>>And welcome back to the Cube everyone. I'm John Walls to continue our coverage here of AWS Reinvent 22. We're part of the AWS Startup Showcase is the global startup program that AWS so proudly sponsors and with us to talk about what they're doing now in the AWS space. Shaun Knapps, the CEO of AS Send IO and Sean, good to have here with us. We appreciate >>It. Thanks for having me, >>John. Yeah, thanks for the time. First off, gotta show the t-shirt. You caught my attention. Big data is a cluster. I don't think you get a lot of argument from some folks, right? But it's your job to make some sense of it, is it not? Yeah. Tell us about a Send io. >>Sure. As Send IO is a data automation platform. What we do is connect a lot of the, the disparate parts of what data teams do when they create ETL and E o T data pipelines. And we use advanced levels of automation to make it easier and faster for them to build these complex systems and have their world be a little bit less of a, a cluster. >>All right. So let's get into automation a little bit then again, I, your definition of automation and how you're applying it to your business case. >>Absolutely. You know, what we see oftentimes is as spaces mature and evolve, the number of repetitive and repeatable tasks that actually become far less differentiating, but far more taxable if you will, right to the business, start to accumulate as those common patterns emerge. And, and, you know, as we see standardization around tech stacks, like on Amazon and on Snowflake and on data bricks, and as you see those patterns really start to, to formalize and standardize, it opens up the door to basically not have your team have to do all those things anymore and write code or perform the same actions that they used to always have to, and you can lean more on technology to properly automate and remove the, the monotony of those tasks and give your teams greater leverage. >>All right. So, so let's talk about at least maybe your, the journey, say in the past 18 months in terms of automation and, and what have you seen from a trend perspective and how are you trying to address that in order to, to meet that need? >>Yeah, I think the last 18 months have become, you know, really exciting as we've seen both that, you know, a very exciting boom and bust cycle that are driving a lot of other macro behaviors. You know, what we've seen over the last 18 months is far greater adoption of the, the standard, what we call the data planes, the, the architectures around snowflake and data bricks and, and Amazon. And what that's created as a result is the emergence of what I would call is the next problem. You know, as you start to solve that category of how >>You, that's it always works too, isn't >>It? Yeah, exactly. Always >>Works that >>This is the wonderful thing about technology is the job security. There's always the next problem to go solve. And that's what we see is, you know, as we we go into cloud, we get that infinite scale, infinite capacity, capacity, infinite flexibility. And you know, with these modern now data platforms, we get that infinite ability to store and process data incredibly quickly with incredible ease. And so what, what do most organizations do? You take a ton of new bodies, like all the people who wanted to do those like really cool things with data you're like, okay, now you can. And so you start throwing a lot more use cases, you start creating a lot more data products, you start doing a lot more things with data. And this is really where that third category starts to emerge, which is you get this data mess, not mesh, but the data mess. >>You get a cluster cluster, you get a cluster exactly where the complexity skyrockets. And as a result that that rapid innovation that, that you are all looking for and, and promised just comes to a screeching halt as you're just, just like trying to swim through molasses. And as a result, this is where that, that new awareness around automation starts really heightened. You know, we, we did a really interesting survey at the start of this year, did it as a blind survey, independent third party surveyed, 500 chief data officers, data scientists, data architects, and asked them a plethora of questions. But one of the questions we asked them was, do you currently or do you intend on investing in data automation to increase your team's productivity? And what was shocking, and I was very surprised by this, okay, what was shocking was only three and a half percent said they do today. Which is really interesting because it really hones in on this notion of automation is beyond what a lot of a think of, you know, tooling and enhancements today, only three and a half percent today had it, but 88.5% said they intend on making data automation investments in the next 12 months. And that stark contrast of how many people have a thing and how many people want that benefit of automation, right? I think it is incredibly critical as we look to 2023 and beyond. >>I mean, this seems like a no-brainer, does it not? I mean, know it is your business, of course you agree with me, but, but of course, of course what brilliant statement. But it is, it seems like, you know, the more you're, you're able to automate certain processes and then free up your resources and your dollars to be spent elsewhere and your, and your human capital, you know, to be invested elsewhere. That just seems to be a layup. I'm really, I'm very surprised by that three and a half percent figure >>I was too. I actually was expecting it to be higher. I was expecting five to 10%. Yeah. As there's other tools in the, the marketplace around ETL tools or orchestration tools that, that some would argue fit in the automation category. And I think the, what, what the market is telling us based on, on that research is that those themselves are, don't qualify as automation. That, that the market has a, a larger vision for automation. Something that is more metadata driven, more AI back, that takes us a greater leap and of leverage for the teams than than what the, the existing capabilities in the industry today can >>Afford. Okay. So if you got this big leap that you can make, but, but, but maybe, you know, should sites be set a little lower, are you, are you in danger of creating too much of an expectation or too much of a false hope? Because you know, I mean sometimes incremental increases are okay. I >>Agree. I I I think the, you know, I think you wanna do a little bit of both. I think you, you want to have a plan for, for reaching for the stars and you gotta be really pragmatic as well. Even inside of a a suni, we actually have a core value, which is build for 10 x plan for a hundred x and so know where you're going, right? But, but solve the problems that are right in front of you today as, as you get to that next scale. And I think the, the really important part for a lot of companies is how do you think about what that trajectory is and be really smart around where you choose to invest as you, one of the, the scenes that we have is last year's innovation is next year's anchor around your neck. And that's because we, we were in this very fortunately, so this really exciting, rapidly moving innovative space, but the thing that was your advantage not too long ago is everybody can move so quickly now becomes commonplace and a year or two later, if you don't jump on whatever that next innovation is that the industry start to standardize on, you're now on hook paying massive debt and, and paying, you know, you thought you had, you know, home mortgage debt and now you're paying the worst of credit card debt trying to pay that down and maintain your velocity. >>It's >>A whole different kind of fomo, right? I'm fair, miss, I'm gonna miss out. What am I missing out on? What the next big thing exactly been missing out >>On that? And so we encourage a lot of folks, you know, as you think about this as it pertains to automation too, is you solve for some of the problems right in front of you, but really make sure that you're, you're designing the right approach that as you stack on, you know, five times, 10 times as many people building data products and, and you, you're, you're your volume and library of, of data weaving throughout your, your business, make sure you're making those right investments. And that's one of the reasons why we do think automation is so important and, and really this, this next generation of automation, which is a, a metadata and AI back to level of automation that can just achieve and accomplish so much more than, than sort of traditional norms. >>Yeah. On that, like, as far as Dex Gen goes, what do you think is gonna be possible that cloud sets the stage for that maybe, you know, not too long ago seem really outta reach, like, like what's gonna give somebody to work on that 88% in there that's gonna make their spin come your way? >>Ah, good question. So I, I think there's a couple fold. I, you know, I think the, right now we see two things happening. You know, we see large movements going to the, the, the dominant data platforms today. And, and you know, frankly, one of the, the biggest challenges we see people having today is just how do you get data in which is insanity to me because that's not even the value extraction, that is the cost center piece of it. Just get data in so you can start to do something with it. And so I think that becomes a, a huge hurdle, but the access to new technologies, the ability to start to unify more of your data and, and in rapid fashion, I think is, is really important. I think as we start to, to invest more in this metadata backed layer that can connect that those notions of how do you ingest your data, how do you transform it, how do you orchestrate it, how do you observe it? One of the really compelling parts of this is metadata does become the new big data itself. And so to do these really advanced things to give these data teams greater levels of automation and leverage, we actually need cloud capabilities to process large volumes of not the data, but the metadata around the data itself to deliver on these really powerful capabilities. And so I think that's why the, this new world that we see of the, the developer platforms for modern data cloud applications actually benefit from being a cloud native application themselves. >>So before you take off, talk about the AWS relationship part of the startup showcase part of the growth program. And we've talked a lot about the cloud, what it's doing for your business, but let's just talk about again, how integral they have been to your success and, and likewise what you're thinking maybe you bring to their table too. Yeah, >>Well we bring a lot to the table. >>Absolutely. I had no doubt about that. >>I mean, honestly, it, working with with AWS has been truly fantastic. Yep. You know, I think, you know, as a, a startup that's really growing and expanding your footprint, having access to the resources in AWS to drive adoption, drive best practices, drive awareness is incredibly impactful. I think, you know, conversely too, the, the value that Ascend provides to the, the AWS ecosystem is tremendous leverage on onboarding and driving faster use cases, faster adoption of all the really great cool, exciting technologies that we get to hear about by bringing more advanced layers of automation to the existing product stack, we can make it easier for more people to build more powerful things faster and safely. Which I think is what most businesses at reinvent really are looking for. >>It's win-win, win-win. Yeah. That's for sure. Sean, thanks for the time. Thank you John. Good job on the t-shirt and keep up the good work. Thank you very much. I appreciate that. Sean Na, joining us here on the AWS startup program, part of their of the Startup Showcase. We are of course on the Cube, I'm John Walls. We're at the Venetian in Las Vegas, and the cube, as you well know, is the leader in high tech coverage.

(Inspiring rock music) >> Hey everyone, I'm Lisa Martin covering Data Citizens 22 brought to you by Collibra. This next conversation is going to 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 citizen. Stan, it's great to have you back on theCUBE. >> Hey Lisa, nice to be here. >> So we're going to 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 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 going to be a data citizen, right? So you need to make sure that these people are aware of it, you need to make sure that these people have the skills and competencies to do with data what is necessary, and that's on all levels, 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 the 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. That's a continuous effort for most organizations because they're always moving somehow, they're hiring new people. And it has to be a continuous effort because we've seen that, on the one hand, organizations continue to be challenged with controlling their data sources and where all the data is flowing right? Which in itself creates lot of risk, but also on the other hand of the equation, you have the benefits, 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 read 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 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, okay, 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 for regulatory reasons. You're trying to bring both of those together. And the ones that get data intelligence, right, are just going to be more successful and more competitive. That's our view and that's what we're seeing out there in the market. >> Absolutely. We know that just generally, Stan, right, The organizations that 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, phones, laptops, what have you. You're not using those IT assets, right? Or you know, you're delivering them throughout the organization, but not enabling your colleagues to actually do something with that asset. Same thing is true with data today, right, if you're not properly using the data asset, and your competitors are, they're going to get more advantage. So as to how you get this done or how you establish this culture there's a few angles to look at, I would say. So one angle is obviously the leadership angle whereby whoever is the boss of data in the organization you typically have multiple bosses there, like a chief Data Officer, sometimes there's multiple, but they may have a different title, right? So I'm just going to 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? >> Lisa: Yes. >> Now, that's one part because then you can clearly see the example of your leadership in the organization, and also the business value, and that's important because 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 go to 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 really have to win them over. And if you have those two combined, and obviously good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like ePlus, then you have the pieces in place to really start upgrading that culture inch by inch, if you will. >> 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 Collibra 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 speakers here, very excited. You know, we have Barb from MIT speaking about data monetization. We have DJ Patil at the last minute on the agenda so really exciting agenda, can't wait to get back out there. But essentially you're right. So over the years at Collibra, we've been doing this now since 2008, so a good 15 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 four people in a garage, if you will, so everybody's wearing all sorts of hat at that time. But over the years I've run pre-sales at Collibra, I've run post sales, partnerships, product, et cetera, and as our company got a little bit biggish, we're now 1,200 something like that, people in the company I believe, systems and processes become a lot more important, right? So we said, you know, Collibra isn't the size of our customers yet, but we're getting there in terms of organization, structure, process systems et cetera. So we said it's really time for us to put our money where our mouth is, and to set up our own data office, which is what we were seeing that all of our customers are doing, and which is what we're seeing that organizations worldwide are doing and Gartner was predicting as well. They said, okay, organizations have an HR unit, they have a finance unit, and over time they'll all have a department, if you will, that is responsible somehow for the data. >> Lisa: Hm. >> So we said, okay, let's try to set an example with Collibra. Let's set up our own data office in such a way 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 sort of good stuff, And in doing all of that, Lisa, exactly as you said, we said, okay, we need to also use our own products and our own practices, right? And from that use, learn how we can make the product better, learn how we can make the practice better and share that learning with all of the markets, of course. And on Monday mornings, we sometimes refer to that as eating our own dog foods, Friday evenings, we refer to that as drinking our own champagne. >> Lisa: I like it. >> So we, we had a (both chuckle) We had the drive 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 follow. This is just the organization that works at our company, but it can serve as an inspiration. So we have pillars, which is data science, The data product builders, if you will 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 pillar where we have those data governance data intelligence stakeholders who help the business as a sort of data partners to the business stakeholders. So that's how we've organized it. And then we started following the Collibra 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 roadmap, and started execution on use case after use case. And a few important ones there are very simple, we see them with all our customers as well, people love talking about the catalog, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in Deagle and privacy, So they have their process registry, and they can see how the data flows. So that's a popular starting place and that turns into a marketplace so that if new analysts and data citizens join Collibra, they immediately have a place to go to to look at what data is out there for me as an analyst or data scientist or whatever, to do my job, right? So they can immediately get access to the data. And another one that we did is around trusted business reporting. We're seeing that, since 2008, you know, self-service BI allowed everyone to make beautiful dashboards, you know, by pie charts. I always, my pet peeve is the pie charts because I love pie, 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? They're reporting on the same thing but the numbers seem different, right? So that's why we have trusted business reporting. So we know if the reports, the dashboard, a data product essentially, is built, we know that all the right steps are being followed, and that whoever is consuming that can be quite confident in the result. >> Lisa: Right, and that confidence is absolutely key. >> 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 chief data officer profession I would say, and again, it always varies, with respect to your organization, but there's a few that we use that might be of interest to you. So remember you have those three pillars, right? And we have metrics across those pillars. So, for example, a pillar on the data engineering side is going to be more related to that uptime, right? 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 signs and the products. Are people using them? Are they getting value from it? Can we calculate that value in a monetary perspective, right? >> Lisa: Yes. >> 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 [Indistinct] People talk about being the owner a data domain for example, like product 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 on and so forth, so these are a set of examples of KPI's. There's a lot more but hopefully those can already inspire the audience. >> Absolutely. So we've, we've talked about the rise of 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 role sort of grow up. I think in 2010 there may have been like, 10 chief data officers or something, Gartner has exact numbers on them. But then they grew, you know, 400's they were like mostly in financial services, but they expanded them to all 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 those competences and need to include them in your strategy. How is that going to evolve for the next couple of years? I wish I had one of those crystal balls, right? But essentially, I think for the next couple of years there's going to 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 officers. So you'll see, over the years that's going to evolve more digital and more data products. So for the next three, five years, my prediction is it's all going to be about data products because it's an immediate link between the data and the dollar essentially. >> Right. >> So that's going to 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 a few years. I think there's going to be a continued challenge for the chief data 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 done will be the ones that do it on the basis of data monetization, right? Connecting value to the data and making that very clear to all the data citizens in the organization, right? >> Right, really creating that value chain. >> 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 are waking up data citizens across the organization and you make everyone in the organization think about data as an essence. >> Absolutely, because there's so much value that can be extracted if organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely going to 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 going to watch this space. Stan, thank you so much for joining me on theCUBE at Data Citizens 22. We appreciate it. >> Thanks for having me over. >> From Data Citizens 22, I'm Lisa Martin you're watching theCUBE, the leader in live tech coverage. (inspiring rock music) >> Okay, this concludes our coverage of Data Citizens 2022 brought to you by Collibra. Remember, all these videos are available on demand at theCUBE.net. And don't forget to check out siliconangle.com for all the news and wikibon.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 theCUBE Your leader in enterprise and emerging tech coverage. We'll see you soon. (inspiring rock music continues)

>>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 citizen. Stan, it's great to have you back on the cube. >>Hey, Lisa, nice to be here. >>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, 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 going to be a data citizen, right? So you need to make sure that these people are aware of it. You need to make sure that these people have the skills and competencies to do with data what is necessary. And that's on all levels, 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 that dashboard to actually make that decision and take that action, right? >>And once you have that why through the organization, that's when you have a good data culture. Now, that's a continuous effort for most organizations because they, they're always moving, somehow there, hiring new people. And it has to be a continuous effort because we've seen that on the one hand, organizations continue to be challenged with controlling 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 benefits. 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 read 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, Okay, I'm doing this, you know, data culture for everyone, wakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this for regulatory reasons. You're trying to bring both of those together and the ones that get data intelligence right, are just going to be more successful and more competitive. That's our view, 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, phones, laptops, what have you, you're not using those IT assets, right? Or you know, you're delivering them through your, throughout the organization, but not enabling your colleagues to actually do something with that asset. Same thing is true with data today, right? If you are not properly using the data assets and your competitors are, they're going to get more advantage. So as to how you get this zone or how you establish this culture, there's a few angles to look at. I would say, Lisa, so one angle is obviously the leadership angle 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 clearly see the example of your leadership in the organization and also the business value. And that's important because 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 culture 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 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 as a data intelligence platform like Colibra, then you have the pieces in place to really start upgrading that culture inch by inch if youll, >>Yes, I like that. The recipe for success. So you are the co-founder of colibra. 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 Collibra 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 speakers here, very excited. You know, we have Barb from MIT speaking about data monetization. We have dig pat at the last minute on the agenda. So really exciting agenda. Can't wait to get back out there. But essentially you're right. So over the years at cbra, we've been doing this now since 2008, so a good 15 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, in a garage if you will. So everybody's wearing all sorts of hat at that time. But over the years I've run, you know, pre-sales at colibra, I've run post-sales partnerships, product, et cetera. And as our company got a little bit biggish for now, 1,200, something like that, people in the company, I believe systems and processes become a lot more important, right? >>So we said, you know, Colibra isn't the size of our customers yet, but we're getting there in terms of organizations, structure, process systems, et cetera. So we said, it's really time for us to put our money where our mouth is and to set up our own data office, which is what we were seeing at all of our customers are doing, and which is what we're seeing that organizations worldwide are doing. And Gartner was predicting us as well. They said, Okay, organizations have an HR unit, they have a finance unit, and over time they'll all have a department, if you will, that is responsible somehow for the data. So we said, Okay, let's try to set a an example at cbra. Let's try to set up our own data office and such way 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 sort of good stuff. And in doing all of that, Lisa, exactly as you said, we said, okay, we need to also use our own product and our own practices, right? And from that use, learn how we can make the product better, learn how we can make the practice better, and share that learning with all of the markets of course. And on, on the Monday mornings, we sometimes refer to that as eating our own dog foods or Friday evenings we refer to that as 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 follow? 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 will, 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 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 builder 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 calibra 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 roadmap and started execution on use case after use case. And a few important ones there are very simple, we see them with our, all our customers as well. People love talking about the catalog, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and legal and privacy. So they have their process registry and they can see how the data flows. So that's a popular starting place. And that turns into a marketplace so that if new analysts and data citizens join cbra, they immediately have a place to go to, to look and see, okay, 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 to the data. And another one that we did is around trusted business reporting. We're seeing that since 2008. You know, self-service BI allowed everyone to make beautiful dashboards, you know, by pie charts. I always, my pet peeve is the pie charts 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 business reporting. So we know if a report, a dashboard, a data product essentially is built, we know that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either right, in that silver or browser Absolutely key. Exactly. Yes. A 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 respect to your organization, but there's a few that we use that might be of interest to you. So remember we have those three 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? Audit is a 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 is a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data science and the products. >>Are people using them? Are they getting value from it? Can we calculate that value in a monetary perspective, right? So that we can to the rest of the business continue to say we're tracking on 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 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 on and so forth. So these are an a 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 of 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 2010 there may have been like 10 chief data officers or something. Gartner has exact numbers on them, but then they grew, you know, 400, they were like mostly in financial services, but they expanded then to all of industries and then to all of the season. 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'd 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 crystal 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 officer. So you'll see over the years that's going to evolve more digital and more data products. So for next three, five years, my, my prediction is it's all going to be about data products because it's an immediate link between the data and, and the dollar essentially, 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 data 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. Yeah. And the ones who get that done will be the ones that do it on the basis of data monetization, right? Connecting value to the data and making that very clear to all the data citizens in the organization, right? Really and in that sense, value chain, 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 essence. >>Absolutely. Because there's so much value that can be extracted if 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 in being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the queue 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.

