(upbeat music) >> Welcome back to Supercloud 2, which is an open industry collaboration between technologists, consultants, analysts, and of course, practitioners, to help shape the future of cloud. And at this event, one of the key areas we're exploring is the intersection of cloud and data, and how building value on top of hyperscale clouds and across clouds is evolving, a concept we call supercloud. And we're pleased to welcome Harvir Singh, who's the chief data architect and global head of data at Western Union. Harvir, it's good to see you again. Thanks for coming on the program. >> Thanks, David, it's always a pleasure to talk to you. >> So many things stand out from when we first met, and one of the most gripping for me was when you said to me, "When data moves, money moves." And that's the world we live in today, and really have for a long time. Money has moved as bits, and when it has to move, we want it to move quickly, securely, and in a governed manner. And the pressure to do so is only growing. So tell us how that trend is evolved over the past decade in the context of your industry generally, and Western Union, specifically. Look, I always say to people that we are probably the first ones to introduce digital currency around the world because, hey, somebody around the world needs money, we move data to make that happen. That trend has actually accelerated quite a bit. If you look at the last 10 years, and you look at all these payment companies, digital companies, credit card companies that have evolved, majority of them are working on the same principle. When data moves, money moves. When data is stale, the money goes away, right? I think that trend is continuing, and it's not just the trend is in this space, it's also continuing in other spaces, specifically around, you know, acquisition of customers, communication with customers. It's all becoming digital, and it's, at the end of the day, it's all data being moved from one place or another. At the end of the day, you're not seeing the customer, but you're looking at, you know, the data that he's consuming, and you're making actionable items on it, and be able to respond to what they need. So I think 10 years, it's really, really evolved. >> Hmm, you operate, Western Union operates in more than 200 countries, and you you have what I would call a pseudo federated organization. You're trying to standardize wherever possible on the infrastructure, and you're curating the tooling and doing the heavy lifting in the data stack, which of course lessens the burden on the developers and the line of business consumers, so my question is, in operating in 200 countries, how do you deal with all the diversity of laws and regulations across those regions? I know you're heavily involved in AWS, but AWS isn't everywhere, you still have some on-prem infrastructure. Can you paint a picture of, you know, what that looks like? >> Yeah, a few years ago , we were primarily mostly on-prem, and one of the biggest pain points has been managing that infrastructure around the world in those countries. Yes, we operate in 200 countries, but we don't have infrastructure in 200 countries, but we do have agent locations in 200 countries. United Nations says we only have like 183 are countries, but there are countries which, you know, declare themselves countries, and we are there as well because somebody wants to send money there, right? Somebody has an agent location down there as well. So that infrastructure is obviously very hard to manage and maintain. We have to comply by numerous laws, you know. And the last few years, specifically with GDPR, CCPA, data localization laws in different countries, it's been a challenge, right? And one of the things that we did a few years ago, we decided that we want to be in the business of helping our customers move money faster, security, and with complete trust in us. We don't want to be able to, we don't want to be in the business of managing infrastructure. And that's one of the reasons we started to, you know, migrate and move our journey to the cloud. AWS, obviously chosen first because of its, you know, first in the game, has more locations, and more data centers around the world where we operate. But we still have, you know, existing infrastructure, which is in some countries, which is still localized because AWS hasn't reached there, or we don't have a comparable provider there. We still manage those. And we have to comply by those laws. Our data privacy and our data localization tech stack is pretty good, I would say. We manage our data very well, we manage our customer data very well, but it comes with a lot of complexity. You know, we get a lot of requests from European Union, we get a lot of requests from Asia Pacific every pretty much on a weekly basis to explain, you know, how we are taking controls and putting measures in place to make sure that the data is secured and is in the right place. So it's a complex environment. We do have exposure to other clouds as well, like Google and Azure. And as much as we would love to be completely, you know, very, very hybrid kind of an organization, it's still at a stage where we are still very heavily focused on AWS yet, but at some point, you know, we would love to see a world which is not reliant on a single provider, but it's more a little bit more democratized, you know, as and when what I want to use, I should be able to use, and pay-per-use. And the concept started like that, but it's obviously it's now, again, there are like three big players in the market, and, you know, they're doing their own thing. Would love to see them come collaborate at some point. >> Yeah, wouldn't we all. I want to double-click on the whole multi-cloud strategy, but if I understand it correctly, and in a perfect world, everything on-premises would be in the cloud is, first of all, is that a correct statement? Is that nirvana for you or not necessarily? >> I would say it is nirvana for us, but I would also put a caveat, is it's very tricky because from a regulatory perspective, we are a regulated entity in many countries. The regulators would want to see some control if something happens with a relationship with AWS in one country, or with Google in another country, and it keeps happening, right? For example, Russia was a good example where we had to switch things off. We should be able to do that. But if let's say somewhere in Asia, this country decides that they don't want to partner with AWS, and majority of our stuff is on AWS, where do I go from there? So we have to have some level of confidence in our own infrastructure, so we do maintain some to be able to fail back into and move things it needs to be. So it's a tricky question. Yes, it's nirvana state that I don't have to manage infrastructure, but I think it's far less practical than it said. We will still own something that we call it our own where we have complete control, being a financial entity. >> And so do you try to, I'm sure you do, standardize between all the different on-premise, and in this case, the AWS cloud or maybe even other clouds. How do you do that? Do you work with, you know, different vendors at the various places of the stack to try to do that? Some of the vendors, you know, like a Snowflake is only in the cloud. You know, others, you know, whether it's whatever, analytics, or storage, or