>>Good morning from New York city, Lisa Martin and John furrier with the cube. We are at AWS summit NYC. This is a series of summits this year, about 15 summit globally. And we're excited to be here, John, with about 10,000 folks. >>It's crowded. New York is packed big showing here at 80 of us summit. So it's super exciting, >>Super exciting. Just a little bit before the keynote. And we have our first guest, Kevin Farley joins us the director of strategic alliances at Maria DB. Kevin, welcome to >>The program. Thank you very much. Appreciate you guys having us. >>So all of us out from California to NYC. Yeah, lots of eyes. We got keynote with Warner Vogels coming up. We should be some good news, hopefully. Yep. But talk to us about Maria DB Skys cloud native version released a couple years ago. What's going on? >>Yeah, well, it's, you know, Skys SQL for us is really a be on the future. I think when we think about like the company's real mission is it's just creating a database for everyone. It's it's any cloud, any scale, um, any size of performance and really making sure that we're able to deliver on something that really kind of takes advantage of everything we've done in the market to date. If you think about it, there's not very many startups that have a billion downloads and 75% of the fortune 500 already using our service. So what we're really thinking about is how do we bridge that gap? How do we create a natural path for all of these customers? And if you think about not just Maria DB, but anyone else using the sequel query language, all the, my people, what I think most Andy jazzy TK, anyone says, you know, it's about 10% of the market currently is in the clouds. That's 90% of a total addressable market that hasn't done it yet. So creating cloud modernization for us, I think is just a huge opportunity. Do >>You guys have a great history with AWS? I want to just step back, you mentioned some stats on, on success. Can you scope the size and track record of Maria DB for us real quick and set the table? Because I think there's a bigger picture going on that we've been tracking for the past 13 years we address is the role of the database has always been one of those things where they didn't believe a one database fits all things, right. You guys have been part of that track record scope, the size and scale of Maria DB, the usage, the use cases and some of the successes. >>Yeah. I mean, like I said, some of the stats are already threw out there. So, you know, it is pervasive, I think is the best way to put it. I think what you look at what the database market really became is very siloed. Right? I think there was a lot of unique solutions that were built and delivered that had promise, but they also had compromise. And I think once you look at the landscape of a lot of fortune 500 companies, they have probably 10 to 15 different database solutions, right? And they're all doing unique things. They're difficult to manage. They're very costly. So what Marie DB is always kind of focused on is how do we continue to build more and more functionality into the database itself and allow that to be a single source of truth where application developers can seamlessly integrate applications. >>So then the theme of this event in New York city, which is scale dot, dot, dot, anything must align quite well with Maria and your >>Objectives. I mean, honestly, I think when I think of the problems that most database, um, companies, um, face customers, I should say it, it really comes down to performance and scale. Most of them like Maria DB, like you said, they it's like the car, you know, and love you've been driving it for years. You're an expert at it. It works great, but it doesn't have enough range. It doesn't go fast enough. It's hitting walls. That modern data requirements are just breaking. So scale for me is the favorite thing to talk about because what we launched as MariaDB expand, which is a plugable storage engine that is integrated into Skye, and it really gives you dynamic scale. So you can scale in, you can scale out, it's not costly compute to try to get for seasonality. So you can make your black Friday numbers. It's really about the dexterity to be able to come in and out as you need in a share, nothing architecture with full failover sale healing, high availability, married to the cloud for full cloud scale. And that's really the beauty of the AWS partnership. >>Can you elaborate a bit more on the partnership? How long have you guys been partners? Where is it now anything exciting coming out? >>Yeah, it it's, it's actually been a wonderful ride. They've really invested from the very beginning we went for the satisfactory. So they really brought a lot of resources to bear. And I think if you're looking at why it works, um, it's probably two things. I think the number one thing is that we share one of the core tenants and it's customer obsession in a, in a, in an environment where there is co-opetition right. You have to find paths for how do you get the best thing for the customer? And the second is pretty obvious, but if you look at any major cloud, their number one priority is getting large mission critical workloads into their cloud because the revenue is exponential on the backside. So what do we own? Large mission critical workloads. So if you marry that objective with AWS, the partnership is absolutely perfect for driving true revenue, growth scale, and, and revenue across, across both entities in the partner ecosystem. >>So Kevin talk about the, um, the hybrid strategy, cuz you're seeing cloud operations. Yep. Go hybrid. Amazon announced AWS announced outpost like four years ago. Right now edge is super hot. Yeah. So you're seeing like most of the enterprise is saying mm-hmm <affirmative> okay. Love cloud love the cloud database, but I got the on-prem hybrid cloud operations. Right. So it's not just proprietary operations. It's cloud ops. Yeah. How do you guys fit into that? What's the story. >>We, we actually it's. I mean, there's, there's all these new deliverables outposts, you know, come out with a promise. What we have is a reality right now, um, one of the largest, um, networking companies, which I can't mention yet publicly, um, we want a really big sky SQL deal, but what they had manufacturing plants, they needed to have on-prem deployments. So Maria DB naturally syncs with sky SQL. It's the same technology. It works in perfect harmony. So we really already deliver on the promise of hybrid, but of course there's a lot more we can grow in that area. And certainly thinking about app posts and other solutions, um, is definitely on the, the longer term roadmap of what could make sense for in our customer. What, >>What are some of the latest things that, that you guys are doing now that you weren't doing a few years ago that customers should know about the audience should know about? >>I mean, I think the game changer, we're always innovating. I mean, when you're the company that writes the code owns the code, you know, we can do hot fixes, we can do security patches, we can always do the things that give you real time access to what you need. But I think the game changer is what I mentioned a little bit earlier. And I think it's really the, the holy grail of the cloud. It's like, how can we take the, the SQL query language, which is well over 50% of the open source market. Right. And how do we convert that seamlessly into the cloud? How do we help you modernize on that journey? And expand gives you the ability to say, I can be the small, I can be a small startup. I got my C round. I don't wanna manage databases. I can use the exact same service as the largest fortune 100 company that has massive global scale and needs to be able to drive that across globe. Yeah. So I think that's the beauty is that it's really a democratization of the database, >>At least that, you know, we've been covering the big data space for 10 years. Remember all those different conversations had do those days and oh, they have big data and right. But then it's like too hard to set up. Then you had that kind of period where you saw a spark and data lakes emerge. Yeah. Then you, now it almost seems, seems like now more than ever, there's a data revolutions back. Right. It was almost like a lull in the, in, in the, in the market a little bit. Yeah. I'm gonna democratize data science right now. You got data. So now it just seems to be an explosion at that level. What's your analysis on that? Because you you've been in, in, in the weeds and in the, in the, in this market for 10 years. Yeah. And nothing really changed. It's just now it's more ready. Yeah. I think what's your observation. Why >>Is that? I think that's a really good question. And I love it cuz I mean, what the promise of things like could do and net new technologies sort of, it was always out there, but it required this whole net new lift and how do I do it? How do I manage it? How do I optimize it? The beauty of what we can do with Maria DB is that sky SQLs, which you already know and love. Right? And now we can Del you can deliver a data lake on S3, right? You can pull that data. And we also have the ability to do both analytical data and transactional data from the same database. So you can write applications that can pull column, store data up into, um, your application, but you can also have all of your asset transactions, which are absolutely required for all of your mission critical business. So I think that we're seeing more and more adoption. You've seen other companies start to talk about bringing the different elements in, but we're the only ones that really >>Do it and SQL standardizing that front end. Yeah. Even better than ever before. All the stuff under the covers is all being connected. >>That's the awesome part is right. Is you're literally doing what you already know how to do, but you blow it out on the back end, married to the cloud. And that I think is the real revolution of what makes usability real in the data space. And I think that's what was always the problem before >>When you're in partner conversations, you mentioned co-opetition. Yeah. <laugh> so I think when you're in partner conversations and customer conversations, there is a lot of the, the there's a lot of competition out there. Absolutely. Everyone's got their own key messages. What are the key differentiators that you're saying AWS Marie to be together better? And here's why, >>Yeah. I, I think that certainly you, you start with the global footprint of AWS, right? So what we rely on the most is having the ability to truly deal with global customers in availability zones, they're gonna optimize performance from them. But then when we look at what we do that really changes the game, it comes down to scale and performance. We actually just ran, um, a suspense test against cockroach that also does distributed sequel. Absolutely. You know, the results were off the chart. So we went public and said, we have an open challenge. Anyone that wants to try to beat, um, expand and Skye will we'll if you can, we'll put $25,000 towards charity. So we really are putting our money where our mouth is on that challenge. So we believe the performance cuz we've seen it and we know it's real, but then it's really always about data scale. Modern data requirements are breaking the mold of charting. They're breaking the mold of all these bandaids that people have put in these traditional services. And we give them future. We, we feature proof their investments, so they can say, Hey, I can start here. But if I end up being a startup that becomes Airbnb, I'm already built to blow it out on the back end. I can already use what I have. >>Speaking of startups, being the next Airbnb. If you look at behind us here, you can see, this is a really packed event in New York city events are back, but the ecosystem here is even flourishing. So Dave and I and Lisa were observing that we're still kind of in a growth mode, big time. So yeah, there's some market forces headwinds for the big unicorns, overfunded, you know, public companies, maybe the valuations are a little bit off, but there's still a surge of new innovations, new companies coming out of this. Um, and it's all around data and scale. It's all around new names. We've never heard of. Absolutely. What's your take on >>Reaction? Well, actually another awesome segues cuz in addition to the public clouds, I manage the ecosystem. And one of the things that we've really been focused on with Skys SQL is making it accessible API accessible. So if you're a company that has a huge Marine DB footprint change data capture might be the most important thing for you to say, we wanna do this, but we want you to stay in sync with our environments. Um, things like monitoring, things like BI, all of these are ecosystem plays and current partners that we have, um, that we really think about how do you holistically look at not only the database and what it can do, but how does it deliver value to different segments of your customer base or just your employee base that are using that stuff? So I think that's huge for us. >>Well, you know, one of the things that we talk often about is that every company, these days, regardless of industry, has to be a data company. Yep. You've gotta be