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, you're watching The Cube, the leader in tech coverage. My name is Dave Vellante here with my cohost Paul Gillin. This is day one of our two day coverage of the MIT CDOIQ conferences. CDO, Chief Data Officer, IQ, information quality. Colin Mahoney is here, he's a good friend and long time CUBE alum. I haven't seen you in awhile, >> I know >> But thank you so much for taking some time, you're like a special guest here >> Thank you, yeah it's great to be here, thank you. >> Yeah, so, this is not, you know, something that you would normally attend. I caught up with you, invited you in. This conference has started as, like back office governance, information quality, kind of wonky stuff, hidden. And then when the big data meme took off, kind of around the time we met. The Chief Data Officer role emerged, the whole Hadoop thing exploded, and then this conference kind of got bigger and bigger and bigger. Still intimate, but very high level, very senior. It's kind of come full circle as we've been saying, you know, information quality still matters. You have been in this data business forever, so I wanted to invite you in just to get your perspectives, we'll talk about what's new with what's going on in your company, but let's go back a little bit. When we first met and even before, you saw it coming, you kind of invested your whole career into data. So, take us back 10 years, I mean it was so different, remember it was Batch, it was Hadoop, but it was cool. There was a lot of cool >> It's still cool. (laughs) projects going on, and it's still cool. But, take a look back. >> Yeah, so it's changed a lot, look, I got into it a while ago, I've always loved data, I had no idea, the explosion and the three V's of data that we've seen over the last decade. But, data's really important, and it's just going to get more and more important. But as I look back I think what's really changed, and even if you just go back a decade I mean, there's an insatiable appetite for data. And that is not slowing down, it hasn't slowed down at all, and I think everybody wants that perfect solution that they can ask any question and get an immediate answers to. We went through the Hadoop boom, I'd argue that we're going through the Hadoop bust, but what people actually want is still the same. You know, they want real answers, accurate answers, they want them quickly, and they want it against all their information and all their data. And I think that Hadoop evolved a lot as well, you know, it started as one thing 10 years ago, with MapReduce and I think in the end what it's really been about is disrupting the storage market. But if you really look at what's disrupting storage right now, public clouds, S3, right? That's the new data league. So there's always a lot of hype cycles, everybody talks about you know, now it's Cloud, everything, for maybe the last 10 years it was a lot of Hadoop, but at the end of the day I think what people want to do with data is still very much the same. And a lot of companies are still struggling with it, hence the role for Chief Data Officers to really figure out how do I monetize data on the one hand and how to I protect that asset on the other hand. >> Well so, and the cool this is, so this conference is not a tech conference, really. And we love tech, we love talking about this, this is why I love having you on. We kind of have a little Vertica thread that I've created here, so Colin essentially, is the current CEO of Vertica, I know that's not your title, you're GM and Senior Vice President, but you're running Vertica. So, Michael Stonebreaker's coming on tomorrow, >> Yeah, excellent. >> Chris Lynch is coming on tomorrow, >> Oh, great, yeah. >> we've got Andy Palmer >> Awesome, yeah. >> coming up as well. >> Pretty cool. (laughs) >> So we have this connection, why is that important? It's because, you know, Vertica is a very cool company and is all about data, and it was all about disrupting, sort of the traditional relational database. It's kind of doing more with data, and if you go back to the roots of Vertica, it was like how do you do things faster? How do you really take advantage of data to really drive new business? And that's kind of what it's all about. And the tech behind it is really cool, we did your conference for many, many years. >> It's coming back by the way. >> Is it? >> Yeah, this March, so March 30th. >> Oh, wow, mark that down. >> At Boston, at the new Encore Hotel. >> Well we better have theCUBE there, bro. (laughs) >> Yeah, that's great. And yeah, you've done that conference >> Yep. >> haven't you before? So very cool customers, kind of leading edge, so I want to get to some of that, but let's talk the disruption for a minute. So you guys started with the whole architecture, MPP and so forth. And you talked about Cloud, Cloud really disrupted Hadoop. What are some of the other technology disruptions that you're seeing in the market space? >> I think, I mean, you know, it's hard not to talk about AI machine learning, and what one means versus the other, who knows right? But I think one thing that is definitely happening is people are leveraging the volumes of data and they're trying to use all the processing power and storage power that we have to do things that humans either are too expensive to do or simply can't do at the same speed and scale. And so, I think we're going through a renaissance where a lot more is being automated, certainly on the Vertica roadmap, and our path has always been initially to get the data in and then we want the platform to do a lot more for our customers, lots more analytics, lots more machine-learning in the platform. So that's definitely been a lot of the buzz around, but what's really funny is when you talk to a lot of customers they're still struggling with just some basic stuff. Forget about the predictive thing, first you've got to get to what happened in the past. Let's give accurate reporting on what's actually happening. The other big thing I think as a disruption is, I think IOT, for all the hype that it's getting it's very real. And every device is kicking off lots of information, the feedback loop of AB testing or quality testing for predictive maintenance, it's happening almost instantly. And so you're getting massive amounts of new data coming in, it's all this machine sensor type data, you got to figure out what it means really quick, and then you actually have to do something and act on it within seconds. And that's a whole new area for so many people. It's not their traditional enterprise data network warehouse and you know, back to you comment on Stonebreaker, he got a lot of this right from the beginning, you know, and I think he looked at the architectures, he took a lot of the best in class designs, we didn't necessarily invent everything, but we put a lot of that together. And then I think the other you've got to do is constantly re-invent your platform. We came out with our Eon Mode to run cloud native, we just got rated the best cloud data warehouse from a net promoter score rating perspective, so, but we got to keep going you know, we got to keep re-inventing ourselves, but leverage everything that we've done in the past as well. >> So one of the things that you said, which is kind of relevant for here, Paul, is you're still seeing a real data quality issue that customers are wrestling with, and that's a big theme here, isn't it? >> Absolutely, and the, what goes around comes around, as Dave said earlier, we're still talking about information quality 13 years after this conference began. Have the tools to improve quality improved all that much? >> I think the tools have improved, I think that's another area where machine learning, if you look at Tamr, and I know you're going to have Andy here tomorrow, they're leveraging a lot of the augmented things you can do with the processing to make it better. But I think one thing that makes the problem worse now, is it's gotten really easy to pour data in. It's gotten really easy to store data without having to have the right structure, the right quality, you know, 10 years ago, 20 years ago, everything was perfect before it got into the platform. Right, everything was, there was quality, everything was there. What's been happening over the last decade is you're pumping data into these systems, nobody knows if it's redundant data, nobody knows if the quality's any good, and the amount of data is massive. >> And it's cheap to store >> Very cheap to store. >> So people keep pumping it in. >> But I think that creates a lot of issues when it comes to data quality. So, I do think the technology's gotten better, I think there's a lot of companies that are doing a great job with it, but I think the challenge has definitely upped. >> So, go ahead. >> I'm sorry. You mentioned earlier that we're seeing the death of Hadoop, but I'd like you to elaborate on that becuase (Dave laughs) Hadoop actually came up this morning in the keynote, it's part of what GlaxoSmithKline did. Came up in a conversation I had with the CEO of Experian last week, I mean, it's still out there, why do you think it's in decline? >> I think, I mean first of all if you look at the Hadoop vendors that are out there, they've all been struggling. I mean some of them are shutting down, two of them have merged and they've got killed lately. I think there are some very successful implementations of Hadoop. I think Hadoop as a storage environment is wonderful, I think you can process a lot of data on Hadoop, but the problem with Hadoop is it became the panacea that was going to solve all things data. It was going to be the database, it was going to be the data warehouse, it was going to do everything. >> That's usually the kiss of death, isn't it? >> It's the kiss of death. And it, you know, the killer app on Hadoop, ironically, became SQL. I mean, SQL's the killer app on Hadoop. If you want to SQL engine, you don't need Hadoop. But what we did was, in the beginning Mike sort of made fun of it, Stonebreaker, and joked a lot about he's heard of MapReduce, it's called Group By, (Dave laughs) and that created a lot of tension between the early Vertica and Hadoop. I think, in the end, we embraced it. We sit next to Hadoop, we sit on top of Hadoop, we sit behind it, we sit in front of it, it's there. But I think what the reality check of the industry has been, certainly by the business folks in these companies is it has not fulfilled all the promises, it has not fulfilled a fraction on the promises that they bet on, and so they need to figure those things out. So I don't think it's going to go away completely, but I think its best success has been disrupting the storage market, and I think there's some much larger disruptions of technologies that frankly are better than HTFS to do that. >> And the Cloud was a gamechanger >> And a lot of them are in the cloud. >> Which is ironic, 'cause you know, cloud era, (Colin laughs) they didn't really have a cloud strategy, neither did Hortonworks, neither did MapR and, it just so happened Amazon had one, Google had one, and Microsoft has one, so, it's just convenient to-- >> Well, how is that affecting your business? We've seen this massive migration to the cloud (mumbles) >> It's actually been great for us, so one of the things about Vertica is we run everywhere, and we made a decision a while ago, we had our own data warehouse as a service offering. It might have been ahead of its time, never really took off, what we did instead is we pivoted and we say "you know what? "We're going to invest in that experience "so it's a SaaS-like experience, "but we're going to let our customers "have full control over the cloud. "And if they want to go to Amazon they can, "if they want to go to Google they can, "if they want to go to Azure they can." And we really invested in that and that experience. We're up on the Amazon marketplace, we have lots of customers running up on Amazon Cloud as well as Google and Azure now, and then about two years ago we went down and did this endeavor to completely re-architect our product so that we could separate compute and storage so that our customers could actually take advantage of the cloud economics as well. That's been huge for us, >> So you scale independent-- >> Scale independently, cloud native, add compute, take away compute, and for our existing customers, they're loving the hybrid aspect, they love that they can still run on Premise, they love that they can run up on a public cloud, they love that they can run in both places. So we will continue to invest a lot in that. And it is really, really important, and frankly, I think cloud has helped Vertica a lot, because being able to provision hardware quickly, being able to tie in to these public clouds, into our customers' accounts, give them control, has been great and we're going to continue on that path. >> Because Vertica's an ISV, I mean you're a software company. >> We're a software company. >> I know you were a part of HP for a while, and HP wanted to mash that in and run it on it's hardware, but software runs great in the cloud. And then to you it's another hardware platform. >> It's another hardware platform, exactly. >> So give us the update on Micro Focus, Micro Focus acquired Vertica as part of the HPE software business, how many years ago now? Two years ago? >> Less than two years ago. >> Okay, so how's that going, >> It's going great. >> Give us the update there. >> Yeah, so first of all it is great, HPE and HP were wonderful to Vertica, but it's great being part of a software company. Micro Focus is a software company. And more than just a software company it's a company that has a lot of experience bridging the old and the new. Leveraging all of the investments that you've made but also thinking about cloud and all these other things that are coming down the pike. I think for Vertica it's been really great because, as you've seen Vertica has gotten its identity back again. And that's something that Micro Focus is very good at. You can look at what Micro Focus did with SUSE, the Linux company, which actually you know, now just recently spun out of Micro Focus but, letting organizations like Vertica that have this culture, have this product, have this passion, really focus on our market and our customers and doing the right thing by them has been just really great for us and operating as a software company. The other nice thing is that we do integrate with a lot of other products, some of which came from the HPE side, some of which came from Micro Focus, security products is an example. The other really nice thing is we've been doing this insource thing at Micro Focus where we open up our source code to some of the other teams in Micro Focus and they've been contributing now in amazing ways to the product. In ways that we would just never be able to scale, but with 4,000 engineers strong in Micro Focus, we've got a much larger development organization that can actually contribute to the things that Vertica needs to do. And as we go into the cloud and as we do a lot more operational aspects, the experience that these teams have has been incredible, and security's another great example there. So overall it's been great, we've had four different owners of Vertica, our job is to continue what we do on the innovation side in the culture, but so far Micro Focus has been terrific. >> Well, I'd like to say, you're kind of getting that mojo back, because you guys as an independent company were doing your own thing, and then you did for a while inside of HP, >> We did. >> And that obviously changed, 'cause they wanted more integration, but, and Micro Focus, they know what they're doing, they know how to do acquisitions, they've been very successful. >> It's a very well run company, operationally. >> The SUSE piece was really interesting, spinning that out, because now RHEL is part of IBM, so now you've got SUSE as the lone independent. >> Yeah. >> Yeah. >> But I want to ask you, go back to a technology question, is NoSQL the next Hadoop? Are these databases, it seems to be that the hot fad now is NoSQL, it can do anything. Is the promise overblown? >> I think, I mean NoSQL has been out almost as long as Hadoop, and I, we always say not only SQL, right? Mike's said this from day one, best tool for the job. Nothing is going to do every job well, so I think that there are, whether it's key value stores or other types of NoSQL engines, document DB's, now you have some of these DB's that are running on different chips, >> Graph, yeah. >> there's always, yeah, graph DBs, there's always going to be specialty things. I think one of the things about our analytic platform is we can do, time series is a great example. Vertica's a great time series database. We can compete with specialized time series databases. But we also offer a lot of, the other things that you can do with Vertica that you wouldn't be able to do on a database like that. So, I always think there's going to be specialty products, I also think some of these can do a lot more workloads than you might think, but I don't see as much around the NoSQL movement as say I did a few years ago. >> But so, and you mentioned the cloud before as kind of, your position on it I think is a tailwind, not to put words in your mouth, >> Yeah, yeah, it's a great tailwind. >> You're in the Amazon marketplace, I mean they have products that are competitive, right? >> They do, they do. >> But, so how are you differentiating there? >> I think the way we differentiate, whether it's Redshift from Amazon, or BigQuery from Google, or even what Azure DB does is, first of all, Vertica, I think from, feature functionality and performance standpoint is ahead. Number one, I think the second thing, and we hear this from a lot of customers, especially at the C-level is they don't want to be locked into these full stacks of the clouds. Having the ability to take a product and run it across multiple clouds is a big thing, because the stack lock-in now, the full stack lock-in of these clouds is scary. It's really easy to develop in their ecosystems but you get very locked into them, and I think a lot of people are concerned about that. So that works really well for Vertica, but I think at the end of the day it's just, it's the robustness of the product, we continue to innovate, when you look at separating compute and storage, believe it or not, a lot of these cloud-native databases don't do that. And so we can actually leverage a lot of the cloud hardware better than the native cloud databases do themselves. So, like I said, we have to keep going, those guys aren't going to stop, and we actually have great relationships with those companies, we work really well with the clouds, they seem to care just as much about their cloud ecosystem as their own database products, and so I think that's going to continue as well. >> Well, Colin, congratulations on all the success >> Yeah, thank you, yeah. >> It's awesome to see you again and really appreciate you coming to >> Oh thank you, it's great, I appreciate the invite, >> MIT. >> it's great to be here. >> All right, keep it right there everybody, Paul and I will be back with our next guest from MIT, you're watching theCUBE. (electronic jingle)