database, might be hybrid. What's your strategy with regard to creating as common an experience as possible between your on-prem and your clouds? >> You asked a question which I asked when I joined as well, right? Which question, this is one of the most important questions is how soon when I fail back, if I need to fail back? And how quickly can I, because not everything that is sitting on the cloud is comparable to on-prem or is backward compatible. And the reason I say backward compatible is, you know, there are, our on-prem cloud is obviously behind. We haven't taken enough time to kind of put it to a state where, because we started to migrate and now we have access to infrastructure on the cloud, most of the new things are being built there. But for critical application, I would say we have chronology that could be used to move back if need to be. So, you know, technologies like Couchbase, technologies like PostgreSQL, technologies like Db2, et cetera. We still have and maintain a fairly large portion of it on-prem where critical applications could potentially be serviced. We'll give you one example. We use Neo4j very heavily for our AML use cases. And that's an important one because if Neo4j on the cloud goes down, and it's happened in the past, again, even with three clusters, having all three clusters going down with a DR, we still need some accessibility of that because that's one of the biggest, you know, fraud and risk application it supports. So we do still maintain some comparable technology. Snowflake is an odd one. It's obviously there is none on-prem. But then, you know, Snowflake, I also feel it's more analytical based technology, not a transactional-based technology, at least in our ecosystem. So for me to replicate that, yes, it'll probably take time, but I can live with that. But my business will not stop because our transactional applications can potentially move over if need to. >> Yeah, and of course, you know, all these big market cap companies, so the Snowflake or Databricks, which is not public yet, but they've got big aspirations. And so, you know, we've seen things like Snowflake do a deal with Dell for on-prem object store. I think they do the same thing with Pure. And so over time, you see, Mongo, you know, extending its estate. And so over time all these things are coming together. I want to step out of this conversation for a second. I just ask you, given the current macroeconomic climate, what are the priorities? You know, obviously, people are, CIOs are tapping the breaks on spending, we've reported on that, but what is it? Is it security? Is it analytics? Is it modernization of the on-prem stack, which you were saying a little bit behind. Where are the priorities today given the economic headwinds? >> So the most important priority right now is growing the business, I would say. It's a different, I know this is more, this is not a very techy or a tech answer that, you know, you would expect, but it's growing the business. We want to acquire more customers and be able to service them as best needed. So the majority of our investment is going in the space where tech can support that initiative. During our earnings call, we released the new pillars of our organization where we will focus on, you know, omnichannel digital experience, and then one experience for customer, whether it's retail, whether it's digital. We want to open up our own experience stores, et cetera. So we are investing in technology where it's going to support those pillars. But the spend is in a way that we are obviously taking away from the things that do not support those. So it's, I would say it's flat for us. We are not like in heavily investing or aggressively increasing our tech budget, but it's more like, hey, switch this off because it doesn't make us money, but now switch this on because this is going to support what we can do with money, right? So that's kind of where we are heading towards. So it's not not driven by technology, but it's driven by business and how it supports our customers and our ability to compete in the market. >> You know, I think Harvir, that's consistent with what we heard in some other work that we've done, our ETR partner who does these types of surveys. We're hearing the same thing, is that, you know, we might not be spending on modernizing our on-prem stack. Yeah, we want to get to the cloud at some point and modernize that. But if it supports revenue, you know, we'll invest in that, and get the, you know, instant ROI. I want to ask you about, you know, this concept of supercloud, this abstracted layer of value on top of hyperscale infrastructure, and maybe on-prem. But we were talking about the integration, for instance, between Snowflake and Salesforce, where you got different data sources and you were explaining that you had great interest in being able to, you know, have a kind of, I'll say seamless, sorry, I know it's an overused word, but integration between the data sources and those two different platforms. Can you explain that and why that's attractive to you? >> Yeah, I'm a big supporter of action where the data is, right? Because the minute you start to move, things are already lost in translation. The time is lost, you can't get to it fast enough. So if, for example, for us, Snowflake, Salesforce, is our actionable platform where we action, we send marketing campaigns, we send customer communication via SMS, in app, as well as via email. Now, we would like to be able to interact with our customers pretty much on a, I would say near real time, but the concept of real time doesn't work well with me because I always feel that if you're observing something, it's not real time, it's already happened. But how soon can I react? That's the question. And given that I have to move that data all the way from our, let's say, engagement platforms like Adobe, and particles of the world into Snowflake first, and then do my modeling in some way, and be able to then put it back into Salesforce, it takes time. Yes, you know, I can do it in a few hours, but that few hours makes a lot of difference. Somebody sitting on my website, you know, couldn't find something, walked away, how soon do you think he will lose interest? Three hours, four hours, he'll probably gone, he will never come back. I think if I can react to that as fast as possible without too much data movement, I think that's a lot of good benefit that this kind of integration will bring. Yes, I can potentially take data directly into Salesforce, but I then now have two copies of data, which is, again, something that I'm not a big (indistinct) of. Let's keep the source of the data simple, clean, and a single source. I think this kind of integration will help a lot if the actions can be brought very close to where the data resides. >> Thank you for that. And so, you know, it's funny, we sometimes try to define real time as before you lose the customer, so that's kind of real time. But I want to come back to this idea of governed data sharing. You mentioned some other clouds, a little bit of Azure, a little bit of Google. In a world where, let's say you go more aggressively, and we know that for instance, if you want to use Google's AI tools, you got to use BigQuery. You know, today, anyway, they're not sort of so friendly with Snowflake, maybe different for the AWS, maybe Microsoft's going