able to access the data glean insights from an act on it quickly, whether it's manufacturing, retail, healthcare, are there any verticals in where Maria DB really excels? >>Um, so certainly we Excel in areas like financial services is huge DBS bank. Um, in APAC, one of our biggest customers, also one of the largest Oracle migrations, probably the, that we've ever done. A lot of people trying to get off Oracle, we make it seamless to get into Maria DB. Um, you can think about Samsung cloud and another, their entire consumer cloud is built on Maria DB, why it's integrated with expand right seasonality. So there's customers like that that really bring it home for us as far as ServiceNow tech sector. Right? So these are all different ones, but I think we're really strong in those >>Areas. So this brings up a good point. Dave and I a coined a term called super cloud at reinvent and Lisa and Dave were at multiple events we're together at events. And so a lot of people are getting behind this cuz it's multi-cloud sounds like something's broken. Yes. But so we call it super cloud because customers are building on top of ecosystems like Maria DB and others. Yeah. Not just AWS SOS does all the CapEx absolutely provide the value. So now people are having this new super cloud moment. We' saying we can get all the benefits of cloud scale mm-hmm <affirmative> without actually being a cloud. Right. So this is where the next gen layer comes. What's your reaction to, to super cloud. Do you think it's a thing? >>Well, I think it's a thing in the sense, from our perspective as an ISV, we're, we're laser focused on making sure that we support any cloud and we have a truly multicloud cloud platform. But the beauty of that as well is from a single UI, you're able to deploy databases in different clouds underneath that you're not looking at so you can have performance proximity, but you're still driving it through the same Skys UI. So for us it's, it's unequivocally true. Got it. And I think it's only ISVs like Maria DB that can deliver on that value because >>You're enabling, >>We're enabling it. Right. We partner, we build on top of everything. Right. So we can access everything underneath >>And they can then build on top of you. >>Sure, exactly. And that's exactly where it goes. Right? Yeah. So that, I think in that sense, the super cloud is actually already somewhat real. >>It's interesting. You look at the old, it spend, you take a big company. I won't say a name, but a leader in a, a vertical, they have such a big spend. Now they can leverage that spend in with the super cloud model. They then could become a service provider in the vertical. Absolutely capital one S doing it. Yeah. You're seeing, um, Goldman Sachs doing it. They have the power on the spend that they're leveraging in for their business and servicing their vertical and the smaller players. Do you see that trend? >>Well, I think that's the reality is that everyone is getting this place where if you're talking about sort of this broader super concept, you're talking about global scale, right? That's if in order to deliver a backbone that can service that model, you have to have the right data structure and the right database footprint to be able to scale. And I think that's what they all need to be able to do. And that's what we're really well positioned with Skys >>To enable companies, as we talked about a minute ago to truly become data companies. Yeah. And to be competitive and to scale on their own, where are your customer conversations? Are they at the C-suite level? Has that changed in the last couple of years? >>Uh, that's actually a really great way to state that question because I think you would've traditionally probably talked more to, um, the DBAs, right? They're the people that are having headaches. They're having problems. They're, they're trying to solve. We see a lot of developers now tons, right? They're thinking about, I have this, I have this new thing that I need to do to deliver this new application. And here's the requirements and the current model's broken. It doesn't optimize that it's a lot of work and it's hard to manage. So I think that we're in a great position to be able to take that to that next phase and deliver. And then of course, as you get deeper in with AWS, you're talking about, you know, CIO level, CISO level, they're they need to understand how do you fit into our larger paradigm. And many of these guys have, you know, hundreds of million dollar commits with AWS. So they think of their investment in the sense of the cloud stack. And we're part of that cloud stack, just like AWS services. So those conversations continue to happen certainly with our larger customers, cuz it truly is married. >>It is. And they continue to evolve. Kevin, thank you so much >>For joining. You're welcome. Great, >>John and me talking about what's going on with Maria >>D. Thank you, John. Thank you, Lisa. On behalf of Maria B, it was wonderful. Really >>Appreciate it. Fantastic as well for John furrier. I'm Lisa Martin. You're watching the cube live from New York city at AWS summit NYC, John and I we're back with our next guest in a minute.

>> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this a CUBE conversation. >> Hello, and welcome to this CUBE conversation, I'm John Furrier, host of theCUBE here, in our Palo Alto, California studios, here with our quarantine crew. We're getting all the remote interviews during this time of COVID-19. We've got two great remote guests here, Rachini Moosavi who's the Executive Director of Analytical Services and Data Governance at UNC Healthcare, and Sonya Jordan, Enterprise Analytics Manager of Data Governance at UNC Health. Welcome to theCUBE, thanks for coming on. >> Thank you. >> Thanks for having us. >> So, I'm super excited. University of North Carolina, my daughter will be a freshman this year, and she is coming, so hopefully she won't have to visit UNC Health, but looking forward to having more visits down there, it's a great place. So, thanks for coming on, really appreciate it. Okay, so the conversation today is going to be about how data and how analytics are helping solve problems, and ultimately, in your case, serve the community, and this is a super important conversation. So, before we get started, talk about UNC Health, what's going on there, how you guys organize, how big is it, what are some of the challenges that you have? >> SO UNC Health is comprised of about 12 different entities within our hospital system. We have physician groups as well as hospitals, and we serve, we're spread throughout all of North Carolina, and so we serve the patients of North Carolina, and that is our primary focus and responsibility for our mission. As part of the offices Sonya and I are in, we are in the Enterprise Analytics and Data Sciences Office that serves all of those entities and so we are centrally located in the triangle area of North Carolina, which is pretty central to the state, and we serve all of our entities equally from our Analytics and Data Governance needs. >> John: You guys got a different customer base, obviously you've got the clinical support, and you got the business applications, you got to be agile, that's what it's all about today, you don't need to rely on IT support. How do you guys do that? What's the framework? How do you guys tackle that problem of being agile, having the data be available, and you got two different customers, you got all the compliance issues with clinical, I can only imagine all the regulations involved, and you've got the business applications. How do you handle those? >> Yeah, so for us in the roles that we are in, we are fully responsible for more of the data and analytics needs of the organization, and so we provide services that truly are balanced across our clinician group, so we have physicians, and nurses, and all of the other ancillary clinical staff that we support, as well as the operational needs as well, so revenue cycle, finance, pharmacy, any of those groups that are required in order to run a healthcare system. So, we balance our time amongst all of those and for the work that we take on and how we continuously support them is really based on governance at the end of the day. How we make decisions around what the priorities are and what needs to happen next, and requires the best insights, is really how we focus on what work we do next. As for the applications that we build, in our office, we truly only build analytical applications or products like visualizations within Tableau as well as we support data governance platforms and services and so we provide some of the tools that enable our end users to be able to interact with the information that we're providing around analytics and insights, at the end of the day. >> Sonya, what's your job? Your title is Analytics Manager of Data Governance, obviously that sounds broad but governance is obviously required in all things. What is your job, what is your day-to-day roles like? What's your focus? >> Well, my day-to-day operations is first around building a data governance program. I try to work with identifying customers who we can start partnering with so that we can start getting documentation and utilizing a lot of the programs that we currently have, such as certification, so when we talk about initiatives, this is one of the initiatives that we use to partner with our stakeholders in order to start bringing visibilities to the various assets, such as metrics, or universes that we want to certify, or dashboards, algorithm, just various lists of different types of assets that we certify that we like to partner with the customers in order for them to start documenting within the tools, so that we can bring visibility to what's available, really focusing on data literacy, helping people to understand what assets are available, not only what assets are available, but who owns them, and who own the asset, and what can they do with it, making sure that we have great documentation in order to be able to leverage literacy as well. >> So, I can only imagine with how much volume you guys are dealing from a data standpoint, and the diversity, that the data warehouse must be massive, or it must be architected in a way that it can be agile because the needs, of the diverse needs. Can you guys share your thoughts on how you guys look on the data warehouse challenge and opportunity, and what you guys are currently doing? >> Well, so- >> Yeah you go ahead, Rachini. >> Go ahead, Sonya. >> Well, last year we implemented a tool, an enterprise warehouse, basically behind a tool that we implemented, and that was an opportunity for Data Governance to really lay some foundation and really bring visibility to the work that we could provide for the enterprise. We were able to embed into probably about six or seven of the 13 initiatives, I was actually within that project, and with that we were able to develop our stewardship committee, our data governance council, and because Rachini managed Data Solutions, our data solution manager was able to really help with the architect and integration of the tools. >> Rachini, your thoughts on running the data warehouse, because you've got to have flexibility for new types of data sources. How do you look at that? >> So, as Sonya just mentioned, we upgraded our data warehouse platform just recently because of these evolving needs, and like a lot of healthcare providers out there, a lot of them are either one or the other EMRs that are top in the market. With our EMR, they provide their own data warehouse, so you have to factor almost the impact of what they bring to the table in with an addition to all of those other sources of data that you're trying to co-mingle and bring together into the same data warehouse, and so for us, it was time for us to evolve our data warehouse. We ended up deciding on trying to create a virtual data warehouse, and in doing so, with virtualization, we had to upgrade our platform, which is what created that opportunity that Sonya was mentioning. And by moving to this new platform we are now able to bring all of that into one space and it's enabled us to think about how does the community of analysts interact with the data? How do we make that available to them in a secure way? In a way that they can take advantage of reusable master data files that could be our source of truth within our data warehouse, while also being able to have the flexibility to build what they need in their own functional spaces so that they can get the wealth of information that they need out of the same source and it's available to everyone. >> Okay, so I got to ask the question, and I was trying to get the good stuff out first, but let's get at the reality of COVID-19. You got pre-COVID-19 pandemic, we're kind of in the middle of it, and people are looking at strategies to come out of it, obviously the world will be changed, higher with a lot of virtualization, virtual meetings, and virtual workforce, but the data still needs to be, the business still needs to run, but data will be changing different sources, how are you guys responding to that crisis because you're going to be leaned on heavily for more and more support? >> Yeah it's been non-stop since March (laughs). So, I'm going