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officers Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. I'm Dave Vellante and you're watching theCUBE, the leader in live tech coverage. This is the end of the day panel at the IBM Chief Data Officer Summit. This is the 10th CDO event that IBM has held and we love to to gather these panels. This is a data all-star panel and I've recruited Seth Dobrin who is the CDO of the analytics group at IBM. Seth, thank you for agreeing to chip in and be my co-host in this segment. >> Yeah, thanks Dave. Like I said before we started, I don't know if this is a promotion or a demotion. (Dave laughing) >> We'll let you know after the segment. So, the data all-star panel and the data all-star awards that you guys are giving out a little later in the event here, what's that all about? >> Yeah so this is our 10th CDU Summit. So two a year, so we've been doing this for 5 years. The data all-stars are those people that have been to four at least of the ten. And so these are five of the 16 people that got the award. And so thank you all for participating and I attended these like I said earlier, before I joined IBM they were immensely valuable to me and I was glad to see 16 other people that think it's valuable too. >> That is awesome. Thank you guys for coming on. So, here's the format. I'm going to introduce each of you individually and then ask you to talk about your role in your organization. What role you play, how you're using data, however you want to frame that. And the first question I want to ask is, what's a good day in the life of a data person? Or if you want to answer what's a bad day, that's fine too, you choose. So let's start with Lucia Mendoza-Ronquillo. Welcome, she's the Senior Vice President and the Head of BI and Data Governance at Wells Fargo. You told us that you work within the line of business group, right? So introduce your role and what's a good day for a data person? >> Okay, so my role basically is again business intelligence so I support what's called cards and retail services within Wells Fargo. And I also am responsible for data governance within the business. We roll up into what's called a data governance enterprise. So we comply with all the enterprise policies and my role is to make sure our line of business complies with data governance policies for enterprise. >> Okay, good day? What's a good day for you? >> A good day for me is really when I don't get a call that the regulators are knocking on our doors. (group laughs) Asking for additional reports or have questions on the data and so that would be a good day. >> Yeah, especially in your business. Okay, great. Parag Shrivastava is the Director of Data Architecture at McKesson, welcome. Thanks so much for coming on. So we got a healthcare, couple of healthcare examples here. But, Parag, introduce yourself, your role, and then what's a good day or if you want to choose a bad day, be fun the mix that up. >> Yeah, sounds good. Yeah, so mainly I'm responsible for the leader strategy and architecture at McKesson. What that means is McKesson has a lot of data around the pharmaceutical supply chain, around one-third of the world's pharmaceutical supply chain, clinical data, also around pharmacy automation data, and we want to leverage it for the better engagement of the patients and better engagement of our customers. And my team, which includes the data product owners, and data architects, we are all responsible for looking at the data holistically and creating the data foundation layer. So I lead the team across North America. So that's my current role. And going back to the question around what's a good day, I think I would say the good day, I'll start at the good day. Is really looking at when the data improves the business. And the first thing that comes to my mind is sort of like an example, of McKesson did an acquisition of an eight billion dollar pharmaceutical company in Europe and we were creating the synergy solution which was based around the analytics and data. And actually IBM was one of the partners in implementing that solution. When the solution got really implemented, I mean that was a big deal for me to see that all the effort that we did in plumbing the data, making sure doing some analytics, is really helping improve the business. I think that is really a good day I would say. I mean I wouldn't say a bad day is such, there are challenges, constant challenges, but I think one of the top priorities that we are having right now is to deal with the demand. As we look at the demand around the data, the role of data has got multiple facets to it now. For example, some of the very foundational, evidentiary, and compliance type of needs as you just talked about and then also profitability and the cost avoidance and those kind of aspects. So how to balance between that demand is the other aspect. >> All right good. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. Carl, tell us a little bit about Zuora. People might not be as familiar with how you guys do software for billing et cetera. Tell us about your role and what's a good day for a data scientist? >> Okay, sure, I'll start by a little bit about Zuora. Zuora is a subscription management platform. So any company who wants to offer a product or service as subscription and you don't want to build your billing and subscription management, revenue recognition, from scratch, you can use a product like ours. I say it lets anyone build a telco with a complicated plan, with tiers and stuff like that. I don't know if that's a good thing or not. You guys'll have to make up your own mind. My role is an interesting one. It's split, so I said I'm a chief data scientist and we work about 50% on product features based on data science. Things like churn prediction, or predictive payment retries are product areas where we offer AI-based solutions. And then but because Zuora is a subscription platform, we have an amazing set of data on the actual performance of companies using our product. So a really interesting part of my role has been leading what we call the subscription economy index and subscription economy benchmarks which are reports around best practices for subscription companies. And it's all based off this amazing dataset created from an anonymized data of our customers. So that's a really exciting part of my role. And for me, maybe this speaks to our level of data governance, I might be able to get some tips from some of my co-panelists, but for me a good day is when all the data for me and everyone on my team is where we left it the night before. And no schema changes, no data, you know records that you were depending on finding removed >> Pipeline failures. >> Yeah pipeline failures. And on a bad day is a schema change, some crucial data just went missing and someone on my team is like, "The code's broken." >> And everybody's stressed >> Yeah, so those are bad days. But, data governance issues maybe. >> Great, okay thank you. Jung Park is the COO of Latitude Food Allergy Care. Jung welcome. >> Yeah hi, thanks for having me and the rest of us here. So, I guess my role I like to put it as I'm really the support team. I'm part of the support team really for the medical practice so, Latitude Food Allergy Care is a specialty practice that treats patients with food allergies. So, I don't know if any of you guys have food allergies or maybe have friends, kids, who have food allergies, but, food allergies unfortunately have become a lot more prevalent. And what we've been able to do is take research and data really from clinical trials and other research institutions and really use that from the clinical trial setting, back to the clinical care model so that we can now treat patients who have food allergies by using a process called oral immunotherapy. It's fascinating and this is really personal to me because my son as food allergies and he's been to the ER four times. >> Wow. >> And one of the scariest events was when he went to an ER out of the country and as a parent, you know you prepare your child right? With the food, he takes the food. He was 13 years old and you had the chaperones, everyone all set up, but you get this call because accidentally he ate some peanut, right. And so I saw this unfold and it scared me so much that this is something I believe we just have to get people treated. So this process allows people to really eat a little bit of the food at a time and then you eat the food at the clinic and then you go home and eat it. Then you come back two weeks later and then you eat a little bit more until your body desensitizes. >> So you build up that immunity >> Exactly. >> and then you watch the data obviously. >> Yeah. So what's a good day for me? When our patients are done for the day and they have a smile on their face because they were able to progress to that next level. >> Now do you have a chief data officer or are you the de facto CFO? >> I'm the de facto. So, my career has been pretty varied. So I've been essentially chief data officer, CIO, at companies small and big. And what's unique about I guess in this role is that I'm able to really think about the data holistically through every component of the practice. So I like to think of it as a patient journey and I'm sure you guys all think of it similarly when you talk about your customers, but from a patient's perspective, before they even come in, you have to make sure the data behind the science of whatever you're treating is proper, right? Once that's there, then you have to have the acquisition part. How do you actually work with the community to make sure people are aware of really the services that you're providing? And when they're with you, how do you engage them? How do you make sure that they are compliant with the process? So in healthcare especially, oftentimes patients don't actually succeed all the way through because they don't continue all the way through. So it's that compliance. And then finally, it's really long-term care. And when you get the long-term care, you know that the patient that you've treated is able to really continue on six months, a year from now, and be able to eat the food. >> Great, thank you for that description. Awesome mission. Rolland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. >> Yeah, sure. So Clover is a startup Medicare Advantage plan. So we provide Medicare, private Medicare to seniors. And what we do is we're because of the way we run our health plan, we're able to really lower a lot of the copay costs and protect seniors against out of pocket. If you're on regular Medicare, you get cancer, you have some horrible accident, your out of pocket is infinite potentially. Whereas with Medicare Advantage Plan it's limited to like five, $6,000 and you're always protected. One of the things I'm excited about being at Clover is our ability to really look at how can we bring the value of data analytics to healthcare? Something I've been in this industry for close to 20 years at this point and there's a lot of waste in healthcare. And there's also a lot of very poor application of preventive measures to the right populations. So one of the things that I'm excited about is that with today's models, if you're able to better identify with precision, the right patients to intervene with, then you fundamentally transform the economics of what can be done. Like if you had to pa $1,000 to intervene, but you were only 20% of the chance right, that's very expensive for each success. But, now if your model is 60, 70% right, then now it opens up a whole new world of what you can do. And that's what excites me. In terms of my best day? I'll give you two different angles. One as an MBA, one of my best days was, client calls me up, says, "Hey Rolland, you know, "your analytics brought us over $100 million "in new revenue last year." and I was like, cha-ching! Excellent! >> Which is my half? >> Yeah right. And then on the data geek side the best day was really, run a model, you train a model, you get ridiculous AUC score, so area under the curve, and then you expect that to just disintegrate as you go into validation testing and actual live production. But the 98 AUC score held up through production. And it's like holy cow, the model actually works! And literally we could cut out half of the workload because of how good that model was. >> Great, excellent, thank you. Seth, anything you'd add to the good day, bad day, as a CDO? >> So for me, well as a CDO or as CDO at IBM? 'Cause at IBM I spend most of my time traveling. So a good day is a day I'm home. >> Yeah, when you're not in an (group laughing) aluminum tube. >> Yeah. Hurdling through space (laughs). No, but a good day is when a GDPR compliance just happened, a good day for me was May 20th of last year when IBM was done and we were, or as done as we needed to be for GDPR so that was a good day for me last year. This year is really a good day is when we start implementing some new models to help IBM become a more effective company and increase our bottom line or increase our margins. >> Great, all right so I got a lot of questions as you know and so I want to give you a chance to jump in. >> All right. >> But, I can get it started or have you got something? >> I'll go ahead and get started. So this is a the 10th CDO Summit. So five years. I know personally I've had three jobs at two different companies. So over the course of the last five years, how many jobs, how many companies? Lucia? >> One job with one company. >> Oh my gosh you're boring. (group laughing) >> No, but actually, because I support basically the head of the business, we go into various areas. So, we're not just from an analytics perspective and business intelligence perspective and of course data governance, right? It's been a real journey. I mean there's a lot of work to be done. A lot of work has been accomplished and constantly improving the business, which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance to really helping us respond to regulators (laughs). So it's a variety of areas I've had to be involved in. >> So one company, 50 jobs. >> Exactly. So right now I wear different hats depending on the day. So that's really what's happening. >> So it's a good question, have you guys been jumping around? Sure, I mean I think of same company, one company, but two jobs. And I think those two jobs have two different layers. When I started at McKesson I was a solution leader or solution director for business intelligence and I think that's how I started. And over the five years I've seen the complete shift towards machine learning and my new role is actually focused around machine learning and AI. That's why we created this layer, so our own data product owners who understand the data science side of things and the ongoing and business architecture. So, same company but has seen a very different shift of data over the last five years. >> Anybody else? >> Sure, I'll say two companies. I'm going on four years at Zuora. I was at a different company for a year before that, although it was kind of the same job, first at the first company, and then at Zuora I was really focused on subscriber analytics and churn for my first couple a years. And then actually I kind of got a new job at Zuora by becoming the subscription economy expert. I become like an economist, even though I don't honestly have a background. My PhD's in biology, but now I'm a subscription economy guru. And a book author, I'm writing a book about my experiences in the area. >> Awesome. That's great. >> All right, I'll give a bit of a riddle. Four, how do you have four jobs, five companies? >> In five years. >> In five years. (group laughing) >> Through a series of acquisition, acquisition, acquisition, acquisition. Exactly, so yeah, I have to really, really count on that one (laughs). >> I've been with three companies over the past five years and I would say I've had seven jobs. But what's interesting is I think it kind of mirrors and kind of mimics what's been going on in the data world. So I started my career in data analytics and business intelligence. But then along with that I had the fortune to work with the IT team. So the IT came under me. And then after that, the opportunity came about in which I was presented to work with compliance. So I became a compliance officer. So in healthcare, it's very interesting because these things are tied together. When you look about the data, and then the IT, and then the regulations as it relates to healthcare, you have to have the proper compliance, both internal compliance, as well as external regulatory compliance. And then from there I became CIO and then ultimately the chief operating officer. But what's interesting is as I go through this it's all still the same common themes. It's how do you use the data? And if anything it just gets to a level in which you become closer with the business and that is the most important part. If you stand alone as a data scientist, or a data analyst, or the data officer, and you don't incorporate the business, you alienate the folks. There's a math I like to do. It's different from your basic math, right? I believe one plus one is equal to three because when you get the data and the business together, you create that synergy and then that's where the value is created. >> Yeah, I mean if you think about it, data's the only commodity that increases value when you use it correctly. >> Yeah. >> Yeah so then that kind of leads to a question that I had. There's this mantra, the more data the better. Or is it more of an Einstein derivative? Collect as much data as possible but not too much. What are your thoughts? Is more data better? >> I'll take it. So, I would say the curve has shifted over the years. Before it used to be data was the bottleneck. But now especially over the last five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case. The definition of what exactly we're going to apply to, how we're going to apply it to. Oftentimes once you have that clear, you can go get the data. And then in the case where there is not data, like in Mechanical Turk, you can all set up experiments, gather data, the cost of that is now so cheap to experiment that I think the bottleneck's really around the business understanding the use case. >> Mm-hmm. >> Mm-hmm. >> And I think the wave that we are seeing, I'm seeing this as there are, in some cases, more data is good, in some cases more data is not good. And I think I'll start it where it is not good. I think where quality is more required is the area where more data is not good. For example like regulation and compliance. So for example in McKesson's case, we have to report on opioid compliance for different states. How much opioid drugs we are giving to states and making sure we have very, very tight reporting and compliance regulations. There, highest quality of data is important. In our data organization, we have very, very dedicated focus around maintaining that quality. So, quality is most important, quantity is not if you will, in that case. Having the right data. Now on the other side of things, where we are doing some kind of exploratory analysis. Like what could be a right category management for our stores? Or where the product pricing could be the right ones. Product has around 140 attributes. We would like to look at all of them and see what patterns are we finding in our models. So there you could say more data is good. >> Well you could definitely see a lot of cases. But certainly in financial services and a lot of healthcare, particularly in pharmaceutical where you don't want work in process hanging around. >> Yeah. >> Some lawyer could find a smoking gun and say, "Ooh see." And then if that data doesn't get deleted. So, let's see, I would imagine it's a challenge in your business, I've heard people say, "Oh keep all the, now we can keep all the data, "it's so inexpensive to store." But that's not necessarily such a good thing is it? >> Well, we're required to store data. >> For N number of years, right? >> Yeah, N number of years. But, sometimes they go beyond those number of years when there's a legal requirements to comply or to answer questions. So we do keep more than, >> Like a legal hold for example. >> Yeah. So we keep more than seven years for example and seven years is the regulatory requirement. But in the case of more data, I'm a data junkie, so I like more data (laughs). Whenever I'm asked, "Is the data available?" I always say, "Give me time I'll find it for you." so that's really how we operate because again, we're the go-to team, we need to be able to respond to regulators to the business and make sure we understand the data. So that's the other key. I mean more data, but make sure you understand what that means. >> But has that perspective changed? Maybe go back 10 years, maybe 15 years ago, when you didn't have the tooling to be able to say, "Give me more data." "I'll get you the answer." Maybe, "Give me more data." "I'll get you the answer in three years." Whereas today, you're able to, >> I'm going to go get it off the backup tapes (laughs). >> (laughs) Yeah, right, exactly. (group laughing) >> That's fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years. So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you are able to navigate, you can get to the data >> Yeah. >> within the required timeline. So I have, astonished you have the technology, team behind you. Jung, you want to add something? >> Yeah, so that's an interesting question. So, clearly in healthcare, there is a lot of data and as I've kind of come closer to the business, I also realize that there's a fine line between collecting the data and actually asking our folks, our clinicians, to generate the data. Because