to be different as well. But in an ideal world, what I'm hearing is you want to keep the data in place. You don't want to move the data. Moving data is expensive, making copies is badness. It's expensive, and it's also, you know, changes the state, right? So you got governance issues. So this idea of supercloud is that you can leave the data in place and actually have a common experience across clouds. Let's just say, let's assume for a minute Google kind of wakes up, my words, not yours, and says, "Hey, maybe, you know what, partnering with a Snowflake or a Databricks is better for our business. It's better for the customers," how would that affect your business and the value that you can bring to your customers? >> Again, I would say that would be the nirvana state that, you know, we want to get to. Because I would say not everyone's perfect. They have great engineers and great products that they're developing, but that's where they compete as well, right? I would like to use the best of breed as much as possible. And I've been a person who has done this in the past as well. I've used, you know, tools to integrate. And the reason why this integration has worked is primarily because sometimes you do pick the best thing for that job. And Google's AI products are definitely doing really well, but, you know, that accessibility, if it's a problem, then I really can't depend on them, right? I would love to move some of that down there, but they have to make it possible for us. Azure is doing really, really good at investing, so I think they're a little bit more and more closer to getting to that state, and I know seeking our attention than Google at this point of time. But I think there will be a revelation moment because more and more people that I talk to like myself, they're also talking about the same thing. I'd like to be able to use Google's AdSense, I would like to be able to use Google's advertising platform, but you know what? I already have all this data, why do I need to move it? Can't they just go and access it? That question will keep haunting them (indistinct). >> You know, I think, obviously, Microsoft has always known, you know, understood ecosystems. I mean, AWS is nailing it, when you go to re:Invent, it's all about the ecosystem. And they think they realized they can make a lot more money, you know, together, than trying to have, and Google's got to figure that out. I think Google thinks, "All right, hey, we got to have the best tech." And that tech, they do have the great tech, and that's our competitive advantage. They got to wake up to the ecosystem and what's happening in the field and the go-to-market. I want to ask you about how you see data and cloud evolving in the future. You mentioned that things that are driving revenue are the priorities, and maybe you're already doing this today, but my question is, do you see a day when companies like yours are increasingly offering data and software services? You've been around for a long time as a company, you've got, you know, first party data, you've got proprietary knowledge, and maybe tooling that you've developed, and you're becoming more, you're already a technology company. Do you see someday pointing that at customers, or again, maybe you're doing it already, or is that not practical in your view? >> So data monetization has always been on the charts. The reason why it hasn't seen the light is regulatory pressure at this point of time. We are partnering up with certain agencies, again, you know, some pilots are happening to see the value of that and be able to offer that. But I think, you know, eventually, we'll get to a state where our, because we are trying to build accessible financial services, we will be in a state that we will be offering those to partners, which could then extended to their customers as well. So we are definitely exploring that. We are definitely exploring how to enrich our data with other data, and be able to complete a super set of data that can be used. Because frankly speaking, the data that we have is very interesting. We have trends of people migrating, we have trends of people migrating within the US, right? So if a new, let's say there's a new, like, I'll give you an example. Let's say New York City, I can tell you, at any given point of time, with my data, what is, you know, a dominant population in that area from migrant perspective. And if I see a change in that data, I can tell you where that is moving towards. I think it's going to be very interesting. We're a little bit, obviously, sometimes, you know, you're scared of sharing too much detail because there's too much data. So, but at the end of the day, I think at some point, we'll get to a state where we are confident that the data can be used for good. One simple example is, you know, pharmacies. They would love to get, you know, we've been talking to CVS and we are talking to Walgreens, and trying to figure out, if they would get access to this kind of data demographic information, what could they do be better? Because, you know, from a gene pool perspective, there are diseases and stuff that are very prevalent in one community versus the other. We could probably equip them with this information to be able to better, you know, let's say, staff their pharmacies or keep better inventory of products that could be used for the population in that area. Similarly, the likes of Walmarts and Krogers, they would like to have more, let's say, ethnic products in their aisles, right? How do you enable that? That data is primarily, I think we are the biggest source of that data. So we do take pride in it, but you know, with caution, we are obviously exploring that as well. >> My last question for you, Harvir, is I'm going to ask you to do a thought exercise. So in that vein, that whole monetization piece, imagine that now, Harvir, you are running a P&L that is going to monetize that data. And my question to you is a there's a business vector and a technology vector. So from a business standpoint, the more distribution channels you have, the better. So running on AWS cloud, partnering with Microsoft, partnering with Google, going to market with them, going to give you more revenue. Okay, so there's a motivation for multi-cloud or supercloud. That's indisputable. But from a technical standpoint, is there an advantage to running on multiple clouds or is that a disadvantage for you? >> It's, I would say it's a disadvantage because if my data is distributed, I have to combine it at some place. So the very first step that we had taken was obviously we brought in Snowflake. The reason, we wanted our analytical data and we want our historical data in the same place. So we are already there and ready to share. And we are actually participating in the data share, but in a private setting at the moment. So we are technically enabled to share, unless there is a significant, I would say, upside to moving that data to another cloud. I don't see any reason because I can enable anyone to come and get it from Snowflake. It's already enabled for us. >> Yeah, or if somehow, magically, several years down the road, some standard developed so you don't have to move the data. Maybe there's a new, Mogli is talking about a new data architecture, and, you know, that's probably years away, but, Harvir, you're an awesome guest. I love having you on, and really appreciate you participating in the program. >> I appreciate it. Thank you, and good luck (indistinct) >> Ah, thank you very much. This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more great coverage from Supercloud 2. (uplifting music)