to tell you about the reporting aspects of it, and then I'd love to turn it over to Sonya to tell you about some of the great things that we've actually been able to do to it and enhance our data governance program by not wasting this terrible event and this opportunity that's come up. So, with COVID, when it kicked off back in March, we actually formed a war room to address the needs around reporting analytics and just insights that our executives needed, and so in doing so, we created within the first week, our first weekend actually, our first dashboard, and within the next two weeks we had about eight or nine other dashboards that were available. And we continuously add to that. Information is so critical to our executives, to our clinicians, to be able to know how to address the evolving needs of COVID-19 and how we need to respond. We literally, and I'm not even exaggerating, at this very moment we have probably, let's see, I think it's seven different forecasts that we're trying to build all at the same time to try and help us prepare for this new recovery, this sort of ramp up efforts, so to your point, it started off as we're shutting down so that we can flatten the curve, but now as we try to also reopen at the same time while we're still meeting the needs of our COVID patients, there's this balancing act that we're trying to keep up with and so analytics is playing a critical factor in doing that. >> Sonya, your thoughts. First of all, congratulations, and action is what defines the players from the pretenders in my mind, you're seeing that play out, so congratulations for taking great action, I know you're working hard. Sonya, your thoughts, COVID, it's putting a lot of pressure? It highlights the weaknesses and strengths of what's kind of out there, what's your thoughts? >> Well, it just requires a great deal of collaboration and making sure that you're documenting metrics in a way where you're factoring true definition because at the end of the day, this information can go into a dashboard that's going to be visualized across the organization, I think what COVID has done was really enhanced the need and the understanding of why data governance is important and also it has allowed us to create a lot of standardization, where we we're standardizing a lot of processes that we currently had in correct place but just enhancing them. >> You know, not to go on a tangent, but I will, it's funny how the reality has kind of pulled back, exposed a lot of things, whether it's the remote work situation, people are VPNing, not under provision with the IT side. On the data side, everyone now understands the quality of the data. I mean, I got my kids talking progression analysis, "Oh, the curves are all wrong," I mean people are now seeing the science behind the data and they're looking at graphs all the time, you guys are in the visualization piece, this really highlights the need of data as a story, because there's an impact, and two, quality data. And if you don't have the data, the story isn't being told and then misinformation comes out of it, and this is actually playing out in real time, so it's not like it's just a use case for the most analytics but this again highlights the value of proposition of what you guys do. What's your personal thoughts on all this because this really is playing out globally. >> Yeah, it's been amazing how much information is out there. So, we have been extremely blessed at times but also burdened at times by that amount of information. So, there's the data that's going through our healthcare system that we're trying to manage and wrangle and do that data storytelling so that people can drive those insights to very effective decisions. But there's also all of this external data that we're trying to be able to leverage as well. And this is where the whole sharing of information can sometimes become really hard to try and get ahead of, we leverage the Johns Hopkins data for some time, but even that, too, can have some hiccups in terms of what's available. We try to use our State Department of Health and Human Services data and they just about updated their website and how information was being shared every other week and it was making it impossible for us to ingest that into our dashboards that we were providing, and so there's really great opportunities but also risks in some of the information that we're pulling. >> Sonya, what's your thoughts? I was just having a conversation this morning with the Chief of Analytics and Insight from NOA which is the National Oceanic Administration, about weather data and forecasting weather, and they've got this community model where they're trying to get the edges to kind of come in, this teases out a template. You guys have multiple locations. As you get more democratized in the connection points, whether it's third-party data, having a system managing that is hard, and again, this is a new trend that's emerging, this community connection points, where I think you guys might also might be a template, and your multiple locations, what's your general thoughts on that because the data's coming in, it's now connected in, whether it's first-party to the healthcare system or third-party. >> Yeah, well we have been leveraging our data governance tool to try to get that centralized location, making sure that we obtain the documentations. Due to COVID, everything is moving very fast, so it requires us to really sit down and capture the information and when you don't have enough resources in order to do that, it's easy to miss some very important information, so really trying to encourage people to understand the reason why we have data governance tools in order for them to leverage, in order to capture the documentation in a way that it can tell the story about the data, but most of all, to be able to capture it in a way so that if that person happened to leave the organization, we're not spending a lot of time trying to figure out how was this information created, how was this dashboard designed, where are the requirements, where are the specifications, where are the key elements, where does that information live, and making sure we capture that up front. >> So, guys, you guys are using Informatica, how are they helping you? Obviously, they have a system they're getting some great feedback on, how are you using Informatica, how is it going, and how has that enabled you guys to be successful? >> Yeah, so we decided on Informatica after doing a really thorough vetting of all of the other vendors in the industry that could provide us these services. We've really loved the capabilities that we've been able to provide to our customers at this point. It's evolving, I think, for us, the ability to partner with a group like Prominence, to be able to really leverage the capabilities of Informatica and then be really super, super hyper focused on providing data literacy back to our end users and making that the full intent of what we're doing within data governance has really enabled us to take the tools and make it something that's specific to UNC Health and the needs that our end users are verbalizing and provide that to them in a very positive way. >> Sonya, they talk about this master catalog, and I've talked to the CEO of Informatica and all their leaders, governance is a big part of it, and I've always said, I've always kind of had a hard time, I'm an entrepreneur, I like to innovate, move fast, break things, which is kind of not the way you work in the data world, you don't want to be breaking anything, so how do you balance governance and compliance with innovation? This has been a key topic and I know that you guys are using their enterprise data catolog. Is that helping? How does that fit in, is that part of it? >> Well, yeah, so during our COVID initiatives and building these telos dashboards, these visualizations and forecast models for executive leaders, we were able to document and EMPower you, which we rebranded Axon to EMPower, we were able to document a lot of our dashboards, which is a data set, and pretty much document attributes and show lineage from EMPower to EDC, so that users would know exactly when they start looking at the visualization not only what does this information mean, but they're also able to see what other sources that that information impacts as well as the data lineage, where did the information come from in EDC. >> So I got to ask the question to kind of wrap things up, has Informatica helped you guys out now that you're in this crisis? Obviously you've implemented before, now that you're in the middle of it, have you seen any things that jumped out at you that's been helpful, and are there areas that need to be worked on so that you guys continue to fight the good fight, come out of this thing stronger than before you came in? >> Yeah, there is a lot of new information, what we consider as "aha" moments that we've been learning about, and how EMPower, yes there's definitely a learning curve because we implemented EDC and EMPower last year doing our warehouse implementation, and so there's a lot of work that still needs to be done, but based on where we were the first of the year, I can say we have evolved tremendously due to a lot of the pandemic issues that arised, and we're looking to really evolve even greater, and pilot across the entire organization so that they can start leveraging these tools for their needs. >> Rachini you got any thoughts on your end on what's worked, what you see improvements coming, anything to share? >> Yeah, so we're excited about some of the new capabilities like the marketplace for example that's available in Axon, we're looking forward to being able to take advantage of some of these great new aspects of the tool so that we can really focus more on providing those insights back to our end users. I think for us, during COVID, it's really been about how do we take advantage of the immediate needs that are surfacing. How do we build all of these dashboards in record-breaking time but also make sure that folks understand exactly what's being represented within those dashboards, and so being able to provide that through our Informatica tools and service it back to our end users, almost in a seamless way like it's built into our dashboards, has been a really critical factor for us, and feeling like we can provide that level of transparency, and so I think that's where as we evolve that we would look for more opportunities, too. How do we make it simple for people to get that immediate answers to their questions, of what does the information need without it feeling like they're going elsewhere for the information. >> Rachini, thank you so much for your insight, Sonya as well, thanks for the insight, and stay safe. Sonya, behind you, I was pointing out, that's your artwork, you painted that picture. >> Yes. >> Looks beautiful. >> Yes, I did. >> You got two jobs, you're an artist, and you're doing data governance. >> Yes, I am, and I enjoy painting, that's how I relax (laughs). >> Looks great, get that on the market soon, get that on the marketplace, let's get that going. Appreciate the time, thank you so much for the insights, and stay safe and again, congratulations on the hard work you're doing, I know there's still a lot more to do, thanks for your time, appreciate it. >> Thank you. >> Thank you. >> It's theCUBE conversation, I'm John Furrier at the Palo Alto studios, for the remote interviews with Informatica, I'm John Furrier, thanks for watching. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with it's ecosystem partners. >> Welcome inside Live here at the Sands as we continue our coverage of AWS re:Invent 2019 on theCUBE, day three. Always an exciting time I think to get a summary of what's happened here. Dave Vellante, John Walls, we're joined by a couple of gentlemen from Immuta, Steven Touw who's a co-founder and CTO. Steve, good to see you. >> Yeah thanks for having me. >> John Walls: And Rob Lancaster, who's the GM of Cloud at Immuta. Rob, thanks for joining us as well. >> Great to be here. >> First off, let's talk about Immuta a little bit. You're all about governance right? You're trying to make it simple, easy, taking out the complexity. But for those at home who might not be too familiar with your company, tell us a little bit about you. >> Yeah so the company started out, our roots are in the U.S. intelligence community. So we had been dealing with access and control issues for data for years and we said to ourselves, "Hey this product has to be useful for non-IC customers. "This problem has to exist." And with the advent of all these privacy regulations like CCPA, GDPR and of course HIPPA's been around for a long time, really our goal was to bring a product to the market that makes it easy to govern access to data in a way that you don't have to be technical to do it, you don't have to understand how to write SQL statements, you don't have to be a system administrator. We really bring together three personas, the users that want to get access to the data, legal compliance that needs to understand how the rules are being enforced or even enforce them themselves, and then of course the data owners and the DBAs who need to expose the data. So usually those three personas are at odds with one another, we bring them together in our platform and allow them to work together in a way that's compliant and also accelerates their data analytics. >> Could we talk a little bit about why this is such a problem? Because it is a big problem and especially today and in the cloud and we'll get into that, but you've got data lakes, data oceans now, you got data coming in, all types of data. Might be internal transaction data, it might be stuff in your data warehouse. And the organization say, "Well I want some other data. "I want to bring in maybe some social data." So certain data is, everybody can have access to. Certain data not everybody can have access to. And it's not necessarily just a security problem, edicts of my organization that need to be enforced. So first of all, is that sort of, the problem that you're solving? And maybe you can double-click on that a little bit. >> Yeah sure, so the market has evolved and is evolving. You allude to data lakes, I think you can point to the immersion of Hadoop, as a distributed infrastructure as kind of the original data lakes, or the most recent data lakes, where you can store all your data and run analytics on all your data, and now with the advent, with the emergence of Cloud you've effectively got very low, if not zero cost storage, and the ability to throw an unlimited amount of compute at the data. That, kind of in conjunction with heightened awareness for consumer data privacy and risk associated with data, has created a market for data governance beyond kind of the course-grained access controls that people have been using on their databases for decades now. >> Yeah I mean Hadoop really got it all started. You're right and despite all it's problems, it had some real epiphany-like technical innovations, but one of the things that it didn't worry about at the time was governance. So whose responsibility is this? Is it the CISO? That is essentially trying to build out a new cloud stack to provide security, privacy, governance and what does that stack look like? >> Rob: Go ahead. >> Yeah so it depends, it's actually pretty interesting that different organizations have tackled this different ways. So we have CISOs that maintain this. In other organizations we've got the legal compliance teams that want to do this but maybe don't have the technical chops. And the CISO doesn't necessarily know all the privacy rules that need to be enforced, so it's kind of moving into this world where security is about keeping the bad guys out and black or white access, like you either can see the data or you won't, but with privacy controls it gets into this gray area where there's a lot of technical complexity and there's a lot of legal complexity. So the organizations struggle with this 'cause you've got to play in that gray area where it's not just like I said, black and white. The analogy we use is, security is like a light switch, you're either in or you're out. With privacy controls you need to anonymize the data, you need to do privacy by design. It's like a dimmer switch where you want to play in that gray area and allow some utility out of the data but also protect privacy at differing levels of whatever you're doing analytically. So this can be challenging for an organization to wrestle with because it's not as, I would argue it's not as black and white as it is with security. >> Your question is in many cases it's the business that's running really fast and that is building these data lakes because they want to get value out of their data and the CISO or the compliance or risk officers are the ones that are telling them to slow down. So our product that Steve set up caters to both parties. It checks the boxes for risk, but it also enable the business to get utility out of their data lake. >> It's a very complicated situation because you've got this corpus of data that's organic and constantly changing and you have, you mentioned GDPR, you've got California now, every state's going to have it's own regulations so you've got to be able to sort of adjudicate that. And can you talk about, I mean obviously I've interviewed Matt Carroll, we covered you guys so I know a little bit about you, but can you talk about your tech in terms of it's ability? You've got a capability to do really granular level understanding and governance policies, can you describe that a little bit? >> Yeah sure, so when we talk about privacy controls, these are things like way beyond just table-level access. So instead of saying, "Hey you have access to this table or not," or even, "You have access to this column or not," you've got to go deeper than that, you've got to be able to make rows disappear based on what people are doing. So for example, we have financial institution customers that are using us for all their trading data and only some traders can see some trade desks and we manage all that dynamically. We're not making anonymized copies of data. Everything happens at query time, and depending on what compute you're using that all works differently, but then at the column level we're able to do these anonymization techniques like we could make numeric data less specific, we could use techniques like k-anonymization that allows analysts to analyze the data but ensures that small groups that exist in that data won't reveal someone's true identity. And we have techniques like differential privacy, which provides mathematical guarantees of privacy. So for example, one of our manufacturing customers set aside, these are the four analytical use cases that we're using our data for and under GDPR we want different levels of privacy associated to those use cases. So they could do that all with Immuta. So they could say, "When I'm doing this "I want these columns to be anonymized to this level "and these rows to disappear, but if I'm doing something, "maybe more critical, which our consumers have consented to "you know there's less privacy controls." And that all happens dynamically so the analysts could actually switch context of what they're doing and get a different view of the data and all of that is audited so we understand why someone's doing what they're doing and when they're running queries we can associate those queries to purpose. >> We've talked about customers of course and they're adapting right, to a new world? How are you adapting? I mean what are you learning about, in terms of policy regulation and governance, what have you, you said you came out of the intelligence community, high bar there right? >> Steven Touw: Yeah. >> So what have you done to evolve as a company and what are you, as the headlights basically for these folks, what are you seeing change that is going to require a lot of shift on the other side? >> Yeah so, I don't know if you have thoughts. >> I mean it's a great question but there's really two parts to it, there's what are we doing? But, what is the market doing as well, right? So if you think about when we got started, even a year ago people understood the technology, they thought it was cool but maybe a little nichey for government or financial services or maybe healthcare because there's well understood regulation, these vertical regulation. Even over the past year with kind of this increasing or heightened awareness for consumer data privacy, not just driven by CCPA and GDPR but kind of this, call it the Facebook Effect right? Cambridge Analytica has created this awareness within the general population for what are these organizations actually doing with my data? Before it was okay 'cause you give your data to Google and you get a better search result and you're okay with that but now they may be using your data for their own profit in different ways so this has created this rising tides effect for the overall market and we talk a lot about organizations using something like Immuta to protect their highly sensitive data. I like to think of it is their most valuable data, which may be highly sensitive but it also could be the crown jewels, trading data for a bank for example. So it's become about extracting value and operational benefit from data, whereas the risk offices are trying to lock it down in many cases. >> So, there's definitely a big problem and people are becoming more aware of it. I want to talk about where you guys fit into this whole cloud ecosystem. There's a sea change now, there's this sort of, this new cloud coming into play. It's not just about infrastructure anymore. I'll give you some examples, you got all these data lakes, maybe you got Redshift running, Snowflake's another one, you've now got this data exchange where you can bring data right in the Cloud bring in all different types of data, you're bringing in some AML and AI and it's all, really again, a complicated situation. So I see you guys as fitting in there and real need but can you describe where you fit in the ecosystem, what your relationship is with AWS, how do I engage with you? >> Yeah absolutely, so a core part of our value is that we are heterogeneous in terms of the environment that we support. We support a hybrid estate so the architecture of the product is fully microservices based so we can run on PRIM as well as on Cloud, on any Cloud, we support effectively any popular database system or analytical tool. So think of us as a data abstraction layer across a hybrid environment, so we're here because AWS is obviously the big boy in the market, they have market share, this is a strategic relationship for us. We're working very deeply with AWS field teams, particularly around some of their verticals, the verticals that align to our business and at the end of the day we're trying to define a category. It's a similar category that we've had for decades but with all the changes that are happening in data and regulation and infrastructure what we're trying to do is raise the level of awareness for the fact that Immuta has actually solved the problem that many of these risk officers are struggling with today. >> Yeah and from a, diving a little on the technical side of that answer is that we are, think of us as the way to enforce policy in the Cloud. We consider ourselves a Cloud-first software vendor. And you don't necessarily want one point solution in Redshift or another point solution on your on-premise Cloudera instance, whatever it may be where you're using your data and running analytics, you need to abstract the policies out into a consistent layer and then have them be enforced across whatever you're using. So you might be using Cloudera today and then you switch to Databricks tomorrow, that shouldn't be a hard change from you from a policy perspective. You just re-point Immuta at Databricks and all your policies are still working like they used to so it gives you this flexibility now to use all these different services that AWS provides 'cause as was stated in the keynote on Tuesday, there's no one database solves all. You're always going to be using a heterogenous set of compute to do your job in analytics so you need a consistent way to enforce policies across all of that. >> That's a great point. I mean I don't know if you saw the Vanguard guy today in the keynote, he basically said, "We rip down, or tore down our big data infrastructure "moved it to the Cloud, spun up EMR." I mean there's a perfect example of, you got to bring your governance with you. You can't have to rebuild that whole stack. Are you in the Marketplace yet? >> Steve and Rob: Yes. >> You are, great, awesome. >> Yeah we launched a managed version of Immuta over the summer on AWS Marketplace. We'll be launching a second one shortly and it's really, the offering that we have out there is really geared toward, for lack of a better term, democratizing data governance. It's actually free up to the fifth user so any organization can deploy Immuta in under 30 minutes through Marketplace and start protecting their data. >> That's great, we had Dave McCann on yesterday, he runs the Marketplace, he was telling us just now, private offers for every marketplace, so ICV, so that's from. Last question I have is, how do you see this all playing out? You got GDPR, remember you talked about California regulations, there's a technology component, any predictions you guys want to share? What's your telescope say? >> All data will be regulated data eventually. So if you're not thinking about that now you need to. So, at least that's our theory, obviously, so we think it's critical that you're doing that from day one instead of day 365 and in your migration strategy. And if you're not thinking about that it's going to potentially bite you in the ass. >> Yeah you're right, I mean Web 2.0 was the wild, wild west, there was no privacy, there was no regulation, GDPR started to get people focused on that and it's now a whole new world. >> Gentlemen thank you, appreciate the time and best of luck. I know you said you had the big launch this summer but good things are ahead no doubt. >> For sure, thank you. >> Thank you. >> Dave Vellante: Thanks guys. >> Back with more coverage here on theCUBE. You're watching AWS re:Invent 2019. We are live and we're in Las Vegas. (upbeat tones)