if you are focused only on generating data, the electronic medical records systems for example. There's burnout, you don't want the clinicians to be working to make sure you capture every element because if you do so, yes on the back end you have all kinds of great data, but on the other side, on the business side, it may not be necessarily a productive thing. And so we have to make a fine line judgment as to the data that's generated and who's generating that data and then ultimately how you end up using it. >> And I think there's a bit of a paradox here too, right? The geneticist in me says, "Don't ever throw anything away." >> Right. >> Right? I want to keep everything. But, the most interesting insights often come from small data which are a subset of that larger, keep everything inclination that we as data geeks have. I think also, as we're moving in to kind of the next phase of AI when you can start doing really, really doing things like transfer learning. That small data becomes even more valuable because you can take a model trained on one thing or a different domain and move it over to yours to have a starting point where you don't need as much data to get the insight. So, I think in my perspective, the answer is yes. >> Yeah (laughs). >> Okay, go. >> I'll go with that just to run with that question. I think it's a little bit of both 'cause people touched on different definitions of more data. In general, more observations can never hurt you. But, more features, or more types of things associated with those observations actually can if you bring in irrelevant stuff. So going back to Rolland's answer, the first thing that's good is like a good mental model. My PhD is actually in physical science, so I think about physical science, where you actually have a theory of how the thing works and you collect data around that theory. I think the approach of just, oh let's put in 2,000 features and see what sticks, you know you're leaving yourself open to all kinds of problems. >> That's why data science is not democratized, >> Yeah (laughing). >> because (laughing). >> Right, but first Carl, in your world, you don't have to guess anymore right, 'cause you have real data. >> Well yeah, of course, we have real data, but the collection, I mean for example, I've worked on a lot of customer churn problems. It's very easy to predict customer churn if you capture data that pertains to the value customers are receiving. If you don't capture that data, then you'll never predict churn by counting how many times they login or more crude measures of engagement. >> Right. >> All right guys, we got to go. The keynotes are spilling out. Seth thank you so much. >> That's it? >> Folks, thank you. I know, I'd love to carry on, right? >> Yeah. >> It goes fast. >> Great. >> Yeah. >> Guys, great, great content. >> Yeah, thanks. And congratulations on participating and being data all-stars. >> We'd love to do this again sometime. All right and thank you for watching everybody, it's a wrap from IBM CDOs, Dave Vellante from theCUBE. We'll see you next time. (light music)

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> Hi everybody, welcome back to theCUBE's coverage of the IBM Chief Data Officer Event. We're here at Fisherman's Wharf in San Francisco at the Centric Hyatt Hotel. This is the 10th anniversary of IBM's Chief Data Officer Summits. In the recent years, anyway, they do one in San Francisco and one in Boston each year, and theCUBE has covered a number of them. I think this is our eighth CDO conference. I'm Dave Vellante, and theCUBE, we like to go out, especially to events like this that are intimate, there's about 140 chief data officers here. We've had the chief data officer from AstraZeneca on, even though he doesn't take that title. We've got a panel coming up later on in the day. And I want to talk about the evolution of that role. The chief data officer emerged out of kind of a wonky, back-office role. It was all about 10, 12 years ago, data quality, master data management, governance, compliance. And as the whole big data meme came into focus and people were realizing that data is the new source of competitive advantage, that data was going to be a source of innovation, what happened was that role emerged, that CDO, chief data officer role, emerged out of the back office and came right to the front and center. And the chief data officer really started to better understand and help companies understand how to monetize the data. Now monetization of data could mean more revenue. It could mean cutting costs. It could mean lowering risk. It could mean, in a hospital situation, saving lives, sort of broad definition of monetization. But it was really understanding how data contributed to value, and then finding ways to operationalize that to speed up time to value, to lower cost, to lower risk. And that required a lot of things. It required new skill sets, new training. It required a partnership with the lines of business. It required new technologies like artificial intelligence, which have just only recently come into a point where it's gone mainstream. Of course, when I started in the business several years ago, AI was the hot topic, but you didn't have the compute power. You didn't have the data, you didn't have the cloud. So we see the new innovation engine, not as Moore's Law, the doubling of transistors every 18 months, doubling of performance. Really no, we see the new innovation cocktail as data as the substrate, applying machine intelligence to that data, and then scaling it with the cloud. And through that cloud model, being able to attract startups and innovation. I come back to the chief data officer here, and IBM Chief Data Officer Summit, that's really where the chief data officer comes in. Now, the role in the organization is fuzzy. If you ask people what's a chief data officer, you'll get 20 different answers. Many answers are focused on compliance, particularly in what emerged, again, in those regulated industries: financial service, healthcare, and government. Those are the first to have chief data officers. But now CDOs have gone mainstream. So what we're seeing here from IBM is the broadening of that role and that definition and those responsibilities. Confusing things is the chief digital officer or the chief analytics officer. Those are roles that have also emerged, so there's a lot of overlap and a lot of fuzziness. To whom should the chief data officer report? Many say it should not be the CIO. Many say they should be peers. Many say the CIO's responsibility is similar to the chief data officer, getting value out of data, although I would argue that's never really been the case. The role of the CIO has largely been to make sure that the technology infrastructure works and that applications are delivered with high availability, with great performance, and are able to be developed in an agile manner. That's sort of a more recent sort of phenomenon that's come forth. And the chief digital officer is really around the company's face. What does that company's brand look like? What does that company's go-to-market look like? What does the customer see? Whereas the chief data officer's really been around the data strategy, what the sort of framework should be around compliance and governance, and, again, monetization. Not that they're responsible for the monetization, but they responsible for setting that framework and then communicating it across the company, accelerating the skill sets and the training of existing staff and complementing with new staff and really driving that framework throughout the organization in partnership with the chief digital officer, the chief analytics officer, and the chief information officer. That's how I see it anyway. Martin Schroeder, the senior vice president of IBM, came on today with Inderpal Bhandari, who is the chief data officer of IBM, the global chief data officer. Martin Schroeder used to be the CFO at IBM. He talked a lot, kind of borrowing from Ginni Rometty's themes in previous conferences, chapter one of digital which he called random acts of digital, and chapter two is how to take this mainstream. IBM makes a big deal out of the fact that it doesn't appropriate your data, particularly your personal data, to sell ads. IBM's obviously in the B2B business, so that's IBM's little back-ended shot at Google and Facebook and Amazon who obviously appropriate our data to sell ads or sell goods. IBM doesn't do that. I'm interested in IBM's opinion on big tech. There's a lot of conversations now. Elizabeth Warren wants to break up big tech. IBM was under the watchful eye of the DOJ 25 years ago, 30 years ago. IBM essentially had a monopoly in the business, and the DOJ wanted to make sure that IBM wasn't using that monopoly to hurt consumers and competitors. Now what IBM did, the DOJ ruled that IBM had to separate its applications business, actually couldn't be in the applications business. Another ruling was that they had to publish the interfaces to IBM mainframes so that competitors could actually build plug-compatible products. That was the world back then. It was all about peripherals plugging into mainframes and sort of applications being developed. So the DOJ took away IBM's power. Fast forward 30 years, now we're hearing Google, Amazon, and Facebook coming under fire from politicians. Should they break up those companies? Now those companies are probably the three leaders in AI. IBM might debate that. I think generally, at theCUBE and SiliconANGLE, we believe that those three companies are leading the charge in AI, along with China Inc: Alibaba, Tencent, Baidu, et cetera, and the Chinese government. So here's the question. What would happen if you broke up big tech? I would surmise that if you break up big tech, those little techs that you break up, Amazon Web Services, WhatsApp, Instagram, those little techs would get bigger. Now, however, the government is implying that it wants to break those up because those entities have access to our data. Google's got access to all the search data. If you start splitting them up, that'll make it harder for them to leverage that data. I would argue those small techs would get bigger, number one. Number two, I would argue if you're worried about China, which clearly you're seeing President Trump is worried about China, placing tariffs on China, playing hardball with China, which is not necessarily a bad thing. In fact, I think it's a good thing because China has been accused, and we all know, of taking IP, stealing IP essentially, and really not putting in those IP protections. So, okay, playing hardball to try to get a quid pro quo on IP protections is a good thing. Not good for trade long term. I'd like to see those trade barriers go away, but if it's a negotiation tactic, okay. I can live with it. However, going after the three AI leaders, Amazon, Facebook, and Google, and trying to take them down or break them up, actually, if you're a nationalist, could be a bad thing. Why would you want to handcuff the AI leaders? Third point is unless they're breaking the law. So I think that should be the decision point. Are those three companies, and others, using monopoly power to thwart competition? I would argue that Microsoft actually did use its monopoly power back in the '80s and '90s, in particular in the '90s, when it put Netscape out of business, it put Lotus out of business, it put WordPerfect out of business, it put Novell out of the business. Now, maybe those are strong words, but in fact, Microsoft's bundling, its pricing practices, caught those companies off guard. Remember, Jim Barksdale, the CEO of Netscape, said we don't need the browser. He was wrong. Microsoft killed Netscape by bundling Internet Explorer into its operating system. So the DOJ stepped in, some would argue too late, and put handcuffs on Microsoft so they couldn't use that monopoly power. And I would argue that you saw from that two things. One, granted, Microsoft was overly focused on Windows. That was kind of their raison d'etre, and they missed a lot of other opportunities. But the DOJ definitely slowed them down, and I think appropriately. And if out of that myopic focus on Windows, and to a certain extent, the Department of Justice and the government, the FTC as well, you saw the emergence of internet companies. Now, Microsoft did a major pivot to the internet. They didn't do a major pivot to the cloud until Satya Nadella came in, and now Microsoft is one of those other big tech companies that is under the watchful eye. But I think Microsoft went through that and perhaps learned its lesson. We'll see what happens with Facebook, Google, and Amazon. Facebook, in particular, seems to be conflicted right now. Should we take down a video that has somewhat fake news implications or is a deep hack? Or should we just dial down? We saw this recently with Facebook. They dialed down the promotion. So you almost see Facebook trying to have its cake and eat it too, which personally, I don't think that's the right approach. I think Facebook either has to say damn the torpedoes. It's open content, we're going to promote it. Or do the right thing and take those videos down, those fake news videos. It can't have it both ways. So Facebook seems to be somewhat conflicted. They are probably under the most scrutiny now, as well as Google, who's being accused, anyway, certainly we've seen this in the EU, of promoting its own ads over its competitors' ads. So people are going to be watching that. And, of course, Amazon just having too much power. Having too much power is not necessarily an indication of abusing monopoly power, but you know the government is watching. So that bears watching. theCUBE is going to be covering that. We'll be here all day, covering the IBM CDO event. I'm Dave Vallente, you're watching theCUBE. #IBMCDO, DM us or Tweet us @theCUBE. I'm @Dvallente, keep it right there. We'll be right back right after this short break. (upbeat music)

>> Live from Boston, it's theCUBE! Covering IBM Chief Data Officers Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with my co-host Paul Gillin. We're joined by Inderpal Bhandari, he is the Global Chief Data Officer at IBM. Thank you so much for coming back on theCUBE, Inderpal. >> It's my pleasure. >> It's great to have you. >> Thank you for having me. >> So I want to talk, I want to start by talking a little bit about your own career journey. Your first CDO job was in the early 2000s. You were one of the first CDOs, ever. In the history of Chief Data Officers. Talk a little bit about the evolution of the role and sort of set the scene for our viewers in terms of what you've seen, in your own career. >> Yes, no thank you, December 2006, I became a Chief Data Officer of a major healthcare company. And you know, it turned out at that time there were only four of us. Two in banking, one in the internet, I was the only one in healthcare. And now of course there are well over 1,999 of us and the professions taken off. And I've had the fortune of actually doing this four times now. So leading a legacy in four different organizations in terms of building that organizational capability. I think initially, when I became Chief Data Officer, the culture was one of viewing data's exhaust. Something that we had to discard, that came out of the transactions that you were, that your business was doing. And then after that you would discard this data, or you didn't really care about it. And over the course of time, people had begun to realize that data is actually a strategic asset and you can really use it to drive not just the data strategy, but the actual business strategy, and enable the business to go to the next level. And that transitions been tremendous to watch and to see. I've just been fortunate that I've been there for the full journey. >> Are you seeing any consensus developing around what background makes for a good CDO? What are the skills that a CDO needs? >> Yeah, no that's a very, very good question. My view has been evolving on that one too, over the last few years, right, as I've had these experiences. So, I'll jump to the conclusion, so that you kind of, to answer your question as opposed to what I started out with. The CDO, has to be the change agent in chief, for the organization. That's really the role of the CDO. So yes, there's the technical sharps that you have to have and you have to be able to deal with people who have advanced technical degrees and to get them to move forward. But you do have to change the entire organization and you have to be adept at going after the culture, changing it. You can't get frustrated with all the push back, that's inevitable. You have to almost develop it as an art, as you move forward. And address it, not just bottom up and lateral, but also top down. And I think that's probably where the art gets the most interesting. Because you've got to push a for change even at the top. But you can push just so far without really derailing everything that you are trying to do. And so, I think if I have to pick one attribute, it would be that the CDO has to be the change agent in chief and they have to be adept at addressing the culture of the organization, and moving it forward. >> You're laying out all of these sort of character traits that someone has to be indefatigable, inspirational, visionary. You also said during the keynote you have six months to really make your first push, the first six months are so important. When we talk about presidents, it's the first 100 days. Describe what you mean by that, you have six months? >> So if a new, and I'm talking here mainly about a large organization like an IBM, a large enterprise. When you go in, the key observation is it's a functioning organization. It's a growing concern. It's already making money, it's doing stuff like that. >> We hope. >> And the people who are running that organization, they have their own needs and demands. So very quickly, you can just become somebody who ends up servicing multiple demands that come from different business units, different people. And so that's kind of one aspect of it. The way the organization takes over if you don't really come in with an overarching strategy. The other way the organizations take over is typically large organizations are very siloed. And even at the lower levels you who have people who developed little fiefdoms, where they control that data, and they say this is mine, I'm not going to let anybody else have it. They're the only one's who really understand that curve. And so, pretty much unless you're able to get them to align to a much larger cause, you'll never be able to break down those silos, culturally. Just because of the way it's set up. So its a pervasive problem, goes across the board and I think, when you walk in you've got that, you call it honeymoon period, or whatever. My estimate is based on my experience, six months. If you don't have it down in six months, in terms of that larger cause that your going to push forward, that you can use to at least align everybody with the vision, or you're not going to really succeed. You'll succeed tactically, but not in a strategic sense. >> You're about to undertake the largest acquisition in IBM's history. And as the Chief Data Officer, you must be thinking right now about what that's going to mean for data governance and data integration. How are you preparing for an acquisition that large? >> Yeah so, the acquisition is still got to work through all the regulations, and so forth. So there's just so much we can do. It's much more from a planning stand point that we can do things. I'll give you a sense of how I've been thinking about it. Now we've been doing acquisitions before. So in that since we do have a set process for how we go about it, in terms of evaluating the data, how we're going to manage the data and so forth. The interesting aspect that was different for me on this one is I also talked back on our data strategy itself. And tried to understand now that there's going to be this big acquisition of move forward, from a planning standpoint how should I be prepared to change? With regard to that acquisition. And because we were so aligned with the overall IBM business strategy, to pursue cognition. I think you could see that in my remarks that when you push forward AI in a large enterprise, you very quickly run into this multi-cloud issue. Where you've got, not just different clouds but also unprime and private clouds, and you have to manage across all that and