(bright upbeat music) >> Hello everyone. And we're back at AWS Re:Invent. You're watching theCUBE and we're here, day two. Actually we started Monday night and we got wall-to-wall coverage. We going all the way through Thursday, myself. I'm Dave Volante with the co-host, David Nicholson. Lisa Martin is also here. Of course, John Furrier. Partners, technologists, customers, the whole ecosystem. It's good to be back in the live event. Of course we have hybrid event as well a lot of people watching online. Anshu Sharma is here. He is the co-founder and CEO of Skyflow, new type of privacy company, really interested in this topic. Great to see you. Thanks for coming on. >> Thank you, thanks for bringing me here. >> It's timely, you know. Privacy, security, they're kind of two sides of the same coin. >> Yes. >> Why did you found Skyflow? >> Well, the idea for Skyflow really comes from my background in some ways. I spent my first nine years at Oracle, six years at Salesforce. And whether we were building databases or CRM products, customers would come to us and say, "Hey, you know, I have this very different type of data. It's things like social security numbers, frequent flyer card numbers, card numbers. You know, can you secure it better? Can you help me manage things like GDPR?" And to be honest, there was never a clear answer. There's a lot of technology solutions out there that do one thing at a time, you can walk around the booths here, there's like a hundred companies. And if you use all those hundred things correctly, maybe you could go tell your board that maybe a social security number is not going to be lost anymore. And I was like, "You know, we've simplified everything else. Why is it so hard to protect my social security number? It should be easy. It should be as easy as using Stripe or Twilio." And this idea just never went away and kept coming back till a few years ago, we learned about the Facebook privacy challenges, the Equifax challenges. And I was like, boy, it's the time. It's time to go do it now. >> You started the company in 2019. Right? >> Yes. >> I mean, your timing was pretty good, right? So what are the big sort of Uber trends that you're seeing? Obviously GDPR, the California Consumer Privacy Act. I heard this morning. Did you hear this? That like, if you post a picture on social media now without somebody's permission, you're now violating their privacy. It's like, you can see the smiles on Anshu's face. >> Its like every week, we're like every week, there's a new story that could be like, well, Skyflow. The new story is the question, the answer is Skyflow. But honestly I think what's happened is, the issue is put very simple. You know all we're trying to do is protect people's social security numbers, phone numbers, credit card numbers, things we hold dear. At the same time, it's complex. Like what does it mean to protect your social security number let's say? Does that mean I don't get to use it for filing your taxes? Well, I need your credit card number to process a payment. And we were like, this is just too complicated. Why, how do companies like Apple do it? How do companies like Netflix manage not have as many breaches as my hotel that barely has any data. And the answer is those companies actually have evolved to a completely different architecture, the zero trust data architecture. And that was our inspiration for starting this company. >> Yeah. I mean. How many times have you been asked to give your social security number? And you're like, why? why do you want it? What are you going to do with it? How do you protect it? And they go, "I don't know." >> You know, what's even, my favorite is like, you give your social security number to say TurboTax, how many days of the year do they need to use it? One. How many days of the year do they have it? And the thing is, it's a liability for those CTOs too. >> Yeah right. >> The CTO of Walgreens, the CTO of Intuit. They don't really want that social security number just so they can process your card once a year, or your social security number once a year. It's almost like we're forcing them to hold onto data. And then they have to bear the burden of having these stories. Like, you know, everybody wants to prevent a New York Times story that says, what Robin Hood had a breach, Twitter had a breach. >> So walk us through how Skyflow would address something like that. So take the, you know, take the make a generic version of TurboTax, social security members. There they are right now, they're sitting in a database somewhere. Hopefully there's some security wrapped around it in some way or another. What would you advise a customer like that to do? And what are you actually doing for them? >> So, look, it's very simple. You are not going to put your username passwords in a generic database. You're going to use something like OD Zero or Octa to do it. We're living in a world where we have polyglot data stores. Like there's a key value store. There's a time series database. There is a search database like Elastic. There's a log database like Splunk. But PII data, Somehow we think just fine. If it's in a hundred places and our answer is that we should do the same thing that companies like Apple, Netflix, Google, everybody, does. They take this data. They completely isolate it from the databases. And it gets stored in a custom data store in our case, that would be Skyflow. And essentially we'd give you encrypted tokens back and you can use these encrypted tokens that look like fake social security number. It's called a Format Preserving Encryption. So if you think about all the breakthroughs we've had in homomorphic encryption, on secure elements, like the way your phone works, the credit card number is stored in a secure element. So it's the same idea. There's a secure part of your data stack, which is Skyflow. That basically keeps the data always protected. And because we can compute and search on encrypted data, this is important, everybody can encrypt data at rest. Skyflow is the first company that's come out and said, "Look, you can keep your phone number and social security number, encrypted while I can run an aggregation query." So I can tell you what's the balance of your customer's account balance. And i can run that query without decrypting, a single row of data. The only other company I know that can do that internally is a certain Cupertino based company. >> So think about it. Anybody can walk something up to a certain degree, but allowing frictionless access at the same time. >> While it's encrypted. So how do you make that? Are you, is a strategy to make that a horizontal service? That I can put into my data protection service or my E-commerce service or whatever. >> It's a cloud-based service that runs on AWS and other clouds. We basically given instance just like, you'll get an instance of a post-grad store or you get an API handled to OD Zero. You basically instantiate Skyflow of what gets created. It can be in your AWS environment, dedicated VPC. So it's private to you and then you have a handle and then basically you just start using it. >> So how, how