits into that. I mean, to the degree that the business feels as though they actually have some kind of ownership in the data, and it's largely, you know, their responsibility as opposed to a lot of the finger pointing that has historically gone on. Whether it's been decision support or enterprise data, warehousing or even, you know, Data Lakes. They've sort of failed toe live up to that. That promise, particularly from a cultural standpoint, it and so I wonder, How have you guys done in that regard? How did you get there? Many Any other observations you could make in that regard? >>Yeah. So, you know, I think culture is probably the hardest nut to crack all of those four pillars that I back up and you've got You've got to address that, Uh, not, you know, not just stop down, but also bottom up as well. As you know, period. Appear I'll give you some some examples based on our experience, that idea. So the way my organization is set up is there is a obviously a technology on the other. People who are doing all the data engineering were kind of laying out the foundational technical elements or the transformation. You know, the the AI enabled one be planning networks, and so so that are those people. And then there is another senior leader who reports directly to me, and his organization is all around adoptions. He's responsible for essentially taking what's available in the technology and then working with the business areas to move forward and make this make and infuse. A. I do the processes that the business and he is looking. It's done in a bottom upwards, deliberately set up, designed it to be bottom up. So what I mean by that is the team on my side is fully empowered to move forward. Why did they find a like minded team on the other side and go ahead and do it? They don't have to come back for funding they don't have, You know, they just go ahead and do it. They're basically empowered to do that. And that particular set up enabled enabled us in a couple of years to have one hundred thousand internal users on our Central data and AI enabled platform. And when I mean hundred thousand users, I mean users who were using it on a monthly basis. We company, you know, So if you haven't used it in a month, we won't come. So there it's over one hundred thousand, even very rapidly to that. That's kind of the enterprise wide storm. That's kind of the bottom up direction. The top down direction Waas the strategic element that I talked with you about what I said, Hey, be our data strategy is going to be to create, make IBM itself into any I enterprise and then use that as a showcase for plants and customers That kind of and be reiterated back. And I worked the senior leadership on that view all the time talking to customers, the central and our senior leaders. And so that's kind of the air cover to do this, you know, that mix gives you, gives you that possibility. I think from a peer to peer standpoint, but you get to these lot scale and to end processes, and that there, a couple of ways I worked that one way is we've kind of looked at our enterprise data and said, Okay, therefore, major pillars off data that we want to go after data, tomato plants, data about our offerings, data about financial data, that s and then our work full student and then within that there are obviously some pillars, like some sales data that comes in and, you know, been workforce. You could have contractors. Was his employees a center But I think for the moment, about these four major pillars off data. And so let me map that to end to end large business processes within the company. You know, the really large ones, like Enterprise Performance Management, into a or lead to cash generation into and risk insides across our full supply chain and to and things like that. And we've kind of tied these four major data pillars to those major into and processes Well, well, yes, that there's a mechanism they're obviously in terms off facilitating, and to some extent one might argue, even forcing some interaction between teams that are the way they talk. But it also brings me and my peers much closer together when you set it up that way. And that means, you know, people from the HR side people from the operation side, the data side technology side, all coming together to really move things forward. So all three tracks being hit very, very hard to move the culture fall. >>Am I also correct that you have, uh, chief data officers that reporting to you whether it's a matrix or direct within the division's? Is that right? >>Yeah, so? So I mean, you know, for in terms off our structure, as you know, way our global company, we're also far flung company. We have many different products in business units and so forth. And so, uh, one of the things that I realized early on waas we are going to need data officers, each of those business units and the business units. There's obviously the enterprise objective. And, you know, you could think of the enterprise objectives in terms of some examples based on what I said in the past, which is so enterprise objective would be We've gotta have a data foundation by essentially making data along these four pillars. I talked about clients offerings, etcetera, you know, very accessible self service. You have mentioned south, so thank you. This is where the South seven speaks. Comes it right. So you can you can get at that data quickly and appropriately, right? You want to make sure that the access control, all that stuff is designed out and you're able to change your policies and you'd swap manual. But, you know, those things got implemented very rapidly and quickly. And so you've got you've got that piece off off the off the puzzle due to go after. And then I think the other aspect off off. This