that becomes the pin point that you have to scale. To scale you have to get past that pin point. And so we were already thinking about that. Actually, I just did a check after the acquisition was announced, asking my team to figure out well how standardized are we with Red Hat Linux? And I find that we're actually completely standardized across with Red Hat Linux. We pretty much will have use cases ready to go, and I think that's the facet of the goal, because we were so aligned with the business strategy to begin with. So we were discovering that pinpoint, just as all our customers were. And so when the cooperation acted as it did, in some extent we're already ready to go with used cases that we can take directly to our clients and customers. I think it also has to do with the fact that we've had a partnership with Red Hat for some time, we've been pretty strategic. >> Do you think people understand AI in a business context? >> I actually think that that's, people don't really understand that. That's was the biggest, in my mind anyway, was the biggest barrier to the business strategy that we had embarked on several years ago. To take AI or cognition to the enterprise. People never really understood it. And so our own data strategy became one of enabling IBM itself to become an AI enterprise. And use that as a showcase for our clients and customers, and over the journey in the last two, three years that I've been with IBM. We've become more, we've been putting forward more and more collateral, but also technology, but also business process change ideas, organizational change ideas. So that our clients and customers can see exactly how it's done. Not that i'ts perfect yet, but that too they benefit from, right? They don't make the same mistakes that we do. And so we've become, your colleagues have been covering this conference so they will know that it's become more and more clear, exactly what we're doing. >> You made an interesting comment, in the keynote this morning you said nobody understands AI in a business context. What did you mean by that? >> So in a business context, what does it look like? What does AI look like from an AI enterprise standpoint? From a business context. So excuse me I just trouble them for a tissue, I don't know why. >> Okay, alright, well we can talk about this a little bit too while he-- >> Yeah, well I think we understand AI as an Amazon Echo. We understand it as interface medium but I think what he was getting at is that impacting business processes is a lot more complicated. >> Right. >> And so we tend to think of AI in terms of how we relate to technology rather than how technology changes the rules. >> Right and clearly its such, on the consumers side, we've all grasped this and we all are excited by its possibilities but in terms of the business context. >> I'm back! >> It's the season, yes. >> Yeah, it is the season, don't want to get in closer. So to your question with regard to how-- >> AI in a business context. >> AI in a business context. Consumer context everybody understands, but in a business context what does it really mean? That's difficult for people to understand. But eventually it's all around making decisions. But in my mind its not the big decisions, it's not the decisions we going to acquire Red Hat. It's not those decisions. It's the thousands and thousands of little decisions that are made day in and night out by people who are working the rank and file who are actually working the different processes. That's what we really need to go after. And if you're able to do that, it completely changes the process and you're going to get just such a lot more out of it, not just terms of productivity but also in terms of new ideas that lead to revenue enhancement, new products, et cetera, et cetera. That's what a business AI enterprise looks like. And that's what we've been bringing forward and show casing. In today's keynote I actually had Sonya, who is one of our data governance people, SMEs, who works on metadata generation. Really a very difficult manual problem. Data about data, specifically labeling data so that a business person could understand it. Its all been done manually but now it's done automatically using AI and its completely changed the process. But Sonya is the person who's at the forefront of that and I don't think people really understand that. They think in terms of AI and business and they think this is going to be somebody who's a data scientist, a technologist, somebody who's a very talented technical engineer, but it's not that. It's actually the rank and file people, who've been working these business processes, now working with an intelligent system, to take it to the next level. >> And that's why as you've said it's so important that the CDO is a change agent in chief. Because it is, it does require so much buy-in from, as you say, the rank and file, its not just the top decision makers that you're trying to persuade. >> Yes, you are affecting change at all levels. Top down, bottom up, laterally. >> Exactly. >> You have to go after it across the board. >> And in terms of talking about the data, it's not just data for data's sake. You need to talk about it in terms that a business person can understand. During the keynote, you described an earlier work that you were doing with the NBA. Can you tell our viewers a little bit about that? And sort of how the data had to tell a story? >> Yes, so that was in my first go 'round with IBM, from 1990 through '97. I was with IBM Research, at the Watson Research Lab, as a research staff member. And I created this program called Advanced Scout for the National Basketball Association. Ended up being used by every team on the NBA. And it would essentially suggest who to put in the line up, when you're matching lines up and so forth. By looking at a lot of game data and it was particularly useful during the Playoff games. The major lesson that came out of that experience for me, at that time, alright, this was before Moneyball, and before all this stuff. I think it was like '90, '93, '92. I think if you Google it you will still see articles about this. But the main lesson that came out for me was the first time when the program identified a pattern and suggested that to a coach during a playoff game where they were down two, zero, it suggested they start two backup players. And the coach was just completely flabbergasted, and said there's no way I'm going to do this. This is the kind of thing that would not only get me fired, but make me look really silly. And it hit me then that there was context that was missing, that the coach could not really make a decision. And the way we solved it then was we tied it to the snippets of video when those two players were on call. And then they made the decision that went on and won that game, and so forth. Today's AI systems can actually fathom all that automatically from the video itself. And I think that's what's really advanced the technology and the approaches that we've got today to move forward as quickly as they have. And they've taken hold across the board, right? In the sense of a consumer setting but now also in the sense of a business setting. Where we're applying it pretty much to every business process that we have. >> Exciting. Well Inderpal, thank you so much for coming back on theCUBE, it was always a pleasure talking to you. >> It's my pleasure, thank you. >> I'm Rebecca Knight for Paul Gillin, we will have more from theCUBE's live coverage of IBM CDO coming up in just a little bit. (upbeat music)

>> Announcer: Live from downtown San Francisco, it's theCUBE, covering IBM Chief Data Officers Strategy Summit 2018, brought to you by IBM. >> We're back in San Francisco, everybody. This is theCUBE, the leader in live tech coverage, and we're here covering exclusive coverage of IBM's Chief Data Officer Strategy Summit. This is the summit, as I said, they book in at each coast, San Francisco and Boston. Intimate, a lot of senior practitioners, chief data officers, data folks, people who love data. Caitlyn Halferty is back. She's the Client Engagement Executive and the Chief Data Officer office at IBM. Great. And, Allen Crane, Vice President at USAA. >> Thank you. >> Good to see you. Thanks for coming on. All right. >> Thanks for having us. >> You're welcome. Well, good day today, as I said, a very intimate crowd. You're here as a sort of defacto CDO, learning, sharing, connecting with peers. Set up your role, Allen. Tell us about that. >> At USA, we've got a distributed data and analytics organization where we have centralized functions in our hub, and then each of the lines of business have their own data offices. I happen to have responsibility for all the different ways that our members interact with us, so about 100 million phone calls a year, about a couple billion internet and digital sessions a year, most of that is on mobile, and always lookin' at the ways that we can give back time to our membership, as well as our customer service reps, who we call our member service reps, so that they can serve our members better. The faster and more predictive we can be with being able to understand our members better and prompt our MSRs with the right information to serve them, then the more they can get on to the actual value of that conversation. >> A lot of data. So, one of the things that Inderpal talked about the very first time I met him, in Boston, he talked about the Five Pillars, and the first one was you have to understand as a CDO, how your organization gets value out of data. You said that could be direct monetization or, I guess, increased revenue, cut costs. That's value. >> Right. >> That's right. >> That's the starting point. >> Right. >> So, how did you start? >> Well, actually, it was the internal monetization. So, first off, I want to say USA never sells any of our member data, so we don't think of monetization in that framework, but we do think of it terms of how do we give something that's even more precious than money back to our company and to our members and the MSRs? And, that is really that gift of time. By removing friction from the system, we've been able to reduce calls per member, through digitization activities, and reduced transfers and reduced misdirects by over 10% every year. We're doing work with AI and machine learning to be able to better anticipate what the member is calling about, so that we can get them to the right place at the right time to the right set member service representatives. And, so all these things have resulted in, not just time savings but, obviously, that translates directly to bottom line savings, but at the end of the day, it's about increasing that member service level, increasing your responsiveness, increasing the speed that you're answering the phone, and ultimately increasing that member satisfaction. >> Yeah, customer satisfaction, lowers churn rates, that's a form of monetization, >> Absolutely. >> so it's hard dollars to the CFO, right? >> Absolutely, yeah. >> All right, let's talk about the role of the CDO. This is something that we touched on earlier. >> Yes. >> We're bringing it home here. >> Yes. >> Last segment. Where are we at with the role of the CDO? It was sort of isolated for years in regulated industries, >> Correct. >> permeated to mainstream organizations. >> Correct. >> Many of those mainstream organizations can move faster, 'cause their not regulated, so have we sort of reached parody between the regulated and the unregulated, and what do you discern there in terms of patterns and states of innovation? >> Sure. I think when we kicked off these summits in 2014, many of our CDOs came from CIO type organizations, defensive posture, you know, king of the data warehouse that we joke about, and now annuls reports of that time were saying maybe 20% of large organizations were investing in the CDO or similar individual responsible for enterprise data, and now we see analysts reports coming out to say upwards of 85, even 90%, of organizations are investing in someone responsible for that role of the CDO type. In my opening remarks this morning, I polled the room to say who's here for the first time. It was interesting, 69, 70% of attendees were joining us for the first time, and I went back, okay, who's been here last year, year before, and I said who was here from the beginning, 2014 with us, and Allen is one of the individuals who's been with us. And, as much as the topics have changed and the role has grown and the purview and scope of responsibilities, some topics have remained, our attendees tell us, they're still important, top-of-mind, and data monetization is one of those. So, we always have a panel on data monetization, and we've had some good discussions recently, that the idea of it's just the external resell, or something to do with selling data externally is one view, but really driving that internal value, and the ways you drive out those efficiencies is another perspective on it. So, fortunate to have Allen here. >> Well, we've been able to, for that very reason, we've been able to grow our team from about six or seven people five years ago to well over a hundred people, that's focused on how we inefficiency out of the system. That mere 10%, when your call-per-member reduction, when you're taking 30 million calls in the bank, you know, that's real dollars, three million calls out of the system that you can monetize like that. So, it's real value that the company sees in us, and I think that, in a sense, is really how you want to be growing in a data organization, because people see value in you, are willing to give you more, and then you start getting into those interesting conversations, if I gave you more people, could you get me more results? >> Let's talk about digital transformation and how it relates to all this. Presumably, you've got a top down initiative, the CEO says, he or she says, okay, this is important. We got to do it. Boom, there's the North Star. Let's go. What's the right regime that you're seeing? Obviously, you've got to have the executive buy-in, you've got the Chief Data Officer, you have the Chief Digital Officer, the Chief Operating Officer, the CFO's always going to be there, making sure things are on track. How are you seeing that whole thing shake out, at least in your organization? >> Well, one thing that we've been seeing is digital digitization or the digital transformation is not about just going only digital. It's how does all this work together. It can't just be an additive function, where you're still taking just as many calls and so forth, but it's got to be something that that experience online has got to do something that's transformative in your organization. So, we really look at the member all the way through that whole ecosystem, and not just through the digital lens. And, that's really where teams like ours have really been able to stitch together the member experience across all their channels that they're interacting with us, whether that's the marketing channels or the digital channels or the call channel, so that we can better understand that experience. But, it's certainly a complementary one. It can't just be an additive one. >> I wonder if we could talk about complacency, in terms of digital transformation. I talk to a lot of companies and there's discussion about digital, but you talk to a lot of people who say, well, we're doing fine. Maybe not in our industry. Insurance is one that hasn't been highly disruptive, financial services, things like aerospace. I'll be retired by the time this all, I mean, that's true, right? And, probably accurate. So, are you seeing a sense of complacency or are you seeing a sense of urgency, or a mix or both? What are you seeing, Caitlyn? >> Well, it's interesting, and people may not be aware, but I'm constantly polling our attendees to ask what are top-of-mind topics, what are you struggling with, where are you seeing successes, and digital was one that came up for this particular session, which is why tomorrow's keynote, we have our Chief Digital Officer giving the morning keynote, to show how our data office and digital office are partnering to drive transformation internally. So, at least for our perspective, in the internal side of it, we have a priority initiative, a cognitive sales advisor, and it's essentially intended to bring in disparate part of customer data, obtained through many different channels, all the ways that they engage with us, online and other, and then, deliver it through sales advisor app that empowers our digital sellers to better meet their revenue targets and impact, and develop more of a quality client relationship and improve that customer experience. So, internally, at least, it's been interesting to see one of our strongest partnerships, in terms of business unit, has been our data and digital office. They say, look, the quality of the data is at the core, you then enable our digital sellers, and our clients benefit, for a better client experience. >> Well, about a year ago, we absolutely changed the organization to align the data office with the digital office, so that reports to our executive counsel level, so their peers, that reporting to the same organization, to ensure that those strategies are connected. >> Yeah, so as Caitlyn was saying, this Chief Data Officer kind of emerged from a defensive posture of compliance, governance, data quality. The Chief Digital Officer, kind of new, oftentimes associated with marketing, more of an external, perhaps, facing role, not always. And then, the CIO, we'll say, well, wait a minute, data is the CIO's job, but, of course, the CIO, she's too busy trying to keep the lights on and make everything work. So, where does the technology organization fit? >> Well, all that's together, so when we brought all those things together at the organizational level, digital, data, and technology were all together, and even design. So, you guys are all peers, reporting into the executive committee, essentially, is that right? Yes, our data, technology, and design, and digital office are all peers reporting to the same executive level. And then, one of the other pillars that Inderpal talks about is the relationship with the line of business. So, how is that connective tissue created? Well, being on the side that is responsible for how all of our members interact, my organization touches every product, every line of business, every channel that our members are interacting with, so our data is actually shared across the organization, so right now, really my focus is to make sure that that data is as accessible as it can be across our enterprise partners, it's as democratized as it can be, it's as high as quality. And then, things that we're doing around machine learning and AI, can be enabled and plugged into from all those different lines of business. >> What does success look like in your organization? How do you know you're doing well? I mean, obviously, dropping money to the bottom line, but how are you guys measuring yourselves and setting objectives? What's your North Star? >> I think success, for me, is when you're doing a good job, to the point that people say that question, could you do more if I gave you more? That, to me, is the ultimate validation. It's how we grew as an organization. You know, we don't have to play that justification game When people are already coming to the table saying, You're doing great work. How can you do more great work? >> So, what's next for these summits? Are you doing Boston again in the fall? Is that right? Are you planning >> We are, we are, >> on doing that? >> and you know, fall of last year, we released the blueprint, and the intent was to say, hey, here's the reflection of our 18 months, internal journey, as well as all our client interactions and their feedback, and we said, we're coming back in the spring and we're showing you the detail of how we really built out these internal platforms. So, we released our hybrid on-prem Cloud showcase today, which was great, and to the level of specificity that shows that the product solutions, what we're using, the Flash Storage, some of the AI components of machine learning models. >> The cognitive systems component? >> Exactly. And then, our vision, to your question to the fall, is coming back with the public Cloud showcases. So, we're already internally doing work on our public Cloud, in particular respect to our backup, some of our very sensitive client data, as well as some initial deep learning models, so those