do you, what's the secret sauce? How do you do that? >> The secret source. Well, now that we filed the patents on it, I can reveal the secret sauce. So the holy grail of encryption right now, if you go talk to people at a leading company, is there's something called Fully Homomorphic Encryption. That's fundamentally the foundation on which things like Bitcoin are built actually. But the hard part about Fully Homomorphic Encryption is it works. You can actually do mathematical computations on it without decrypting the data, but it's about a million times slower. >> Yes slower, right. >> So nobody uses it. My insight was that we don't need to do multiplications and additions on phone numbers. You never take my phone number and divide by your social security number. (Dave laughing) These numbers are not numbers, they are data structures. So our insight was if you treat them as specialized data structures, we're all talking about basically about 80 different types of data across the globe. Every human being has an ID, date of birth, height, color of eyes. There's not that many fields. What we can do then is create specialized encryption schemes for each data type. We call this polymorphic data encryption. Poly means multiple. As a result of that, we can actually store the data encrypted and build indexes on it. Since we can index interpret data, it's kind of like, imagine you can run real-time queries on data that's encrypted. Every other data store, When you encrypt the data, it becomes invisible to database. And that's why we had to build this as a full stacked service. Just like the Snowflake guys had to start with the foundation of storage, rethink indexing, and build Snowflake. We did the same thing, except we built it for encrypted indexes Whereas they built it for encrypted, for regular data stores. >> So thinking, if you think about today's tech stack, it's evolving, right? The data protection and security are coming together. Where does this fit? Is it sort of now becoming a fundamental part of the-- >> We think every leading company, whether you're building a new brokerage application or you are the largest bank in the world, and we're talking to some of them right now. They're all going to have an internal service called a PII wall. This wall just like Apple and Google have their own internal walls. You're going to have a wall service in your service oriented architecture, essentially. And it's going to basically be the API. Every other application and database in your company is not going to store my social security number. The SSNs don't belong in 600 databases at a leading bank. They don't belong inside your customer support system. Think about what happened with Robinhood two weeks ago, right? Someone tricked one call center guy into giving the keys up, which is fine happens. But why did the call center guy have access to like a million email addresses? He's never used going to use that. So we think if you isolate the PII, every leading company is going to end up with a PII Wall, as part of their core architecture. Just like today, we have an Alt API, you have a Search API, you have a Logging API, you're going to have a PII API. And that's going to be part of your modern data stack. >> So okay. So this is definitely not a bolt on, right? It's going to be a fundamental company, just like security is, just like backup is. It's now, you got to have it. It's-- >> Yes. I mean, if you think about it, it just logically makes sense. Like you should be isolating this data. You don't keep your money and gold around at home. You put it either in a locker or a bank. I think the same applies for PII. We just haven't done it because companies would pay off a fine for $10,000 or a million dollars. And. >> Yeah. So you've recently raised $45 million to expand your efforts. Obviously that means that people are looking at this and saying there's opportunity, right? What does that look like when you think of growth, where during your go to market strategy at first you're convincing people that it's a good idea to do it. Do you think or hope for, hope one day that there's an inflection point where it's not that people are thinking, you know, let's do this because it's a good idea, but people are like, I have to do this because if I don't, it's irresponsible and I'm going to be penalized for not having it. It becomes something that isn't really a choice. It's something where you just do it. >> So, you know, when we were starting the company, we didn't even have a word to explain what we were trying to do. We would say things like what if there was a cloud service for XYZ. And, but over the last one year, I don't want to take credit for creating this market, but this market has been created in the last year and a half. And you know, we get tons of people, including some of the largest institutions emailing us, saying, "I'm looking to build a PII wall, API service inside my company. Can you tell me why your product meets that need?" And I thought that would take us three to five years to get there. And, you know, we've ended up creating a category, basically just like other companies have. And I think, you know, you don't get, I believe in market permission. You don't get to create a category. The market gives somebody the permission to create a category. Saying, "Look, this makes sense. Something like this should emerge." And if you're there at the right time, like you said. >> Yep. >> You get to take the opportunity. >> So where are you at as a company say for some, some capital is great. When do you scale? >> We're scaling now? So we just doubled our headcount in the last nine to 10 months. We're now 75 people. We think we'll be about 150 to 200 people in the next year. We are hiring across all regions. We just hired a head of Asia pack from segment.com. We just hired our first, you know, lead on international expansion. And in the US, we have an office in Palo Alto. We have an office in Bangalore. We just announced a data residency solution for Europe, data residency solution for India and emerging markets. Because data residency is another one of those things that's just emerging right now. And irrespective of whether you believe in security and privacy. Data residency is one of those things that you are mandated to implement. >> And where are you hiring? Is it combination to go to market? Tell me about your go to market. >> The go to market. We are direct sales organization, but we work with partners. So we haven't announced some of these partnerships, but you're working with some of the companies here who either are large database companies, large security companies. We think there is a win-win relationship between us and some of the partner. >> You're a partner model, partner channel model. >> So, direct sales but partner assisted. >> Yeah. Right. All right. We got to go. Hey, awesome story. Congratulations. Best of luck. >> Very interesting. >> Love to have you back and track the progress. >> Thank you, thank you so much. >> Okay. Thank you for watching theCUBE, the leader in and high-tech coverage. We're at Re-Invent 2021. Be right back (upbeat music)