>> live from Las Vegas. It's the queue covering Inform Attica! World 2019. Brought to you by in from Attica. >> Welcome back, everyone to the cubes. Coverage of Infra Matic. A world here in Las Vegas. I'm your host, Rebecca Knight. I'm doing by two guests. For the segment we have Rick Tam Daniels. He is the VP. Strategic ecosystems and technology than from Attica. Welcome, Rick and Yoav. Enough! He is the VP product for Giga Space. Welcome >> to be here. >> So this is a fun segment. You are the winner of the infirm Attica World 2,019 Solution Expo Cloud and Innovation. I want to get to you in a second and hear all about Giga Space. But I want to start with you. Rick, talk a little bit about this award and about the genesis of it. Where did the idea come from? >> Yes, So one of the things we really wanted to do it in from Attica World this year is create address Some of the most important topics that the customers want to hear about. It's a cloud and I two of the hottest tops the industry every wants to know about it and We wanted to take a lot of our emerging partners there doing some very innovative things than from Attica technology and put them front center. So if you look at the Expo Hall floor right in the middle, we have this almost like an art gallery of all this cool innovation have going on around the inn from Attica. Technology on the idea was that we had attendees come in and actually review the solutions. They had to be really full demos for working demos. Andi could vote on the app. They could say what their favorites were, and the end result is happy announced. Giga Spaces are big winner. >> And so yeah, attendees would vote on the app and so get so big a space. Tell us about it. You're based in Israel. >> Yeah, so aren't is based in Israel or H Q is in New York. Basically, the biggest bass was we've been in the market for more than a decade, deployed like in the largest enterprise in the world. You like banks like Bank of America, like international. I ot like another electric largest airline, largest railway companies, and basically we provide the speed for the application and big data infer structures so they deploy, like real time use cases like fraud detection, economic pricing, predictive maintenance, all those different types of services that required the speed on the big data side. >> You're all about speed, >> all about spirit. If you need the speed, we're the provided for you. >> Well, that's that's very exciting. So talk a little bit about the conversations that you were having with some of the attendees. What kinds of questions were you getting? >> So I think a lot of customers, during customers of ours and informative are talking about the move from kind of historical analysis to more proactive, event driven analytics when you want to be able to instead of interact with the data you want today, one so and now you want to baby toe Dr Analytical on the moment as soon as it happened to provide it that burrito Theron your online processes and instead of kind of offline processes. So, for example, fraud detection, which is the most, is the example. You want to be able to 100 further analysis on on the payment of a soon as it happens and Emilie second level and not like a few seconds after the transaction was over. So it's again. We're talking about the speed. They're very to handle high or amount of data with related sub second response time. >> And how are you using in from Attica? >> Cool So well, We've been working lately with Informatica very tightly with both their product team, and there are in the team because Israel, India, the US, on integrating with some of their different products were basically we've built kind of what Gardner calls the digital integration hub. It's like the next Jan big data architecture, which provides you both. Informatica side that allows to ingest any type of data could be taxed logs, transaction payments, anything you have together with their medal, the meta data management and on top of it, using Giga spaces for the real Time analytics and the high performance in speed. >> So, Rick, I know that this was attendee chosen, so there's no rigging here, but I'd love to hear what your thoughts are in Giga Space in terms of the innovations that they're doing in these in these very important problems, like fraud detection and predictive maintenance, these air these air big problems. That company's heir really wrestling with. >> And I think what's exciting about the solution they had. It was a great business case, right? I think that really resonated. Attendees looking at Everyone can identify with Fraud Analytics. Everyone's unfortunately, probably on a victim of it, so they could see how it works. I think it also focuses on the aspect of a iva. How do operationalized a I? So is the whole model building piece of it, And Infra Matic has a strong player there as well. But now you say, Well, let's actually have the model we need to execute quickly. How do we do that? You know what the biggest spaces technology, but also combine it with the right historical context, right to make the right decisions. So they're really does hit on. How do you actually take a I and make it a real thing? >> And the other important part is the business case in what you were just saying in terms of if a if a customer is the victim of fraud here, she blames the institution, not the hacker on. And if there's a problem with with an airline maintenance problem, you blame the airline. Of course not the faulty problems that it was having. So so I think that that also really shows what what's in the future. What are you seeing? Kind of Mohr innovations that you want to add to the biggest space platform. So >> I think we're working to their lot about like Rick was mentioning about operationalize ing A. I so a lot of challenge today off moving from the research development training part of Day I or the machine anymore to move to production. Let's say you're a payment provider you have the more than you can detect fraud, but your ability for you to run it on millions of transactions a second in a sub lets a few millisecond level. That's the biggest challenge. And if you do it in there a few seconds after the transaction was over, then the you know the last of the fraud or the wire was already happened. So again, the operation was part of taking your more than formula that sound flat from putting in production with the scale of the ingestion rate low latest c you know, scaling on pick events like Black Friday or Cyber Monday. That's the biggest challenges on the production systems. >> Now the speed is of the essence. Rick, this has been a successful experiment trying this. What are you hearing from attendees? Did they like it where they sort of How do we Dad? Does this work? What is this about? >> I think they're really enjoyed it. Every time I look, I went over to the zone. It was full of people having deep conversations, really getting into the technology and understanding. Because as I mentioned these air topics that I think everyone came here to the show to really learn more about How are they going to get where they're going There, Cloud journey where they're going to go in there, eh? I journey. It's a great feedback from attendees. Lot of active participation. So I'm going >> to do it. We're going to see it in >> your batter. It's gonna be great. >> So now that you're the winner, you're going to be up there on the main stage, getting some recognition. That's exciting. What? What are you going to take back? Teo, I know you're based in both Israel and New York. What? What? What does this mean for your company? >> So I think the next step is taking it to the business side. Right? We want to make sure that the joint offering and the joy in partnership moves to the next stage taking it to the next customer. We have some joint customer. We have some new prospect. Were a lot of late from the show here, sitting next to me, sitting side by side with the other partners of Info Matic. I like data breaks and slow flaked and clothes are so we have a lot of joint offering and solving real time like business and off the largest, most challenging enterprise we have, like, you know, largest banks, largest airlines, largest like railways companies. So I think the next step is moving, taking it from the exhibition to the field. >> Great. Well, this is terrific. Congratulations. Once again. Really exciting. Really happy for you. Thanks so much for coming on the show. Thank you. You have been watching the cubes live coverage of in from Attica, World 2019. I'm Rebecca night. Stay tuned

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to the special. Keep conversation here in Palo Alto, California. I'm John for a co host of the Cube. Were Arsenal was a C I S O. C. So for Microsoft also corporate vice President, Chief information security. Thanks for joining me today. >> Thank you. >> Appreciate it. Thanks. So you have a really big job. You're a warrior in the industry, security is the hardest job on the planet. >> And hang in sight >> of every skirt. Officer is so hard. Tell us about the role of Microsoft. You have overlooked the entire thing. You report to the board, give us an overview of what >> happens. Yeah. I >> mean, it's you know, obviously we're pretty busy. Ah, in this world we have today with a lot of adversaries going on, an operational issues happening. And so I have responsibility. Accountability for obviously protecting Microsoft assets are customer assets. And then ah, And for me, with the trend also responsibility for business continuity Disaster recovery company >> on the sea. So job has been evolving. We're talking before the camera came on that it's coming to CEO CF roll years ago involved to a business leader. Where is the sea? So roll now in your industry is our is a formal title is it establishes their clear lines of reporting. How's it evolved? What's the current state of the market in terms of the sea? So it's roll? >> Yeah, the role is involved. A lot. Like you said, I think like the CIA or twenty years ago, you know, start from the back room of the front room and I think the, you know, one of things I look at in the role is it's really made it before things. There's technical architecture, there's business enablement. There's operational expert excellence. And then there's risk management and the older ah, what does find the right word? But the early see so model was really about the technical architecture. Today. It's really a blend of those four things. How do you enable your business to move forward? How do you take calculated risks or manage risks? And then how do you do it really effectively and efficiently, which is really a new suit and you look at them. You'LL see people evolving to those four functions. >> And who's your boss? Would you report to >> I report to a gentleman by the name of a curtain. Little Benny on DH. He is the chief digital officer, which would be a combination of Seo did officer and transformation as well as all of Microsoft corporate strategy >> and this broad board visibility, actually in security. >> Yeah, you >> guys, how is Microsoft evolved? You've been with the company for a long time >> in the >> old days ahead perimeters, and we talk about on the Cube all the time. When a criminalist environment. Now there's no perimeter. Yeah, the world's changed. How is Microsoft evolved? Its its view on security Has it evolved from central groups to decentralize? How is it how how was it managed? What's the what's the current state of the art for security organization? >> Well, I think that, you know, you raise a good point, though things have changed. And so in this idea, where there is this, you know, perimeter and you demanded everything through the network that was great. But in a client to cloak cloud world, we have today with mobile devices and proliferation or cloud services, and I ot the model just doesn't work anymore. So we sort of simplified it down into Well, we should go with this, you know, people calls your trust, I refer to It is just don't talk to strangers. But the idea being is this really so simplified, which is you've got to have a good identity, strong identity to participate. You have to have managed in healthy device to participate, to talk to, ah, Microsoft Asset. And then you have to have data in telemetry that surrounds that all the time. And so you basically have a trust, trust and then verify model between those three things. And that's really the fundamental. It's really that simple. >> David Lava as Pascal senior with twenty twelve when he was M. C before he was the C E O. V M. Where he said, You know his security do over and he was like, Yes, it's going to be a do over its opportunity. What's your thoughts on that perspective? Has there been a do over? Is it to do over our people looking at security and a whole new way? What's your thoughts? >> Yeah, I mean, I've been around security for a long time, and it's there's obviously changes in Massa nations that happened obviously, at Microsoft. At one point we had a security division. I was the CTO in that division, and we really thought the better way to do it was make security baked in all the products that we do. Everything has security baked in. And so we step back and really change the way we thought about it. To make it easier for developers for end users for admin, that is just a holistic part of the experience. So again, the