are the three pieces we're doing in public Cloud internally, and just as we made the commitment to come back and unveil and show those detail, we want to come back in the fall and show a variety of public Cloud showcases where we're doing this work. And then, hopefully, we'll continue to partner and say, hey, here's how we're doing it. We'd love to see how you're doing it. Let's share some best practices, accelerate, build these capabilities. And, I'll say to your business benefit question, what we've found is once we've built that platform, we call it, internally, a one IBM architecture, out our platform, we can then drive critical initiatives for the enterprise. So, for us, GVPR, you know, we own delivery of GVPR readiness across the IBM corporation, working with senior executives in all of our lines of business, to make sure we get there. But, now we've got the responsibility to drive out initiatives like that cross business unit, to your question on the partnerships. >> The evolution of this event seems to be, well, it's got a lot of evangelism early on, and now it's really practical, sort of sharing, like you say, the blueprint, how to apply it, a lot of people asking questions, you know, there's different levels of maturity. Now, you guys back tomorrow? You got to panel, you guys are doing a panel on data monetization? >> We're doing a panel on data monetization tomorrow. >> Okay, and then, you've got Bob Lord and Inderpal talking about that, so perfect juxtaposition and teamwork of those two major roles. >> And, this is the first time we've really showcased the data/digital partnership and connection, so I'm excited, want to appeal to the developer viewpoint of this. So, I think it'll be a great conversation about data at the core, driving digital transformation. And then, as you said, our data monetization panel, both external efforts, as well as a lot of the internal value that we're all driving, so I think that'll be a great session tomorrow. >> Well, and it's important, 'cause there's a lot of confusing, and still is a lot of confusion about those roles, and you made the point early today, is look, there's a big organizational issue you have to deal with, particularly around data silos, MyData. I presume you guys are attacking that challenge? >> Absolutely. >> Still, it's still a-- >> It's an ongoing-- >> Oh, absolutely. >> I think we're getting a lot better at it, but you've got to lean in, because if it's not internal, it's some of the external challenges around. Now we're picking Cloud vendors and so forth. Ten years ago, we had our own silos and our own warehouses, if we had a warehouse, and then, we were kind of moving into our own silos in our own databases, and then as we democratized that, we solved the one problem, but now our data's so big and compute needs are so large that we have no choice but to get more external into Cloud. So, you have to lean in, because everything is changing at such a rapid rate. >> And, it requires leadership. >> Yep. >> Absolutely. >> The whole digital data really requires excellent leadership, vision. IBM's catalyzing a lot of that conversation, so congratulations on getting this going. Last thoughts. >> Oh, I would just say, we were joking that 2014, the first couple of summits, small group, maybe 20-30 participants figuring out how to best organize from a structural perspective, you set up the office, what sort of outcomes, metrics, are we going to measure against, and those things, I think, will continue to be topics of discussion, but now we see we've got about 500 data leaders that are tracking our journey and that are involved and engaged with us. We've done a lot in North America, we're starting to do more outside the geographies, as well, which is great to see. So, I just have to say I think it's interesting to see the topics that continue to be of interest, the governance, the data monetization, and then, the new areas around AI, machine learning, data science, >> data science >> the empowering developers, the DevOps delivery, how we're going to deliver that type of training. So, it's been really exciting to see the community grow and all the best practices leveraged, and look forward to continuing to do more of that this year as well. >> Well, you obviously get a lot of value out of these events. You were here at the first one, you're here today. So, 2018. Your thoughts? >> I think the first one, we were all trying to figure out who we are, what's our role, and it varied from I'm a individual contributor, data evangelist in the organization to I'm king of the warehouse thing. >> Right. >> And, largely, from that defensive standpoint. I think, today, you see a lot more people that are leaning in, leading data science teams, leading the future of where the organizations are going to be going. This is really where the center of a lot of organizations are starting to pivot and look, and see, where is the future, and how does data become the leading edge of where the organization is going, so it's pretty cool to be a part of a community like this that's evolving that way, but then also being able to have that at a local level within your own organization. >> Well, another big take-away for me is the USAA example shows that this can pay for itself when you grow your own organization from a handful of people to a hundred plus individuals, driving value, so it makes it easier to justify, when you can demonstrate a business case. Well, guys, thanks very much for helping me wrap here. >> Absolutely. >> I appreciate you having us here. >> Thank you. >> It's been a great event. Always a pleasure, hopefully, we'll see you in the fall. >> Sounds good. Thank you so much. >> All right, thanks, everybody, for watching. We're out. This is theCUBE from IBM CDO Summit. Check out theCUBE.net for all of the videos, siliconangle.com for all the news summaries of this event, and wikibon.com for all the research. We'll see you next time. (techy music)

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

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

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

>> Announcer: Live from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit, 2018, brought to you by IBM. (techno music) >> Welcome to San Francisco everybody. My name is Dave Vellante and you're watching theCUBE, the leader in live tech coverage, and we're at the IBM CDO Strategy Summit, #IBMCDO. The chief data officer role emerged about a decade ago, and it was typically focused in regulated industries, health care, financial services, and government. And it sort of emerged from a dark, back office role of governance and compliance and data quality. But increasingly as the big data wave came to the market, people realized there was an opportunity to take that sort of wonky back office governance, compliance, discipline, and really point it toward generating value, whether that was with direct monetization of data or contributing to an organization's data strategy. And, over the next five to seven years, that chief data officer role... Couple things happen, one is got much much deeper into those regulated industries, but also permeated other non-regulated industries beyond those three that I mentioned. IBM is an organization that has targeted the chief data officer role as a key constituency as part of what IBM calls the cognitive enterprise. And IBM hosts shows in Boston and San Francisco each year, gathering chief data officers, about 100 to 150 chief data officers, in each city. These are very focused and targeted events that comprise of chief data officers, data analytics officers, and the like, people focused sometimes on compliance and governance. They're very intimate events and today, we heard from a number of IBM experts, Inderpal Bhandari, who's been on theCUBE a number of times, who is IBM's global chief data officer, laying out, sort of a blueprint, an enterprise blueprint, for data strategy. So the audience is filled with practitioners who are really sort of lapping up sort of the how to implement some of these techniques, and ultimately platforms. IBM has put together solutions, that not only involve, of course, Watson, but also some of the other components, whether its cognitive systems, governance systems, compliance systems, to create a solution that chief data officers and their colleagues can implement. So, this morning we heard about the cognitive enterprise blueprint, what IBM calls the AI enterprise, or the cognitive enterprise, talking about organizational issues. How do you break down silos of data? If you think about most incumbent organizations, the data lives in silos. It may be data in the marketing department, data in the sales department, data in the customer service department, data in the maintenance department. So these are sort of separate silos of data. How do you break those down? How do you bring those together so you can compete with some of these born digital AI-oriented companies, the likes of, just the perfect example is Facebook, Google, LinkedIn, et cetera, who have these sort of centralized data models. How do you take an existing organization, break down those silos, and deal with a data model that is accessible by everyone who needs to access that data, and as well, very importantly, make it secure, make it enterprise-ready. The other thing that IBM talked about was process. We always talk about on theCUBE, people, process, and technology. Technology is the easiest piece of that. It's the people and process components of that matrix that you need to really focus on before you even bring in the technology, and then, of course, there is the technology component. IBM is a technology company. We've heard about Watson. IBM has a number of hardware and software components that it brings to bear to try to help organizations affect their data strategy, and be more effective in the marketplace. So, as I say this is about 130, 150 chief data officers. We heard from Kaitlin Lafferty, who's going to come on a little later. She's going to be my quasi-co-host, which will be interesting. Beth Smith, who is the GM of Watson Data. She talked a lot about use cases. She gave an example of Orange Bank, a totally digital bank, using Watson to service customers. You can't call this bank. And they've got some interesting measurements that they'll share with us in terms of customer satisfaction and born-digital or all-digital bank. She also talked about partnerships that they're doing, not directly, sort of indirectly I inferred, she talked about IT service management embedding Watson into the IT service management from an HR perspective. I believe that she was referring to, even though she didn't mention it, a deal that IBM struck with ServiceNow. IBM's got similar deals with Watson with Salesforce. Salesforce Einstein is based on Watson. So what you're seeing is embedding AI into different applications, and we've talked about this a lot at siliconANGLE and theCUBE and at Wikibon. It's really those embedded use cases for AI that are going to drive adoption, as opposed to generalized horizontal AI. That seems to be not the recipe for adoption success, really more so specific use cases. I mean the obvious ones are some consumer ones, and even in the enterprise as well: security, facial recognition, natural language processing, for example. Very specific use cases for AI. We also heard from Inderpal Bhandari, the global chief data officer of IBM, talking about the AI enterprise, really showcasing IBM as a company that is bringing this AI enterprise to itself, and then teaching, sharing that knowledge with its clients and with its customers. I really like talking to Inderpal Bhandari. I learn a lot from him. This is his fourth CDO gig, okay. He was the very first CDO ever in health care when there, I mean I think he was the first of four or one of four, first CDOs in health care. Now there are thousands. So this is his fourth gig as a CDO. He talks about what a CDO has to do to get started, starting with a clear data strategy. When I've talked to him before, he said, he mentioned, how does data contribute to the monetization of your organization? Now it's not always monetization. If it's a non-public company or a health care company, for example, that's not-for-profit, it's not necessarily a monetization component, it's more of a how does it effect your strategy. But that's number one is sort of, how does data drive value for you organization? The second is, how do you implement the system that's based on governance and security? What's the management system look like? Who has data and who has access to that data? How do you affect privacy? And then, how do you become a central source for that AI-framework, being a service organization essentially to the entire organization? And then, developing deep analytics partnerships with lines of business. That's critical, because the domain expertise for the business is obviously going to live in the line of business, not in some centralized data organization. And, then, finally, very importantly, skills. What skills do you need, identify those skills, and then how do you get those people? How do you both train internally and find those people externally? Very hard to find those skills. He talked about AI systems having four attributes. Number one is expertise, domain knowledge. AI systems have to be smart about the problem that they're trying to solve. Natural human interaction, IBM talks about natural language processing, a lot of companies do. Everybody's familiar with the likes of Alexa, Google Home, and Siri. Well IBM Watson also has an NLP capability that's quite powerful. So that's very important. And interestingly he talked about, I'll ask him about this, the black box phenomenon. Most AI is a black box. If you think about it, AI can tell you if you're looking at a dog, but think about your own human frame. How do you know when you're actually seeing a dog? Try to explain to somebody someday how you go about recognizing that animal. It's sort of hard to do. Systems today can tell you that if it's a dog or for you Silicon Valley watchers, hot dog. But, it's a black box. What IBM is saying is no, we can't live with a black box in the enterprise. We have to open up that black box, make it a white box, and share with our customers exactly how that decision is being made. That's an interesting problem that I want to talk to him about. And then, next, the third piece is learning through education. How do you learn at scale? And then the fourth piece was, how do you evolve, how do you iterate, how do you become auto-didactic or self-learning with regard to the system and getting better and better and better over time. And that sets a foundation for this AI enterprise or cognitive enterprise blueprints, where the subject matter expert can actually interact with the system. We had some questions from the audience. One came up on cloud and security concerns, not surprising. Data exposure, how do you automate a lot of this stuff and provide access, at the same time ensuring privacy and security. So IBM's going to be addressing that today. So, we're here all day, wall-to-wall coverage of the IBM CDO Strategy Summit, #IBMCDO. Of course, we're running multiple live programs today. I'm covering this show in San Francisco. John Furrier is in Copenhagen at KubeCon with The Linux Foundation. Stu Miniman is holding down the fort with a very large crew at Dell Technology's World. So keep it right there everybody. This is theCUBE at IBM's CDO Strategy Summit in San Francisco. We'll be right back after this short break. (techno music) (dial tones)

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and it's ecosystem partners. >> Welcome back to theCUBE's continuing coverage of our own event, Big Data SV. I'm Lisa Martin, with my cohost Dave Vellante. We're in downtown San Jose at this really cool place, Forager Eatery. Come by, check us out. We're here tomorrow as well. We're joined by, next, one of our CUBE alumni, Seth Dobrin, the Vice President and Chief Data Officer at IBM Analytics. Hey, Seth, welcome back to theCUBE. >> Hey, thanks for having again. Always fun being with you guys. >> Good to see you, Seth. >> Good to see you. >> Yeah, so last time you were chatting with Dave and company was about in the fall at the Chief Data Officers Summit. What's kind of new with you in IBM Analytics since then? >> Yeah, so the Chief Data Officers Summit, I was talking with one of the data governance people from TD Bank and we spent a lot of time talking about governance. Still doing a lot with governance, especially with GDPR coming up. But really started to ramp up my team to focus on data science, machine learning. How do you do data science in the enterprise? How is it different from doing a Kaggle competition, or someone getting their PhD or Masters in Data Science? >> Just quickly, who is your team composed of in IBM Analytics? >> So IBM Analytics represents, think of it as our software umbrella, so it's everything that's not pure cloud or Watson or services. So it's all of our software franchise. >> But in terms of roles and responsibilities, data scientists, analysts. What's the mixture of-- >> Yeah. So on my team I have a small group of people that do governance, and so they're really managing our GDPR readiness inside of IBM in our business unit. And then the rest of my team is really focused on this data science space. And so this is set up from the perspective of we have machine-learning engineers, we have predictive-analytics engineers, we have data engineers, and we have data journalists. And that's really focus on helping IBM and other companies do data science in the enterprise. >> So what's the dynamic amongst those roles that you just mentioned? Is it really a team sport? I mean, initially it was the data science on a pedestal. Have you been able to attack that problem? >> So I know a total of two people that can do that all themselves. So I think it absolutely is a team sport. And it really takes a data engineer or someone with deep expertise in there, that also understands machine-learning, to really build out the data assets, engineer the features appropriately, provide access to the model, and ultimately to what you're going to deploy, right? Because the way you do it as a research project or an activity is different than using it in real life, right? And so you need to make sure the data pipes are there. And when I look for people, I actually look for a differentiation between machine-learning engineers and optimization. I don't even post for data scientists because then you get a lot of data scientists, right? People who aren't really data scientists, and so if you're specific and ask for machine-learning engineers or decision optimization, OR-type people, you really get a whole different crowd in. But the interplay is really important because most machine-learning use cases you want to be able to give information about what you should do next. What's the next best action? And to do that, you need decision optimization. >> So in the early days of when we, I mean, data science has been around forever, right? We always hear that. But in the, sort of, more modern use of the term, you never heard much about machine learning. It was more like stats, math, some programming, data hacking, creativity. And then now, machine learning sounds fundamental. Is that a new skillset that the data scientists had to learn? Did they get them from other parts of the organization? >> I mean, when we talk about math and stats, what we call machine learning today has been what we've been doing since the first statistics for years, right? I mean, a lot of the same things we apply in what we call machine learning today I did during my PhD 20 years ago, right? It was just with a different perspective. And you applied those types of, they were more static, right? So I would build a model to predict something, and it was only for that. It really didn't apply it beyond, so it was very static. Now, when we're talking about machine learning, I want to understand Dave, right? And I want to be able to predict Dave's behavior in the future, and learn how you're changing your behavior over time, right? So one of the things that a lot of people don't realize, especially senior executives, is that machine learning creates a self-fulfilling prophecy. You're going to drive a behavior so your data is going to change, right? So your model needs to change. And so that's really the difference between what you