>> Narrator: From theCUBE studios in Palo Alto and Boston connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, welcome to this CUBE conversation here in the Palo Alto studios for theCUBE. I'm John Furrier, the host. We're here during this time where everyone's sheltering in place during the COVID-19 crisis. We're getting the interviews out and getting the stories that matter for you. It's theCUBE's mission just to share and extract the data from, signal from the noise, and share that with you. Of course the conversation here is about how the data analytics are being used. We have a great friend and CUBE alum, Bruno Kurtic, VP, founding VP of Product and Strategy for Sumo Logic, a leader in analytics. We've been following you guys, kind of going back I think many, many years, around big data, now with AI and machine learning. You guys are an industry leader. Bruno, thanks for spending the time to come on theCUBE, I know you're sheltering in place. Thanks for coming on. >> You're welcome, pleasure. >> Obviously with the crisis, the work at home has really highlighted the at-scale problem, right? We've been having many conversations on theCUBE of cybersecurity at scale, because now the endpoint protection business has been exploding, literally, a lot of pressure of malware. A convenient crime time for those hackers. You're starting to see cloud failure. Google had 18 hours of downtime. Azure's got some downtime. I think Amazon's the only one that haven't had any downtime. But everything is being at scale now, because the new work environment is actually putting pressure on the industry, not only just the financial pressure of people losing their jobs or the hiring freezes, but now the focus is staying in business and getting through this. But the pressure points of scale are starting to show. And working at home is one of them. Analytics has become a big part of it. Can you share your perspective of how people using analytics to get through this, because now the scale of the problem-solving is there with analytics. It's in charts on the virus, exponential curves, people want to know the impact of their business in all this. What's your view on this situation? >> Yeah. The world has changed so quickly. Analytics has always been important. But there are really two aspects of analytics that are important right now. A lot of our enterprises today, obviously, as you said, are switching to this sort of remote workforce. Everybody who was local is now remote, so, people are working from home. That is putting stress on the systems that support that working from home. It's putting stress on infrastructure, things like VPNs and networks and things like that because they're carrying more bits and bytes. It's putting stress on productivity tools, things like cloud provider tools, things like Office 365, and Google Drive, and Salesforce, and other things that are now being leveraged more and more as people are remote. Enterprises are leveraging analytics to optimize and to ensure that they can facilitate course of business, understand where their issues are, understand where their failures are, internal and external, route traffic appropriately to make sure that they can actually do the business they need. But that's only half of the problem. In fact, I think the other half of the problem is maybe even bigger. We as humans are no longer able to go out. We're not supposed to, and able to go shopping and doing things as we normally do, so all of these enterprises are not only working remotely, leveraging productivity tools and quote-unquote "digital technologies" to do work. They're also serving more customers through their digital properties. And so their sites, their apps, their retail stores online, and all of the digital aspects of enterprises today are under more load because consumers and customers are leveraging those channels more. People are getting groceries delivered at home, pharmaceuticals delivered at home. Everything is going through online systems rather than us going to Walgreens and other places to pick things up. Both of those aspects of scale and security are important. Analytics is important in both figuring out how do you serve your customers effectively, and how do you secure those sites. Because now that there's more load, there's more people, and it's a bigger honeypot. And then also, how do you actually do your own business to support that in a digital world? >> Bruno, that's a great point. I just want to reiterate that the role of data in all this is really fundamental and clear, the value that you can get out of the data. Now, you and I, we've had many conversations with you guys over the years. For all of us insiders, we all know this already. Data analytics, everyone's instrumenting their business. But now when you see real-life examples of death and destruction, I mean, I was reporting yesterday that leaked emails from the CDC in the United States showed that in January, they saw that people didn't have fevers with COVID-19. The system was lagging. There was no real-time notifications. This is our world. We've been living in this for this past decade, in the big data world. This is highlighting a global problem, that with notifications, with the right use of data, is a real game-changer. You couldn't get any more clear. I have to ask you, with all this kind of revelations, and I don't mean to be all gloom-and-doom, but that's the reality, highlights the fact that instrumenting and having the data analytics is a must-have. Can you share your reaction to that? >> Yeah, absolutely. You're right. Like you said, we are insiders here, and we've been espousing this world of what we internally in Sumo call the continuous intelligence, which essentially means to us and to our customers, that you collect and process all signals that are available to you as a business, as a government, as a whatever entity that is dealing with critical things. You need to process all of that data as quickly as you can. You need to mine it for insights. You need to, in an agile fashion, just like software development, you need to consume those insights, build them into your processes to improve, to react, to respond quickly, and then deliver better outcomes. The sooner you understand what the data is telling you, the sooner you can actually respond to whatever that data is telling you, and actually avoid bad outcomes, improve good outcomes, and overall, react to whatever is forcing you to react. >> I was just talking with Dave Vellante last week about this, my co-host, and also Jeff Frick, my general manager, who interviewed you in the past on theCUBE, about the transition and transformation that's happening. I want to just get your reaction to what we're seeing, and I wanted to get your thoughts on it. There's transitions and there's transformations. Yeah, we've been kind of in this data transition around analytics. You pointed out, as insiders, we've been pointing this out for years. But I think now there's more of a transformative component to this. I think it's becoming clear to everyone the role of data, and you've laid out some good things there. Now I want to ask you, on this transformation. Do you agree with it, and if you do, how does that change the roles? Because if I'm going to react to this as a business, whether small, medium, and large business, large enterprise or government, I now realize that the old world's over. I need to get to the new way. That means new roles, new responsibilities, new outcomes, new ways to measure. Can you share your thoughts on that? Do you agree with the transformation, and