technology really should disappear. If you really want to be affected, I think >> don't make it a happy thought. Make it baked in from Day one on new product development and new opportunity. >> Yeah, basically, shift the whole thing left. Put it right in from the beginning. And so then, therefore, it's a better experience for everyone using it. >> So one of things we've observed over the past ten years of doing the Cube when do first rolled up with scene, you know, big data role of date has been critical, and I think one of the things that's interesting is, as you get data into the system, you can use day that contextually and look at the contextual behavioral data. It's really is create some visibility into things you, Meyer may not have seen before. Your thoughts and reaction to the concept of leveraging data because you guys get a lot of data. How do you leverage the data? What's the view of data? New data will make things different. Different perspectives creates more visibility. Is that the right view? What's your thoughts on the role of Data World Data plays? >> Well, they're gonna say, You know, we had this idea. There's identity, there's device. And then there's the data telemetry. That platform becomes everything we do, what there's just security and are anomalous behavior like you were talking about. It is how do we improve the user experience all the way through? And so we use it to the service health indicator as well. I think the one thing we've learned, though, is I was building where the biggest data repositories your head for some time. Like we look at about a six point five trillion different security events a day in any given day, and so sort of. How do you filter through that? Manage? That's pretty amazing, says six point five trillion >> per day >> events per day as >> coming into Microsoft's >> that we run through the >> ecosystem your systems. Your computers? >> Yeah. About thirty five hundred people. Reason over that. So you can Certainly the math. You need us. Um, pretty good. Pretty good technology to make it work effectively for you and efficiently >> at RC A Heard a quote on the floor and on the q kind of echoing the same sentiment is you can't hire your way to success in this market is just not enough people qualified and jobs available to handle the volume and the velocity of the data coming in. Automation plays a critical role. Your reaction to that comment thoughts on? >> Well, I think I think the cure there, John, those when you talk about the volume of the data because there's what we used to call speeds and feeds, right? How big is it? And I used to get great network data so I can share a little because we've talked, like from the nineties or whatever period that were there. Like the network was everything, but it turns out much like a diverse workforce creates the best products. It turns out diverse data is more important than speeds and feeds. So, for example, authentication data map to, you know, email data map to end point data map. TEO SERVICE DATA Soon you're hosting, you know, the number of customers. We are like financial sector data vs Healthcare Data. And so it's the ability Teo actually do correlation across that diverse set of data that really differentiates it. So X is an example. We update one point two billion devices every single month. We do six hundred thirty billion authentications every single month. And so the ability to start correlating those things and movement give us a set of insights to protect people like we never had before. >> That's interesting telemetry you're getting in the marketplace. Plus, you have the systems to bring it in >> a pressure pressure coming just realized. And this all with this consent we don't do without consent, we would never do without consent. >> Of course, you guys have the terms of service. You guys do a good job on that, But I think the point that I'm seeing there is that you guys are Microsoft. Microsoft got a lot of access. Get a lot of stuff out there. How does an enterprise move to that divers model because they will have email, obviously. But they have devices. So you guys are kind of operating? I would say tear one of the level of that environment cause you're Microsoft. I'm sure the big scale players to that. I'm just an enterprising I'm a bank or I'm an insurance company or I'm in oil and gas, Whatever the vertical. Maybe. What do I do if I'm the sea? So they're So what does that mean, Diversity? How should they? >> Well, I think they have a diverse set of data as well. Also, if they participate, you know, even in our platform today, we you know, we have this thing called the security graph, which is an FBI people can tap into and tap into the same graph that I use and so they can use that same graph particular for them. They can use our security experts to help them with that if they don't have the all the resource and staff to go do that. So we provide both both models for that to happen, and I think that's why a unique perspective I should think should remind myself of which is we should have these three things. We have a really good security operations group we have. I think that makes us pretty unique that people can leverage. We build this stuff into the product, which I think is good. But then the partnership, the other partners who play in the graph, it's not just us. So there's lots of people who play on that as well. >> So like to ask you two lines of questions. Wanting on the internal complex is that organizations will have on the external complexity and realities of threats and coming in. How do they? How do you balance that out? What's your vision on that? Because, you know, actually, there's technology, his culture and people, you know in those gaps and capabilities on on all three. Yeah, internally just getting the culture right and then dealing with the external. How does a C so about his company's balance? Those realities? >> Well, I think you raised a really good point, which is how do you move the culture for? That's a big conversation We always have. And that was sort of, you know, it's interesting because the the one side we have thirty five hundred people who have security title in their job, But there's over one hundred thousand people who every day part of their job is doing security, making sure they'LL understand that and know that is a key part we should reinforce everyday on DSO. But I think balancing it is, is for me. It's actually simplifying just a set of priorities because there's no shortage of, you know, vendors who play in the space. There's no shortage of things you can read about. And so for us it was just simplifying it down and getting it. That simplifies simplified view of these are the three things we're going to go do we build onerous platform to prioritize relative to threat, and then and then we ensure we're building quality products. Those five things make it happen. >> I'd like to get your thoughts on common You have again Before I came on camera around how you guys view simplification terminal. You know, you guys have a lot of countries, the board level, and then also you made a common around trust of security and you an analogy around putting that drops in a bucket. So first talk about the simplification, how you guys simplifying it and why? Why is that important? >> You think we supply two things one was just supplying the message to people understood the identity of the device and making sure everything is emitting the right telemetry. The second part that was like for us but a Z to be illustrative security passwords like we started with this technology thing and we're going to do to FAA. We had cards and we had readers and oh, my God, we go talk to a user. We say we're going to put two FAA everywhere and you could just see recoil and please, >> no. And then >> just a simple change of being vision letters. And how about this? We're just going to get rid of passwords then People loved like they're super excited about it. And so, you know, we moved to this idea of, you know, we always said this know something, know something new, how something have something like a card And they said, What about just be something and be done with it? And so, you know, we built a lot of the capability natively into the product into windows, obviously, but I supported energies environment. So I you know, I support a lot of Mac clinics and IOS and Android as well So you've read it. Both models you could use by or you could use your device. >> That's that. That's that seems to be a trend. Actually, See that with phones as well as this. Who you are is the password and why is the support? Because Is it because of these abuses? Just easy to program? What's the thought process? >> I think there's two things that make it super helpful for us. One is when you do the biometric model. Well, first of all, to your point, the the user experience is so much better. Like we walk up to a device and it just comes on. So there's no typing this in No miss typing my password. And, you know, we talked earlier, and that was the most popular passwords in Seattle with Seahawks two thousand seventeen. You can guess why, but it would meet the complexity requirements. And so the idea is, just eliminate all that altogether. You walk up machine, recognize you, and you're often running s o. The user experience is great, but plus it's Actually the entropy is harder in the biometric, which makes it harder for people to break it, but also more importantly, it's bound locally to the device. You can't run it from somewhere else. And that's the big thing that I think people misunderstanding that scenario, which is you have to be local to that. To me, that's a >> great example of rethinking the security paradigm. Exactly. Let's talk about trust and security. You you have an opinion on this. I want to get your thoughts, the difference between trust and security so they go hand in hand at the same time. They could be confused. Your thoughts on this >> well being. You can have great trust. You can, so you can have great security. But you generally and you would hope that would equate like a direct correlation to trust. But it's not. You need to you build trust. I think our CEO said it best a long time ago. You put one bucket of water, one bucket. Sorry, one truffle water in the bucket every time. And that's how you build trust. Over time, my teenager will tell you that, and then you kick it over and you put it on the floor. So you have to. It's always this ratcheting up bar that builds trust. >> They doing great you got a bucket of water, you got a lot of trust, that one breach. It's over right, >> and you've got to go rebuild it and you've got to start all over again. And so key, obviously, is not to have that happen. But then, that's why we make sure you have operational rigor and >> great example that just totally is looking Facebook. Great. They have massive great security. What really went down this past week, but still the trust factor on just some of the other or societal questions? >> Yeah, >> and that something Do it. >> Security. Yeah, I think that's a large part of making sure you know you're being true. That's what I said before about, you know, we make sure we have consent. We're transparent about how we do the things we do, and that's probably the best ways to build trust. >> Okay, so you guys have been successful in Microsoft, just kind of tight the company for second to your role. It's pretty well documented that the stock prices at an all time high. So if Donatella Cube alumni, by the way, has been on the cue before he he took over and clear he didn't pivot. He just said we'd go in the cloud. And so the great moves, he don't eat a lot of great stuff. Open source from open compute to over the source. And this ship has turned and everything's going great. But that cheering the cloud has been great for the company. So I gotta ask you, as you guys move to the cloud, the impact to your businesses multi fold one products, ecosystem suppliers. All these things are changing. How has security role in the sea? So position been impact that what have you guys done? How does that impact security in general? Thoughts? >> Yeah, I think we obviously were like any other enterprise we had thousands of online are thousands of line of business applications, and we did a transformation, and we took a method logical approach with risk management. And we said, Okay, well, this thirty percent we should just get rid of and decommission these. We should, you know, optimize and just lifting shifting application. That cloud was okay, but it turns out there's massive benefit there, like for elasticity. Think of things that quarterly reporting or and you'll surveys or things like that where you could just dynamically grow and shrink your platform, which was awesome linear scale that we never had Cause those events I talk about would require re architectures. Separate function now becomes linear. And so I think there is a lot of things from a security perspective I could do in a much more efficient must wear a fish. In fact, they're then I had to have done it before, but also much more effective. I just have compute capability. Didn't have I have signal I didn't have. And so we had to wrap her head around that right and and figure out how to really