think of as stats and what we think of as machine learning today. So what we were looking for years ago is all the same we just described it a little differently. >> So how fine is the line between a statistician and a data scientist? >> I think any good statistician can really become a data scientist. There's some issues around data engineering and things like that but if it's a team sport, I think any really good, pure mathematician or statistician could certainly become a data scientist. Or machine-learning engineer. Sorry. >> I'm interested in it from a skillset standpoint. You were saying how you're advertising to bring on these roles. I was at the Women in Data Science Conference with theCUBE just a couple of days ago, and we hear so much excitement about the role of data scientists. It's so horizontal. People have the opportunity to make impact in policy change, healthcare, etc. So the hard skills, the soft skills, mathematician, what are some of the other elements that you would look for or that companies, enterprises that need to learn how to embrace data science, should look for? Someone that's not just a mathematician but someone that has communication skills, collaboration, empathy, what are some of those, openness, to not lead data down a certain, what do you see as the right mix there of a data scientist? >> Yeah, so I think that's a really good point, right? It's not just the hard skills. When my team goes out, because part of what we do is we go out and sit with clients and teach them our philosophy on how you should integrate data science in the enterprise. A good part of that is sitting down and understanding the use case. And working with people to tease out, how do you get to this ultimate use case because any problem worth solving is not one model, any use case is not one model, it's many models. How do you work with the people in the business to understand, okay, what's the most important thing for us to deliver first? And it's almost a negotiation, right? Talking them back. Okay, we can't solve the whole problem. We need to break it down in discreet pieces. Even when we break it down into discreet pieces, there's going to be a series of sprints to deliver that. Right? And so having these soft skills to be able to tease that in a way, and really help people understand that their way of thinking about this may or may not be right. And doing that in a way that's not offensive. And there's a lot of really smart people that can say that, but they can come across at being offensive, so those soft skills are really important. >> I'm going to talk about GDPR in the time we have remaining. We talked about in the past, the clocks ticking, May the fines go into effect. The relationship between data science, machine learning, GDPR, is it going to help us solve this problem? This is a nightmare for people. And many organizations aren't ready. Your thoughts. >> Yeah, so I think there's some aspects that we've talked about before. How important it's going to be to apply machine learning to your data to get ready for GDPR. But I think there's some aspects that we haven't talked about before here, and that's around what impact does GDPR have on being able to do data science, and being able to implement data science. So one of the aspects of the GDPR is this concept of consent, right? So it really requires consent to be understandable and very explicit. And it allows people to be able to retract that consent at any time. And so what does that mean when you build a model that's trained on someone's data? If you haven't anonymized it properly, do I have to rebuild the model without their data? And then it also brings up some points around explainability. So you need to be able to explain your decision, how you used analytics, how you got to that decision, to someone if they request it. To an auditor if they request it. Traditional machine learning, that's not too much of a problem. You can look at the features and say these features, this contributed 20%, this contributed 50%. But as you get into things like deep learning, this concept of explainable or XAI becomes really, really important. And there were some talks earlier today at Strata about how you apply machine learning, traditional machine learning to interpret your deep learning or black box AI. So that's really going to be important, those two things, in terms of how they effect data science. >> Well, you mentioned the black box. I mean, do you think we'll ever resolve the black box challenge? Or is it really that people are just going to be comfortable that what happens inside the box, how you got to that decision is okay? >> So I'm inherently both cynical and optimistic. (chuckles) But I think there's a lot of things we looked at five years ago and we said there's no way we'll ever be able to do them that we can do today. And so while I don't know how we're going to get to be able to explain this black box as a XAI, I'm fairly confident that in five years, this won't even be a conversation anymore. >> Yeah, I kind of agree. I mean, somebody said to me the other day, well, it's really hard to explain how you know it's a dog. >> Seth: Right (chuckles). But you know it's a dog. >> But you know it's a dog. And so, we'll get over this. >> Yeah. >> I love that you just brought up dogs as we're ending. That's my favorite thing in the world, thank you. Yes, you knew that. Well, Seth, I wish we had more time, and thanks so much for stopping by theCUBE and sharing some of your insights. Look forward to the next update in the next few months from you. >> Yeah, thanks for having me. Good seeing you again. >> Pleasure. >> Nice meeting you. >> Likewise. We want to thank you for watching theCUBE live from our event Big Data SV down the street from the Strata Data Conference. I'm Lisa Martin, for Dave Vellante. Thanks for watching, stick around, we'll be rick back after a short break.

>> Announcer: Live, from Orlando, Florida, it's The Cube. Covering PentahoWorld 2017. Brought to you by, Hitachi Vantara. >> Welcome back to Orlando, everybody. This is PentahoWorld, #pworld17 and this is The Cube, The leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host, Jim Kobielus Donna Prlich is here, she's the Chief Product Officer of Pentaho and a many-time Cube guest. Great to see you again. >> Thanks for coming on. >> No problem, happy to be here. >> So, I'm thrilled that you guys decided to re-initiate this event. You took a year off, but we were here in 2015 and learned a lot about Pentaho and especially about your customers and how they're applying this, sort of, end-to-end data pipeline platform that you guys have developed over a decade plus, but it was right after the acquisition by Hitachi. Let's start there, how has that gone? So they brought you in, kind of left you alone for awhile, but what's going on, bring us up to date. >> Yeah, so it's funny because it was 2015, it was PentahoWorld, second one, and we were like, wow, we're part of this new company, which is great, so for the first year we were really just driving against our core. Big-Data Integration, analytics business, and capturing a lot of that early big-data market. Then, probably in the last six months, with the initiation of Hitachi Ventara which really is less about Pentaho being merged into a company, and I think Brian covered it in a keynote, we're going to become a brand new entity, which Hitachi Vantara is now a new company, focused around software. So, obviously, they acquired us for all that big-data orchestration and analytics capability and so now, as part of that bigger organization, we're really at the center of that in terms of moving from edge to outcome, as Brian talked about, and how we focus on data, digital transformation and then achieving the outcome. So that's where we're at right now, which is exciting. So now we're part of this bigger portfolio of products that we have access to in some ways. >> Jim: And I should point out that Dave called you The CPO of Pentaho, but in fact you're the CPO of Hitachi Vantara, is that correct? >> No, so I am not. I am the CPO for the Pentaho product line, so it's a good point, though, because Pentaho brand, the product brand, stays the same. Because obviously we have 1,800 customers and a whole bunch of them are all around here. So I cover that product line for Hitachi Vantara. >> David: And there's a diverse set of products in the portfolios >> Yes. >> So I'm actually not sure if it makes sense to have a Chief Products officer for Hitachi Vantara, right? Maybe for different divisions it makes sense, right? But I've got to ask you, before the acquisition, how much were you guys thinking about IOT and Industrial IOT? It must have been on your mind, at about 2015 it certainly was a discussion point and GE was pushing all this stuff out there with the ads and things like that, but, how much was Pentaho thinking about it and how has that accelerated since the acquisition? >> At that time in my role, I had product marketing I think I had just taken Product Management and what we were seeing was all of these customers that were starting to leverage machine-generated data and were were thinking, well, this is IOT. And I remember going to a couple of our friendly analyst folks and they were like, yeah, that's IOT, so it was interesting, it was right before we were acquired. So, we'd always focus on these blueprints of we've got to find the repeatable patterns, whether it's Customer 360 in big data and we said, well they're is some kind of emerging pattern here of people leveraging sensor data to get a 360 of something. Whether it's a customer or a ship at sea. So, we started looking at that and going, we should start going after this opportunity and, in fact, some of the customers we've had for a long time, like IMS, who spoke today all around the connected cars. They were one of the early ones and then in the last year we've probably seen more than 100% growth in customers, purely from a Pentaho perspective, leveraging Machine-generated data with some other type of data for context to see the outcome. So, we were seeing it then, and then when we were acquired it was kind of like, oh this is cool now we're part of this bigger company that's going after IOT. So, absolutely, we were looking at it and starting to see those early use cases. >> Jim: A decade or more ago, Pentaho, at that time, became very much a pioneer in open-source analytics, you incorporated WECA, the open-source code base for machine-learning, data mining of sorts. Into the core of you're platform, today, here, at the conference you've announced Pentaho 8.0, which from what I can see is an interesting release because it brings stronger integration with the way the open-source analytic stack has evolved, there's some Spark Streaming integration, there's some Kafaka, some Hadoop and so forth. Can you give us a sense of what are the main points of 8.0, the differentiators for that release, and how it relates to where Pentaho has been and where you're going as a product group within Hiatachi Vantara. >> So, starting with where we've been and where we're going, as you said, Anthony DeShazor, Head of Customer Success, said today, 13 years, on Friday, that Pentaho started with a bunch of guys who were like, hey, we can figure out this BI thing and solve all the data problems and deliver the analytics in an open-source environment. So that's absolutely where we came form. Obviously over the years with big data emerging, we focused heavily on the big data integration and delivering the analytics. So, with 8.0, it's a perfect spot for us to be in because we look at IOT and the amount of data that's being generated and then need to address streaming data, data that's moving faster. This is a great way for us to pull in a lot of the capabilities needed to go after those types of opportunities and solve those types of challenges. The first one is really all about how can we connect better to streaming data. And as you mentioned, it's Spark Streaming, it's connecting to Kafka streams, it's connecting to the Knox gateway, all things that are about streaming data and then in the scale-up, scale-out kind of, how do we better maximize the processing resources, we announced in 7.1, I think we talked to you guys about it, the Adaptive Execution Layers, the idea that you could choose execution engine you want based on the processing you need. So you can choose the PDI engine, you can choose Spark. Hopefully over time we're going to see other engines emerge. So we made that easier, we added Horton Work Support to that and then this concept of, so that's to scale up, but then when you think about the scale-out, sometimes you want to be able to distribute the processing across your nodes and maybe you run out of capacity in a Pentaho server, you can add nodes now and then you can kind-of get rid of that capacity. So this concept of worker-nodes, and to your point earlier about the Hitachi Portfolio, we use some of the services in the foundry layer that Hitachi's been building as a platform. >> David: As a low balancer, right? >> As part of that, yes. So we could leverage what they had done which if you think about Hitachi, they're really good at storage, and a lot of things Pentaho doesn't have experience in, and infrastructure. So we said, well why are we trying to do this, why don't we see what these guys are doing and we leverage that as part of the Pentaho platform. So that's the first time we brought some of their technology into the mix with the Pentaho platform and I think we're going to see more of that and then, lastly, around the visual data prep, so how can we keep building on that experience to make data prep faster and easier. >> So can I ask you a really Columbo question on that sort-of load-balancing capabilities that you just described. >> That's a nice looking trench coat you're wearing. >> (laughter) gimme a little cigar. So, is that the equivalent of a resource negotiator? Do I think of that as sort of your own yarn? >> Donna: I knew you were going to ask me about that (laughter) >> Is that unfair to position it that way? >> It's a little bit different, conceptually, right, it's going to help you to better manage resources, but, if you think about Mesos and some of the capabilities that are out there that folks are using to do that, that's what we're leveraging, so it's really more about sometimes I just need more capacity for the Pentaho server, but I don't need it all the time. Not every customer is going to get to the scale that they need that so it's a really easy way to just keep bringing in as much capacity as you need and have it available. >> David: I see, so really efficient, sort of low-level kind of stuff. >> Yes. >> So, when you talk about distributed load execution, you're pushing more and more of the processing to the edge and, of course, Brian gave a great talk about edge to outcome. You and I were on a panel with Mark Hall and Ella Hilal about the, so called, "power of three" and you did a really good blog post on that the power of the IOT, and big data, and the third is either predictive analytics or machine learning, can you give us a quick sense for our viewers about what you mean by the power of three and how it relates to pushing more workloads to the edge and where Hitachi Vantara is going in terms of your roadmap in that direction for customers. >> Well, its interesting because one of the things we, maybe we have a recording of it, but kind of shrink down that conversation because it was a great conversation but we covered a lot of ground. Essentially that power of three is. We started with big data, so as we could capture more data we could store it, that gave us the ability to train and tune models much easier than we could before because it was always a challenge of, how do I have that much data to get my model more accurate. Then, over time everybody's become a data scientist with the emergence of R and it's kind of becoming a little bit easier for people to take advantage of those kinds of tools, so we saw more of that, and then you think about IOT, IOT is now generating even more data, so, as you said, you're not going to be able to process all of that, bring all that in and store it, it's not really efficient. So that's kind of creating this, we might need the machine learning there, at the edge. We definitely need it in that data store to keep it training and tuning those models, and so what it does is, though, is if you think about IMS, is they've captured all that data, they can use the predictive algorithms to do some of the associations between customer information and the censor data about driving habits, bring that together and so it's sort of this perfect storm of the amount of data that's coming in from IOT, the availability of the machine learning, and the data is really what's driving all of that, and I think that Mark Hall, on our panel, who's a really well-known data-mining expert was like, yeah, it all started because we had enough data to be able to do it. >> So I want to ask you, again, a product and maybe philosophy question. We've talked on the Cube a lot about the cornucopia of tooling that's out there and people who try to roll their own and. The big internet companies and the big banks, they get the resources to do it but they need companies like you. When we talk to your customers, they love the fact that there's an integrated data pipeline and you've made their lives simple. I think in 8.0 I saw spark, you're probably replacing MapReduce and making life simpler so you've curated a lot of these tools, but at the same time, you don't own you're own cloud, you're own database, et cetera. So, what's the philosophy of how you future-proof your platform when you know that there are new projects in Apache and new tooling coming out there. What's the secret sauce behind that? >> Well the first one is the open-source core because that just gave us the ability to have APIs, to extend, to build plugins, all of that in a community that does quite a bit of that, in fact, Kafka started with a customer that built a step, initially, we've now brought that into a product and created it as part of the platform but those are the things that in early market, a customer can do at first. We can see what emerges around that and then go. We will offer it to our customers as a step but we can also say, okay, now we're ready to productize this. So that's the first thing, and then I think the second one is really around when you see something like Spark emerge and we were all so focused on MapReduce and how are we going to make it easier and let's create tools to do that and we did that but then it was like MapReduce is going to go away, well there's still a lot of MapReduce out there, we know that. So we can see then, that MapReduce is going to be here and, I think the numbers are around 50/50, you probably know better than I do where Spark is versus MapReduce. I might be off but. >> Jim: If we had George Gilbert, he'd know. >> (laughs) Maybe ask George, yeah it's about 50/50. So you can't just abandon that, 'cause there's MapReduce out there, so it was, what are we going to do? Well, what we did in the Hadoop Distro days is we created a adaptive, big data layer that said, let's abstract a layer so that when we have to support a new distribution of Hadoop, we don't have to go back to the drawing board. So, it was the same thing with the execution engines. Okay, let's build this adaptive execution layer so that we're prepared to deal with other types of engines. I can build the transformation once, execute it anywhere, so that kind of philosophy of stepping back if you have that open platform, you can do those kinds of things, You can create those layers to remove all of that complexity because if you try to one-off and take on each one of those technologies, whether it's Spark or Flink or whatever's coming, as a product, and a product management organization, and a company, that's really difficult. So the community helps a ton on that, too. >> Donna, when you talk to customers about. You gave a great talk on the roadmap today to give a glimpse of where you guys are headed, your basic philosophy, your architecture, what are they pushing you for? Where are they trying to take you or where are you trying to take them? (laughs) >> (laughs) Hopefully, a little bit of both, right? I think it's being able to take advantage of the kinds of technologies, like you mentioned, that are emerging when they need them, but they also want us