two, what are some of those new role changes? How should a business manager or technologist make that transformation? >> Yeah. If it was ever more clear, getting a switch, or a transformation as you say, from the old way we did business and we did technology to the new way, is only being highlighted by this crisis. If you are an enterprise, and you are trying to do everything yourself, running your own IT stacks and all of that, it is clear today that it is much more difficult to do that than if you were leveraging next generation technologies: clouds, SaaS, PaaS, and other things, because it is hard to get people even to work. I think if we have ever been in a place where this sort of transformation is a must, not a slow choice or an evolution, it is now. Because enterprises who have done that, who have done that already, are now at an advantage. I think this is a critical moment in time for us all as we all wake up to this new reality. It is not to say that enterprises are going to be switched over after this specific crisis, but what's going to happen, I believe, is that, I think the philosophies are going to change, enterprises are going to think of this as the new normal. They're going to think about, "Hey, if I don't have the data "about my business, about my customers, "about my infrastructure, about my systems, "I won't be able to respond to the next one." Because right now there's a lot of plugging the holes in the dam with fingers and toes, but we are going to need to be ready for this, because if you think about what this particular pandemic means, this isn't going to end in April or May. Because without a treatment, or without a vaccination, it's going to continue to resurface. Unless we eradicate the entire population of the virus, any new incident is going to start up like a small flare-up, and that is going to continue to bring us back into the situation. Over this time, we're going to have to continue to respond to this crisis as we are, and we need to plan for the future ones like this. That might not be a pandemic type of crisis. It could be a change in the business. It could be other types of world events, whatever it might be. But I think this is the time when enterprises are going to start adopting these types of procedures and technologies to be able to respond. >> It's interesting, Bruno, you bring up some good points. I think about all the conversations that I've had over the years with pros around "disaster recovery" and continuous operations. This is a different vector of what that means, because when you highlighted earlier, IT, it's not like a hurricane or a power outage. This is a different kind of disruption. We talked about scale. What are some of the things that you're seeing right now that businesses are being faced with, that you guys are seeing in the analytics, or use cases that have emerged from this new normal that is facing today's business with this crisis. What's changed? What is this new challenge? When you think about the business continuity and how continuous operations need to be sustained because, again, it's a different vector. It's not a blackout, it's not a hurricane. It's a different kind of disruption. It's one where the business needs to stay on more than ever. >> Yep. Correct. True. What's really interesting, and there are some relatively straightforward use cases that we're seeing. People are dealing with their authentication, VPN network issues, because everybody is low on bandwidth. Everybody is, all of these systems are at their breaking point because they're carrying more than they ever did. These are use cases that existed all along. The problem with the use cases that existed all along is that they've been slowly picking up and growing. This is the discontinuity right now. What's happened right now, all of a sudden you've got double, triple, quadruple the load, and you need to both scale up your infrastructure, scale up your monitoring, be much more vigilant about that monitoring, speed up your recovery because more is at stake, and all of those things. That's the generic use case that existed all along, but have not been in this disruptive type of operating environment. Second is, enterprises are now learning very quickly what they need to do in terms of scaling and monitoring their production, customer-facing infrastructure, what used to be in the data center, the three-tier world, adding a few notes to an application, to your website over time, worked. Right now everybody is realizing that this whole bent on building our microservices, building for scale, rearchitecting and all that stuff, so that you can respond to an instantaneous burst of traffic on your site. You want to capture that traffic, because it means revenue. If you don't capture it, you miss out on it, and then customers go elsewhere, and never come back, and all that stuff. A lot of the work loads are to ensure that the systems, the mission-critical systems, are up and running. It's all about monitoring real-time telemetry, accelerating root cause analysis across systems that are cloud systems, and so on. >> It's a great point. You actually were leading into my next question I wanted to ask you. You know, the old saying goes, "Preparation meets opportunity. Those are the lucky ones." Luck is never really there. You're prepared, and opportunity. Can you talk about those people that have been prepared, that are doing it right now, or who are actually getting through this? What does preparation look like? What's that opportunity? Who's not prepared? Who's hurting the most? Who's suffering, and what could they do differently? Are you seeing any patterns out there, that people, they did their work, they're cloud native, they're scaled out, or they have auto-scaling. What are some of the things where people were prepared, and could you describe that, and on the other side where people weren't prepared, and they're hurting. Can you describe those two environments? >> Sure. Yeah. You think about the spectrum of companies that are going through digital transformation. There are companies who are on the left side. I don't know whether I'm mirroring or not. Basically, on the left side are people who are just making that transformation and moving to serving customers digitally, and on the right side are the ones that are basically all in, already there, and have been building modern architectures to support that type of transformation. The ones that are already all the way on the right, companies like us, right? We've been in this business forever. We serve customers who are early adopters of digital, so we've had to deal with things like November 6th, primary elections, and all of our media and entertainment customers who were spiking. Or we have to deal with companies that do sporting events like World Cup or Super Bowl and things like that. We knew that our business was going to always demand of us to be able to respond to both scheduled and unscheduled disruptions, and we needed to build systems that can scale to that without many human interactions. And there are many of our customers, and companies who are in that position today, who are actually able to do business and are now thriving, because they are the ones capturing market share at this point in time. The people who are struggling are people who have not yet made it to that full transformation, people