is, though, when you recognize that every business unit also has its own objectives and they are looking at some of those things somewhat differently. So I'll give you an example. We've got data any our product units. Now, those CEOs right there, concern is going to be a lot more around the products themselves And how were monetizing those box and so they're not per se concerned with, You know, how you reduce the enter and cycle time off IBM in total supply chain so that this is my point. So they but they're gonna have substantial considerations and objectives that they want to accomplish. And so I recognize that early on, and we came up with this notion off a data officer council and I helped staff the council s. So this is why that's the Matrix to reporting that we talked about. But I selected some of the key Blair's that we have in those units, and I also made sure they were funded by the unit. So they report into the units because their paycheck is actually determined. Pilot unit and which makes them than aligned with the objectives off the unit, but also obviously part of my central approach so that I can disseminate it out to the organization. It comes in very, very handy when you are trying to do things across the company as well. So when we you know GDP our way, we have to get the company ready for Judy PR, I would say that this mechanism became a key key aspect of what enabled us to move forward and do it rapidly. Trouble them >>be because you had the structure that perhaps the lines of business weren't. Maybe is concerned about GDP are, but you had to be concerned with it overall. And this allowed you to sort of hiding their importance, >>right? Because think of in the case of Jeannie PR, they have to be a company wide policy and implementation, right? And if he did not have that structure already in place, it would have made it that much harder. Do you get that uniformity and consistency across the company, right, You know, So you will have to in the weapon that structure, but we already have it because way said Hey, this is around for data. We're gonna have these types of considerations that they are. And so we have this thing regular. You know, this man network that meat meets regularly every month, actually, and you know, when things like GDP are much more frequently than that, >>right? So that makes sense. We're out of time. But I wonder if we could just close if you could address the M I t CDO audience that probably this is the largest audience, Believe or not, now that it's that's virtual definitely expanded the audience, but it's still a very elite group. And the reason why I was so pleased that you agreed to do this is because you've got one of the more complex organizations out there and you've succeeded. And, ah, a lot of the hard, hard work. So what? What message would you leave the M I t CDO audience Interpol? >>So I would say that you know, it's it's this particular professional. Receiving a profession is, uh, if I have to pick one trait of let me pick two traits, I think what is your A change agent? So you have to be really comfortable with change things are going to change, the organization is going to look to you to make those changes. And so that's what aspect off your job, you know, may or may not be part of me immediately. But the those particular set of skills and characteristics and something that you know, one has to, uh one has to develop or time, And I think the other thing I would say is it's a continuous looming jaw. So you continue sexism and things keep changing around you and changing rapidly. And, you know, if you just even think just in terms off the subject areas, I mean this Syria today you've got to understand technology. Obviously, you've gotta understand data you've got to understand in a I and data science. You've got to understand cybersecurity. You've gotta understand the regulatory framework, and you've got to keep all that in mind, and you've got to distill it down to certain trends. That's that's happening, right? I mean, so this is an example of that is that there's a trend towards more regulation around privacy and also in terms off individual ownership of data, which is very different from what's before the that's kind of weather. Bucket's going and so you've got to be on top off all those things. And so the you know, the characteristic of being a continual learner, I think is a is a key aspect off this job. One other thing I would add. And this is All Star Coleman nineteen, you know, prik over nineteen in terms of those four pillars that we talked about, you know, which had to do with the data technology, business process and organization and culture. From a CDO perspective, the data and technology will obviously from consent, I would say most covert nineteen most the civil unrest. And so far, you know, the other two aspects are going to be critical as we move forward. And so the people aspect of the job has never bean, you know, more important down it's today, right? That's something that I find myself regularly doing the stalking at all levels of the organization, one on a one, which is something that we never really did before. But now we find time to do it so obviously is doable. I don't think it's just it's a change that's here to stay, and it ships >>well to your to your point about change if you were in your comfort zone before twenty twenty two things years certainly taking you out of it into Parliament. All right, thanks so much for coming back in. The Cuban addressing the M I t CDO audience really appreciate it. >>Thank you for having me. That my pleasant >>You're very welcome. And thank you for watching everybody. This is Dave a lot. They will be right back after this short >>break. You're watching the queue.