leverage that. And to be honest, get the point. We're exploited because you were the MySpace. I have disaster and continent and business. This is processed stuff. And so, you know, everyone build dark fiber, big data centers, storage, active, active. And now when you use a platform is a service like on that kind of azure. You could just click a Bach and say, I want this thing to replicate. It also feeds your >> most diverse data and getting the data into the system that you throw a bunch of computer at that scale. So What diverse data? How does that impact the good guys and the bad guys? That doesn't tip the scales? Because if you have divers date and you have his ability, it's a race for who has the most data because more data diversity increases the aperture and our visibility into events. >> Yeah, I you >> know, I should be careful. I feel like I always This's a job. You always feel like you're treading water and trying to trying to stay ahead. But I think that, um, I think for the first time in my tenure do this. I feel there's an asymmetry that benefits. They're good guys in this case because of the fact that your ability to reason over large sets of data like that and is computed data intensive and it will be much harder for them like they could generally use encryption were effectively than some organization because the one the many relationship that happens in that scenario. But in the data center you can't. So at least for now, I feel like there's a tip This. The scales have tipped a bit for the >> guy that you're right on that one. I think it's good observation I think that industry inside look at the activity around, from new fund adventures to overall activity on the analytics side. Clearly, the data edge is going to be an advantage. I think that's a great point. Okay, that's how about the explosion of devices we're seeing now. An explosion of pipe enabled devices, Internet of things to the edge. Operational technologies are out there that in factory floors, everything being I P enables, kind of reminds me of the old days. Were Internet population you'd never uses on the Internet is growing, and >> that costs a lot >> of change in value, creation and opportunities devices. Air coming on both physical and software enabled at a massive rate is causing a lot of change in the industry. Certainly from a security posture standpoint, you have more surface area, but they're still in opportunity to either help on the do over, but also create value your thoughts on this exploding device a landscape, >> I think your Boston background. So Metcalfe's law was the value the net because the number of the nodes on the network squared right, and so it was a tense to still be true, and it continues to grow. I think there's a huge value and the device is there. I mean, if you look at the things we could do today, whether it's this watch or you know your smartphone or your smart home or whatever it is, it's just it's pretty unprecedented the capabilities and not just in those, but even in emerging markets where you see the things people are doing with, you know, with phones and Lauren phones that you just didn't have access to from information, you know, democratization of information and analysis. I think it's fantastic. I do think, though, on the devices there's a set of devices that don't have the same capabilities as some of the more markets, so they don't have encryption capability. They don't have some of those things. And, you know, one of Microsoft's responses to that was everything. Has an M see you in it, right? And so we, you know, without your spirit, we created our own emcee. That did give you the ability to update it, to secure, to run it and manage it. And I think that's one of the things we're doing to try to help, which is to start making these I, O. T or Smart devices, but at a very low cost point that still gives you the ability because the farm would not be healed Update, which we learn an O. T. Is that over time new techniques happen And you I can't update the system >> from That's getting down to the product level with security and also having the data great threats. So final final talk Tracking one today with you on this, your warrior in the industry, I said earlier. See, so is a hard job you're constantly dealing with compliance to, you know, current attacks, new vector, new strains of malware. And it's all over the map. You got it. You got got the inbound coming in and you got to deal with all that the blocking and tackling of the organization. >> What do you What do >> you finding as best practice? What's the what if some of the things on the cso's checklist that you're constantly worried about and or investing in what some of >> the yeah, >> the day to day take us through the day to day life >> of visited a lot? Yeah, it >> starts with not a Leslie. That's the first thing you have to get used to, but I think the you know again, like I said, there's risk Manager. Just prioritize your center. This is different for every company like for us. You know, hackers don't break and they just log in. And so identity still is one of the top things. People have to go work on him. You know, get rid of passwords is good for the user, but good for the system. We see a lot in supply chain going on right now. Obviously, you mentioned in the Cambridge Analytical Analytics where we had that issue. It's just down the supply chain. And when you look at not just third party but forthe party fifth party supply and just the time it takes to respond is longer. So that's something that we need to continue to work on. And then I think you know that those are some of the other big thing that was again about this. How do you become effective and efficient and how you managed that supply chain like, You know, I've been on a mission for three years to reduce my number of suppliers by about fifty percent, and there's still lots of work to do there, but it's just getting better leverage from the supplier I have, as well as taking on new capability or things that we maybe providing natively. But at the end of the day, if you have one system that could do what four systems going Teo going back to the war for talent, having people, no forces and versus one system, it's just way better for official use of talent. And and obviously, simplicity is the is the friend of security. Where is entropy is not, >> and also you mentioned quality data diversity it is you're into. But also there's also quality date of you have quality and diverse data. You could have a nice, nice mechanism to get machine learning going well, but that's kind of complex, because in the thie modes of security breaches, you got pre breached in breech post breach. All have different data characteristics all flowing together, so you can't just throw that answer across as a prism across the problem sets correct. This is super important, kind of fundamentally, >> yeah, but I think I >> would I would. The way I would characterize those is it's honestly, well, better lessons. I think I learned was living how to understand. Talk with CFO, and I really think we're just two things. There's technical debt that we're all working on. Everybody has. And then there's future proofing the company. And so we have a set of efforts that go onto like Red Team. Another actually think like bad people break them before they break you, you know, break it yourself and then go work on it. And so we're always balancing how much we're spending on the technical, that cleanup, you know, modernizing systems and things that are more capable. And then also the future proofing. If you're seeing things coming around the corner like cryptography and and other other element >> by chain blockchain, my supply chain is another good, great mechanism. So you constantly testing and R and D also practical mechanisms. >> And there in the red team's, which are the teams that attacking pen everything, which is again, break yourself first on this super super helpful for us >> well bred. You've seen a lot of ways of innovation have been involved in multiple ways computer industry client server all through the through the days, so feel. No, I feel good about this you know, because it reminds me and put me for broken the business together. But this is the interesting point I want to get to is there's a lot of younger Si SOS coming in, and a lot of young talent is being attractive. Security has kind of a game revived to it. You know, most people, my friends, at a security expert, they're all gamers. They love game, and now the thrill of it. It's exciting, but it's also challenging. Young people coming might not have experience. You have lessons you've learned. Share some thoughts over the years that scar either scar tissue or best practices share some advice. Some of the younger folks coming in breaking into the business of, you know, current situation. What you learned over the years it's Apple Apple. But now the industry. >> Yeah, sadly, I'd probably say it's no different than a lot of the general advice I would have in the space, which is there's you value experience. But it turns out I value enthusiasm and passion more here so you can teach about anybody whose passion enthusiastic and smart anything they want. So we get great data people and make them great security people, and we have people of a passion like you know, this person. It's his mission is to limit all passwords everywhere and like that passion. Take your passion and driver wherever you need to go do. And I >> think the nice >> thing about security is it is something that is technically complex. Human sociology complex, right? Like you said, changing culture. And it affects everything we do, whether it's enterprise, small, medium business, large international, it's actually a pretty It's a fasten, if you like hard problem. If you're a puzzle person, it's a great It's a great profession >> to me. I like how you said Puzzle. That's I think that's exactly it. They also bring up a good point. I want to get your thoughts on quickly. Is the talent gap is is really not about getting just computer science majors? It's bigger than that. In fact, I've heard many experts say, and you don't have to be a computer scientist. You could be a lot of cross disciplines. So is there a formula or industry or profession, a college degree? Or is it doesn't matter. It's just smart person >> again. It depends if your job's a hundred percent. Security is one thing, but like what we're trying to do is make not we don't have security for developers you want have developed to understand oppa security and what they build is an example on DSO. Same with administrators and other components. I do think again I would say the passion thing is a key piece for us, but But there's all aspects of the profession, like the risk managers air, you know, on the actuarial side. Then there's math people I had one of my favorite people was working on his phD and maladaptive behavior, and he was super valuable for helping us understand what actually makes things stick when you're trying to train their educate people. And what doesn't make that stick anthropologist or super helpful in this field like anthropologist, Really? Yeah, anthropologist are great in this field. So yeah, >> and sociology, too, you mentioned. That would think that's a big fact because you've got human aspect interests, human piece of it. You have society impact, so that's really not really one thing. It's really cross section, depending upon where you want to sit in the spectrum of opportunity, >> knowing it gives us a chance to really hire like we hire a big thing for us has been hard earlier in career and building time because it's just not all available. But then also you, well, you know, hire from military from law enforcement from people returning back. It's been actually, it's been a really fascinating thing from a management perspective that I didn't expect when I did. The role on has been fantastic. >> The mission. Personal question. Final question. What's getting you excited these days? I mean, honestly, you had a very challenging job and you have got attend all the big board meetings, but the risk management compliance. There's a lot of stuff going on, but it's a lot >> of >> technology fund in here to a lot of hard problems to solve. What's getting you excited? What what trends or things in the industry gets you excited? >> Well, I'm hopeful we're making progress on the bad guys, which I think is exciting. But honestly, this idea the you know, a long history of studying safety when I did this and I would love to see security become the air bags of the technology industry, right? It's just always there on new president. But you don't even know it's there until you need it. And I think that getting to that vision would be awesome. >> And then really kind of helping move the trust equation to a whole other level reputation. New data sets so data, bits of data business. >> It's total data business >> breath. Thanks for coming on the Q. Appreciate your insights, but also no see. So the chief information security officer at Microsoft, also corporate vice president here inside the Cuban Palo Alto. This is cute conversations. I'm John Career. Thanks for watching. >> Thank you.

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.