to make sure that all of that is really enterprise-ready, you're making it solid. Because we know from history and big data, a lot of those technologies are early, somebody has to get their knees skinned and all that with the first one. So they're really counting on us to really make it solid and quality and take care of all of those intricacies of delivering it in a non-open-source way where you're making it a real commercial product, so I think that's one thing. Then the second piece that we're seeing a lot more of as part of Hitachi we've moved up into the enterprise we also need to think a lot more about monitoring, administration, security, all of the things that go at the base of a pipeline. So, that scenario where they want us to focus. The great thing is, as part of Hitachi Vantara now, those aren't areas that we always had a lot of expertise in but Hitachi does 'cause those are kind of infrastructure-type technologies, so I think the push to do that is really strong and now we'll actually be able to do more of it because we've got that access to the portfolio. >> I don't know if this is a fair question for you, but I'm going to ask it anyway, because you just talked about some of the things Hitachi brings and that you can leverage and it's obvious that a lot of the things that Pentaho brings to Hitachi, the family but one of the things that's not talked about a lot is go-to-market, Hitachi data systems, traditionally don't have a lot of expertise at going to market with developers as the first step, where in your world you start. Has Pentaho been able to bring that cultural aspect to the new entity. >> For us, even though we have the open-source world, that's less of the developer and more of an architect or a CIO or somebody who's looking at that. >> David: Early adopter or. >> More and more it's the Chief Data Officer and that type of a persona. I think that, now that we are a entity, a brand new entity, that's a software-oriented company, we're absolutely going to play a way bigger role in that, because we brought software to market for 13 years. I think we've had early wins, we've had places where we're able to help. In an account, for instance, if you're in the data center, if that's where Hitachi is, if you start to get that partnership and we can start to draw the lines from, okay, who are the people that are now looking at, what's the big data strategy, what's the IOT strategy, where's the CDO. That's where we've had a much better opportunity to get to bigger sales in the enterprise in those global accounts, so I think we'll see more of that. Also there's the whole transformation of Hitachi as well, so I think there'll be a need to have much more of that software experience and also, Hitachi's hired two new executives, one on the sales side from SAP, and one who's now my boss, Brad Surak from GE Digital, so I think there's a lot of good, strong leadership around the software side and, obviously, all of the expertise that the folks at Pentaho have. >> That's interesting, that Chief Data Officer role is emerging as a target for you, we were at an event on Tuesday in Boston, there were about 200 Chief Data Officers there and I think about 25% had a Robotic Process Automation Initiative going on, they didn't ask about IOT just this little piece of IOT and then, Jim, Data Scientists and that whole world is now your world, okay great. Donna Prlich, thanks very much for coming to the Cube. Always a pleasure to see you. >> Donna: Yeah, thank you. >> Okay, Dave Velonte for Jim Kobielus. Keep it right there everybody, this is the Cube. We're live from PentahoWorld 2017 hashtag P-World 17. Brought to you by Hitachi Vantara, we'll be right back. (upbeat techno)

>> Announcer: Live from San Francisco, it's The Cube covering Informatica World 2017. Brought to you by Informatica. >> Okay, welcome back everyone. We are here live in San Francisco for The Cube's exclusive coverage of Informatica World 2017. I'm John Furrier, this is Siliconangle's flagship program, we go out to the events and (he mumbles). My next guest is Jitesh Ghai who's the Vice President General Manager of data quality and governance for Informatica. Welcome to The Cube, thanks for joining us today. >> Happy to be here, John. Pleasure. >> So, two things right out of the gate. One, data quality and governance, two of the hottest topics in the industry, never mind within Informatica. You guys are announcing a lot of stuff, customers are pretty happy, you got a solid customer base. >> That's right. >> Product's been blooming, you got a big brand behind you now. This is important. There's laws now in place coming online in 2018, I think it's the GDPR. >> That's right. >> And there's a variety of other things, but more importantly customers got to get hold of their data. >> That's right. >> What's your take and what are you announcing here at the show? >> Well, you know, from a data governance and compliance and overall quality standpoint, data governance started off as a stick, a threat of regulatory pressure, but really the heart of what it is is effective access to and consumption of data, trusted data. And through that exercise of the threat of a stick, healthy practices have been implemented and that's resulted in an appreciation for data governance as a carrot, as an opportunity to innovate, innovate with your data to develop new business models. The challenge is as this maturation in the practice of data governance has happened there's been a realization that there's a lot of manual work, there's a lot of collaboration that's required across a cross-functional matrixed organization of stakeholders. And there's the concept of ... >> There's some dogma too, let's just face it, within organizations. I got all this data, I did it this way before. >> Right. >> And now, whoa, the pressure's on to make data work, right, I mean that's the big thing. >> That's exactly right. So, you collaborate, you align, and you agree on what data matters and how you govern it. But then you ultimately have to stop documenting your policies but actually make it real, implement it, and that's where the underlying data management stack comes into place. That could be making it real for regulatory, financial regulations, like BCBS 239 and CCAR, where data quality is essential. It could be making it real for security related regulations where protection is essential, like GDPR, the data protection regulation in the EU. And that's where, Informatica is launching a holistic enterprise data governance offering that enables you to not just document it, or as one CDO said to me, "You know, at some point you've got to stop talking about it, "you actually have to do it." To connecting the conceptual, the policies, with the underlying physical systems, which is where intelligent automation with the underlying data management portfolio, the industry-leading data management portfolio that we have, really delivers significant productivity benefits, it's really redefining the practice of data governance. >> Yeah, most people think of data as being one of those things, it's been kind of like, whether it's healthcare, HIPAA old models, it's always been an excuse to say no. "Whoa, we don't do it that way." Or, "Hey." It's kind of become a no-op kind of thing where, "No, we don't want to do any more than data." But you guys introduced CLAIR which is the acronym for the clairvoyant or AI, it's kind of a clever way to brand. >> That's right. >> That's going to bring in machine learning augmented intelligence and cool things. That only, to me, feels like you're speeding things up. >> That's exactly right. >> When in reality governance is more of a slowdown, so how do you blend the innovation strategy of making data freely available ... >> Right. >> ..and yet managing the control layer of governance, because governance wants to go slow, CLAIR wants to go fast, you know. Help me explain that. >> Well, in short, sometimes you have to go slow to go fast. And that's the heart of what our automated intelligence that CLAIR provides in the practice of data governance, is to ensure that people are getting access to, efficient access to trusted data and consuming it in the right context. And that's where you can set, you can define a set of policies, but ultimately you need those policies to connect to the right data assets within the enterprise. And to do that you need to be able to scan an entire enterprise's data sets to understand where all the data is and understand what that data is. >> Talk about the silver bullet that everyone just wants to buy, the answer to the test, which is ungettable, by the way, I believe, we just had Allegis on, one of your customers, and their differentiation to their competition is that they're using data as an asset but they're not going all algorithmic. There's the human data relationship. >> Absolutely. >> So there's really no silver bullet in data. You could use algorithms like machine learning to speed things up and work on things that are repeatal tasks. >> Right. >> Talk about that dynamic because governance can be accelerated with machine learning, I would imagine, right? >> Absolutely, absolutely. Governance is a practice of ensuring an understanding across people, processes and systems. And to do that you need to collaborate and define who are the people, what are your processes, and what are the systems that are most critical to you. Once you've defined that it's, well, how do we connect that to the underlying data assets that matter, and that's where machine learning really helps. Machine learning tells you that if you define customer id as a critical data element, through machine learning, through CLAIR, we are able to surface up everywhere in your organization where customer id resides. It could be cmd id, it could be customer_id, could be customer space id, cust id. Those are all the inferences we can make, the relationships we can make, and surface all of that up so that people have a clear understanding of where all these data assets reside. >> Jitesh, let's take a step back. I want to get your thoughts on this, I really want you to take a minute to explain something for the folks watching. So, there's a couple of different use cases, at least I've observed in a row and the wikibon team has certainly observed. Some people have an older definition of governance. >> Right. >> What's the current definition from your standpoint? What should people know about governance today that's different than just last year or even a few years ago, what's the new picture, what's the new narrative for governance and the impact to business? >> You know, it's a great question. I held a CDO summit in February, we had about 20 Chief Data Officers in New York and I just held an informal survey. "Who implements data governance programs "for regulatory reasons?" Everybody put their hand up. >> Yeah. >> And then I followed that up with, "Who implements data governance programs "to positively affect the top line?" and everybody put their hand up. That's the big transition that's happened in the industry is a realization that data governance is not just about compliance, it's also about effective policies to better understand your data, work with your data, and innovate with your data. Develop new business models, support your business in developing those new business models so that you can positively affect the top line. >> Another question we get up on The Cube all the time, and we also observe, and we've heard this here from other folks at Informatica and your customers have said, getting to know what you actually have is the first step. >> Right. >> Which sounds counter-intuitive but the reality is that a lot of folks realize there's an asset opportunity, they raise their, hey, top line revenue. I mean, who's not going to raise their hand on that one, right, you get fired. I mean, the reality is this train's coming down the tracks pretty fast, data as an input into value creation. >> That's exactly right. >> So now the first step is oh boy, just signed up for that, raise my hand, now what the hell do I have? >> Right. >> How do you react to that? What's your perspective on that? >> That's where you need to be able to, google indexed the internet to make it more consumable. Actually, a few search engines indexed the internet. Google came up with sophistication through its page-ranking algorithm. Similarly, we are cataloging the enterprise and through CLAIR we're making it so that the right relevant information is surfaced to the right practitioner. >> And that's the key. >> That is the key. >> Accelerating the access method, so increase the surface area of data, have the control catalog for the enterprise. >> That's right. >> Which is like your google search analogy. A little harder than searching the internet, but even google's not doing a great job these days, in my opinion, I should say that. But there's so many new data points coming in. >> That's right. >> So now the followup question is, okay, it's really hard when you start having IOT come in. >> That's right. >> Or gesture data or any kind of data coming in. How do you guys deal with that? How does that rock your world, as they say? >> And that's where effective consumption of data permeates across big data, cloud, as well as streaming data. We have implemented, in service to governance, we've implemented in-stream data quality rules to filter out the noise from the signal in sensor data coming in from aircraft subsystems, as an example. That's a means of, well, first you need to understand what are the events that matter, and that's a policy definition exercise which is a governance exercise. And then there's the implementation of filtering events in realtime so that you're only getting the signal and avoiding the noise, that's another IOT example. >> What's your big, take your Informatica hat off, put your kind of industry citizen hat on. >> Mm-hm. >> What's your view of the marketplace right now? What's the big wave that people are riding? Obviously, data, you could say data, don't say data 'cause we know that already. >> Sure. >> What should people, what do you observe out there in the marketplace that's different, that's changing very rapidly? Obviously we see Amazon stock going up like a hockey stick, obviously cloud is there. What are you getting excited about these days? >> You know, what I'm excited about is bringing broad-based access of data to the right users in the right context, and why that's exciting is because there's an appreciation that it's not the analytics that are important, it's the data that fuels those analytics that's important. 'Cause if you're not delivering trusted, accurate data it's effectively a garbage in, garbage out analytics problem. >> Hence the argument, data or algorithms, which one's more important? >> Right. >> I mean data is more important than algorithms 'cause algorithms need data. >> That's exactly right and that's even more true when you get into non-deterministic algorithms and when you get into machine learning. Your machine learning algorithm is only as good as the data you train it with. >> I mean look, machine learning is not a new thing. Unsupervised machine learning's getting better. >> Right. >> But that's really where the compute comes in, and the more data you have the more modeling you can do. These are new areas that are kind of coming online, so the question is, to you, what new exciting areas are energizing some of these old paradigms? We hear neural nets, I mean, google's just announced neural nets that teach neural nets to make machine learning easier for humans. >> Right. >> Okay. I mean, it has a little bit of computer science baseball but you're seeing machine learning now hitting mainstream. >> Right. >> What's the driver for all this? >> The driver for all this comes down to productivity and automation. It's productivity and automation in autonomous vehicles, it's productivity and automation that's now coming into smart homes, it's productivity and automation that is being introduced through data-driven transformation in the enterprise as well, right, that's the driver. >> It's so funny, one of my undergraduate computer science degrees was databases. And in the '80s it wasn't like you went out to the tub, "Hey, I'm a databaser." (He mimics uncertain mumbling) And now it's like the hottest thing, being a data guy. >> Right. >> And what's also interesting is a lot of the computer science programs have been energized by this whole software defined with cloud data because now they have unlimited, potentially, compute power. >> Right. >> What's your view on the young generation coming in as you look to hire and you look to interview people? What are some of the disciplines that are coming out of the universities and the masters programs that are different than it was even five years ago? What are some trends you're seeing in the young kids coming in, what are they gravitating towards? >> Well, you know, there's always an appreciation of, a greater appreciation for, you know, the phrase I love is, "In god we trust, all others must have data." There's an increasing growing culture around being data-driven. But from a background of young people, it's from a variety of backgrounds, of course computer science but philosophy majors, arts majors in general, all in service to the larger cause of making information more accessible, democratizing data, making it more consumable. >> I think AI, I agree, by the way, I would just add, I think AI, although it is hyped and I don't really want to burst that bubble because it's really promoting software. >> Right. >> I mean, AI's giving people a mental model of, "Oh my god, some pretty amazing things are happening." >> Sure. >> I mean, autonomous vehicles is what most people point to and say, "Hey, wow, that's pretty cool." A Tesla's much different than a classic car. I mean, you test-drive a TESLA you go, "Why am I buying BMW, Audi, Mercedes?" >> Right, exactly. >> It's a no brainer. >> Right. >> Except it's like (he mumbles), you got to get it installed. But, again, that's going to change pretty quickly. >> At this point it's becoming a table sticks exercise. If you're not innovating, if you're not applying intelligence and AI, you're not doing it right. >> Right, final question. What's your advice to your customers who are in the trenches, they raise their hand, they're committed to the mandate, they're going down the digital business transformation route, they recognize that data's the center of the value proposition, and they have to rethink and reimagine their businesses. >> Right. >> What advice do you give them in respect to how to think architecturally about data? >> Well, you know, it all starts with your data-driven transformations are only as good as the data that you're driving your transformations with. So, ensure that that's trusted data. Ensure that that's data you agree as an organization upon, not as a functional group, right. The definition of a customer in support is different from the definition of a customer in sales versus marketing. It's incredibly important to have a shared understanding, an alignment on what you are defining and what you're reporting against, because that's how you're running your business. >> So, the old schema concept, the old database world, know your types. >> Right. >> But then you got the unstructured data coming in as well, that's a tsunami IOT coming in. >> Sure, sure. >> That's going to be undefined, right? >> And the goal and the power of AI is to infer and extract metadata and meaning from this whole landscape of semi-structured and unstructured data. >> So you're of the opinion, I'm sure you're biased with being Informatica, but I'm just saying, I'm sure you're in favor of collect everything and connect the dots as you see fit. >> Well ... >> Or is that ...? >> It's a nuance, you can't collect everything but you can collect the metadata of everything. >> Metadata's important. >> Data that describes the data is what makes this achievable and doable, practically implementable. >> Jitesh Ghai here sharing the metadata, we're getting all the metadata from the industry, sharing it with you here on The Cube. I'm John Furrier here live at Informatica World 2017, exclusive Cube coverage, this is our third year. Go to siliconangle.com, check us out there, and also wikibon.com for our great research. Youtube.com/siliconangle for all the videos. More live coverage here at Informatica World in San Francisco after this short break, stay with us.