>> Announcer: Live from Las Vegas, it's the Cube. Covering Informatica World, 2018. Brought to you by Informatica. >> Okay welcome back everyone, this is the Cube live here in Las Vegas for Informatica World 2018 exclusive coverage of the Cube. I'm John Furrier co-host of the Cube with Jim Kobielus, my co-host this segment and with that we'll keep on continue with the Cube. Our next guest is Vira Shanti who is the chief data officer at Lippo Digital Group, welcome to the Cube. >> Thank you so much, very excited to be here. >> Thank you for coming on, but people don't know before we came on camera, you and Jim were talking in the native tongue. Thanks for coming on. I know your chief data officer, we've got a lot of questions we love these conversations because we love data, but take a minute to explain what you guys are doing, what the company is, what the size is and the data challenges. >> Okay, maybe let me introduce myself first, so my name is Vira, my role is the chief data officer. Responsibility, that actually is cover for the big data transformation for the Lippo group data. Lippo group is actually part of the one of the largest in Indonesia, we serve a middle class for the consumer services, so we are connecting I think more than 120 million of the customers. What's Lippo as a group doing is actually we do many things. We are the largest of the hospital in Indonesia or just super market, we do department stores, coffee shop, cinema, data centers. We on bang as well, news, cable TV, what else? >> You have a lot of digital assets. >> What you do is you drive to any state in Indonesia and you see Lippo everywhere. >> Yeah, education as well, from the kindergarten to the university, that's why it's a lot of diversity of the business, that owned by Lippo. But recently we're endorsing a lot in the digital transformation, so we're releasing a new mobile app, it is called OVO, O, V, O. Actually it's like centralized loyalty E money to providing the priority bills to all the Lippo group customers, so they're not going to maintain their own membership loyalty program, it's going to just like the OVO, so it's not only being accepted by Lippo ecosystem, but also to the external ecosystem as well. We start to engage with the machine partner, we just today sorted like reaching out 30000 machine outlets. >> Let's get Jim's perspective, I want you to connect the dots for me, because the size and scope of data, you talk about deep learning a lot. And let's connect the dots, cuz we've heard a lot of customers here talking about being having data all over the place. How does deep learning, why do you catalog everything? If you've always diverse assets, I'm sure there are different silos. Is there a connection, how are you handling? >> Okay, differently it's not easy job to do, implementing big data for this kind of a lot of diversity of the business, because how to bring all of this data coming from the different source, coming from the different ecosystem to the single analytical platform is quite challenging. The thing is, we also need to learn first about the business, what kind of the business, how they operate, how they run the hospital, how they run the supermarket, how they run the cinema, how they run the coffee shop. By understanding this thing, my team is responsible to transform, not start from the calling the data, cleansing the data, transform the data, then generate the insight. It has to be an action inside. Then we also not only doing the BI things, but also how from their data we can developing the analytical product on top of the technology big data, that we own today. What we deliver is actually beyond the BI. Of course we do a lot of thing, for example, we really focusing in doing the customers 360 degree profile, because that's the only reason how we really can understand out customers. Today, we have more than 100s of customer attribute teaching for individual customers. I can understand what's your profile for the purchasing behaviors, what kind of the product, that you like. Let's say for the data coming from the supermarket, I know what's your brands, your favorite, whether you're spending is declining. How you spend your point, part of the loyalty program. Then many things, so by understanding very deep these, that we can engage with customers in the better way in providing the new customer experience, because we not only let's say providing them with the right deals, but also when would be the right time, we should connect to them providing something, that they might need. This is the way how from the data we try to connect with our customers. >> Yeah, provided more organic experience across the entire portfolio of Lippo brands throughout the ecosystem. It doesn't feel to the customer and so it isn't simply a federation of brands, it's one unified brand in some degree from the customer's point of view delivering value, that each of the individual components of the Lippo portfolio may not be able to provide. >> Yes, yes, so many things actually we can do on top of that 360 degree of the customers. Our big data outcome in the form of the API. Why it has to be in the API, because when we interact with the customer, there could be unlimited customer touch point to call this API. It could be like the mobile apps after smart customer touch point or could be the dashboard, that we develop for our Lippo internal business. Could be anything or even we can also connect to the other industry from the different business, then how we can connect each other using that big data API, so that's why-- >> Is it an ecosystem, isn't that one API, or it's one API, when unified API for accessing all the back end data and services? >> For something like this, there are to type of the API, that we develop, number one is the API, that belong to the customer 360 degree. Every entry would then attach to your profile and say we can convert it to the API. Let's say smart apps, as part of customer touch point, for example like OVO, we would like to engage with our customers, meaning, that the apps can just designing their online business orchestration, then calling a specific API by understanding let's say from the point of view of loyalty or product preference, that you like, so that then what kind of offers, that we need to push to the customer touch point general using the OVO apps. Or even let's say other supermarket have their on apps, so the apps can also following our API based on their data to understand what kind of the brand or the preference probably they like. Let's run in their apps, when the customer connects, it's going to be something, that really personalized. That's why it's in order to manage the future, actually it's very important for us to deliver this big data outcome in the form of the API. >> It scales too, not a lot of custom work, you don't have to worry about connecting people and making sure it works, expose an API and say, there it is and then. >> Different countries, in terms of privacy in the use of personally identifiable information, different countries and regions have their own different policies and regulations, clearly the European union is fairly strict, the European union with GDPR coming along, the US has its own privacy mandates, in Indonesia, are there equivalent privacy regulations or laws, that we require for example. You ask the customers to consent to particular uses of their data, that you're managing with your big data system, that sits behind OVO. Is that something in your overall program, that you reflect? >> Yes, there are some regulation in Indonesia governed by the government, they'll call having their own regulation, but we let's say part of the thing, that, yes, there is a specific regulation. But regulation for the retail is not really that clear yet for now, but we put ourself in the higher restricted regulation, that we put in place as part of our data protection, part of our data governance compliance as well. If until we do this demonetization or consolidating this data, there is no data, that's being shared outside the entity of the organization. Because let's say, when we do that demonetization everything's done by system to system, when it's called the API, so there is no hands off for other customer in individual data. Let's say if our partner FMCG digital agency or even advertiser, future wise they would like to call our API, what they can see, but that target lead of the customers, that they would like to connect is actually not individual of the data. It's going to be in the aggregated format. Even though many segmentation, that we can deliver is not going to expose every individual customer. >> You have a lot of use cases, that you can handle, because of the control governance piece. How about, by the way, that's fantastic and I know how hard it must be the challenge, but you have it setup nicely. Now that the setup with Informatica and the work you're doing, how are you interfacing with developers, cuz now you have the API. Is it just API based, are you looking at containers, kubernetes, clout technologies? Are you guys looking at that down the road or is that part of the, or is it just expose the API to the developers? >> For today, that actually who's going to consume our API actually? Definitely it's going to be the ecosystem of the Lippo internals, how the customer touch point can leverage the API. Then for the external, for example, like FMCG, the digital agency, when they call our API, usually it's like they can subscribe, there could be some kind of the business model divine there, but once again, like I mentioned to you, let's say it's not going to reveal any individual customer information, but the thing is, how we deliver this API things? We develop our own API system, we develop our API gateway, in simple thing, that actually how to put the permission or grant the access of any kind of digital channel, when they consumer our API and what kind of subscription meta? What we did for the big data actually is not really into, we investing a lot of technology in place for us to use. The thing, that makes my team so exciting about this transformation, because we like to create something, that's we create our own API gateway. We create some analytic product on top of the technology, that we have today. >> When they subscribe to the API, you're setting policy for the data, that they can get and you're done. >> Something like that. >> You automated that. Cool, well we see a lot of AI, any machine learning in your future, you, guys, doing any automation, how are you guys thinking about some of the tools we've been seeing here at the show around automation and AI, Clair, you tapping into any of the goodness? >> Yes, if everybody like to talk what AI right? >> John: You got API, you're good, you don't need anything. >> Many organization, when they're really implementing big data, sometimes they start jumping, I need to start doing the AI things. But from our point of view, yes, AI is very important, definitely we will go there, but for now, what's important for us is how we really can bring the data to single analytical platform, developing that 360 degree customer profile, because we really need to understand our customer better. Then thinking about how we can connect with them, how we can bring the new experience and especially at the right time. >> Actually let me break down AI, cuz I cover AI for Wiki bond, it's such an enormous topic, I break it down in specific things, like for example, speech recognition for voice activated access to digital assistance, that might be embedded in a mobile phones. Indonesia is a huge diverse country, it's an acapela, you have many groups living under the unitary national structure, but they speak different languages, they have different dialects, do you use or are you considering speech recognition? How you would tailor speech recognition in a country, that is so diverse as Indonesia. Is that something an application of AI you're considering using in terms of your user interface? >> Okay, for now we not really into there yet, because you are definitely correct. Developing that kind of library for Indonesia, because different dialect, different accent, it's tough, so the AI things, that we're looking for is actually going to be product recommendation engine. Because you know, let's say, that a lot of things on top of this customer 360 degree, that we can do, right? Because meaning it's going to open unlimited opportunity how I can engage to the customers, what kind of the right offer. Because there's a lot of brand owners, like FMCG, that they would like to connect, also getting in touch, reach out our customers. By developing this kind of product recommendation engine, let's say using the typical machine learning, so we can understand when we introduce this thing, customer like it, introduce that thing, they don't like it. >> Let me ask the next logical question there, it's such a big diverse country, do you, in modeling the customer profile, are you able to encode cultural sensitivities, once again, a very diverse country, there's probably things you could recommend in terms of products to some peoples, that other people might find offensive or insensitive, is that something, that in terms of modeling the customer, you take into consideration? It doesn't just apply to Indonesia, it applies here too or anywhere else, where you have many people. >> Of course can to do that the modeling, but we're doing right now, let's say once again, speaking about the personalized offer, from that point of view, what we see is to create the definition based on customer spending power first, buying power, we need to understand, that this customer's actually in which level of the buying power. By understanding this kind of buying power level, then we really can understand, that should we introduce this kind of the offers or not. Because this is too expensive or not. Because customer spending level can be also different. Let's say when our customers spend in our supermarket, maybe it's going to medium spending level, but let's say when they spend their money to purchase the coffee, maybe it's regular basis, so it's more spending. Could be different spending, so we also need to learn this kind of thing, because sometimes the low spending or medium spending or high spending, sometimes it's not something, that we put in the effort level for everything, sometimes it could be different. This is the thing, that also very exciting for us to understand this kind of spending, buying power. >> Great to have you on the Cube, thanks for coming, so I got to ask you one final question. I heard you were in an honorary Informatica innovation award honoree, congratulations. >> Thank you. >> What advice would you have for your peers, that might want to aspire to get the award next year? >> The thing is, our big data journey just start last year. Really start from the zero, so when yesterday we get an award for the analytics, so actually what we really focus on to do something, that actually is very simple. Some organization, when they're implementing big data sometimes they would like to do everything in the phase one. What we're planning to do is number one, how to bring the data very fast, then understand what kind of value of the data, that we can bring to the organization. Our favorite one is developing the customer 360 degree profile, because once you really understand your customer from any point of view, it's going to open unlimited opportunities how you can engage with your customers, it also open another opportunity how you can bring another ecosystem to our business to engage with our customers, that one point of view is already opening a lot of thing, huge. Either that thinking what would be the next step. Of course, that API is going to simplify your business in the future scale so on. That's becoming our main focus to allow us to deliver a lot of quick low hanging effort at the same time. I think that's a thing, that makes us really can, within a short period of time, can deliver a lot of things. >> The chief data officer at Lippo digital group, thanks for sharing your story, it's the Cube, we're here live in Las Vegas. They're going to be bonding here talking about all the greatness going on there. This is the Cube here in Las Vegas, stay with us for continuing day two coverage of Informatica world 2018, we'll be right back.