who, essentially, assume business as normal, who are maybe beginning that transformation, but don't have the know-how, or the architecture, or the technology yet to support it. Their customers are coming to them through their new digital channels, but those digital channels struggle. You'll see this, more often than not you're going to find these still running in a traditional data center than in the cloud. Sometimes they're running in the cloud where they've done just a regular lift-and-shift instead of rearchitecting and things like that. There's really a spectrum, and it's really funny and amazing how much it maps to the journey in digital transformation, and how this specific thing is essentially, what's happening right now, it looks like the business environment demands everybody to be fully digital, but not everybody is. Effectively, the ones that are not are struggling more than the ones that are. >> Yeah. Certainly, we're seeing with theCUBE, with the digital events happening on our side, all events are canceled, so they've got to move online. You can't just take a physical, old way of doing something, where there's content value, and moving it to digital. It's a whole different ball game. There's different roles, there's different responsibilities. It's a completely different set of things. That's putting pressure on all these teams, and that's just one use case. You're seeing it in IT, you're seeing it happen in marketing and sales, how people are doing business. This is going to be very, very key for these companies. The data will be, ultimately, the key. You guys are doing a great job. I do want to get to the news, and I want to get the plug in for Sumo Logic. I want to say congratulations to you guys. A press release went out today from Sumo Logic. You guys are offering free cloud-based data analytics to support work from home and online classroom environments. That's great news. Can you just share and give a plug for that, PSA? >> Sure! We basically have a lot of customers who, just like us, are now starting to work from home. As soon as this began, we got inbound demands saying, "Oh, could you get, do you have an application for this, "do you have some analytics for that, "things that support our work from home." We thought hey, why don't we just make this as a package, and actually build out-of-the-box solutions that can support people who have common working from home technologies that they used to use for 10% of their workforce, and now work for 100% of their workforce. Let's package those, let's push those out. Let's support educational institutions who are now struggling. I have two kids in here who are learning. Everything is online, right? We had to get another computer for them and all this stuff. They're younger, they're in fourth grade. They are doing this, I can see personally how the schools are struggling, how they're trying to learn this whole new model. They need to have their systems be reliable and resilient, and this is not just elementaries, but middle school, high schools, colleges have all expanded their on-premise teaching. So we said, "Okay. Let's do something to help the community "with what we do best." Which is, we can help them make sure that the things that they do, that they need to do for this remote workforce, remote learning, whatever it might be, is efficient, working, and secure. We packaged several bundles of these solutions and offered those for free for a while, so that both our customers, and non-customers, and educational institutions have something they can go and reach for when they are struggling to keep their systems up and running. >> Yeah, it's also a mindset change, too. They want comfort. They want to have a partner. I think that's great that you guys are doing for the community. Can you just give some color commentary on how this all went down? Did you guys have a huddle in your room, said, "Hey, this is a part of our business. "We could really package this up "and really push it out and help people." Is that how it all came together? Can you share some inside commentary on how this all went down and what happened? >> Yeah. Basically, we had a discussion, literally, I think, the first or the second day when we all were sent home. We got on our online meeting and sat down, and essentially learned about this inbound demand from our customers, and what they were looking to do. We were like, "Okay, why don't we, "why don't we just offer this? "Why don't we package it?" It was a cross-functional team that just sat there. It was a no-brainer. Nobody was agonizing over doing this for free or anything like that. We were just sitting there thinking, "What can we do? "Right now is the time for us to all "pull each other up and help each other. "It'll all sort itself out afterwards." >> You know, during the bubonic plague, Shakespeare wrote Macbeth during that time. You guys are being creative during this time, as the coronavirus, so props to you guys at Sumo Logic. Congratulations, and thanks for taking the time. Can you give some parting thoughts on it, for the folks who are working at home? Just some motivational inspiration from you guys? What's going to come next for you guys? >> Sure. And thank you for having me on this video. I would say that we have been making slow transition towards remote workforce as it is. In a lot of places around the world, it's not that easy to make it to an office. Traffic is getting worse, big centers are getting populated, real estate is getting more expensive, all of this stuff. I think, actually, this is an opportunity for enterprises, for companies, and for people to figure out how this is done. We can actually practice now. We're forced to practice. It might actually have positive impact on all industries. We are going to probably figure out how to travel less, probably figure out how to actually do this more effectively, the cost of doing business is going to go down, ability to actually find new jobs might broaden, because you might be able to actually find jobs at companies who never thought they could do this remotely, and now are willing to hire remote workforces and people. I think this is going to be all good for us in the end. Right now it feels painful, and everybody's scared, and all that stuff, but I think long term, both the transformation into digitally serving our customers and the transformation towards remote workforce is going to be good for business. >> Yeah. It takes a community, and we really appreciate the effort you guys make, making that free for people, the classrooms. Remember, Isaac Newton discovered gravity and calculus while sheltering in place. A lot of interesting, new things are going to happen. I appreciate it. >> Bruno: Absolutely. >> Bruno, thank you for taking the time and sharing your insights from your place, sheltering. I made a visit into the studio to get this interview and a variety of other interviews we're doing digitally here. Thanks for sharing. Appreciate your time. >> Thank you. Appreciate you as well. >> I'm John Furrier with theCUBE here. CUBE conversation with Bruno from Sumo Logic sharing his perspective on the COVID-19. The impact, the disruption and path to the future out of this, and the new normal that is going to change our lives. Thanks for watching.

>>live from Las Vegas. It's the two covering AWS executive Something >>brought to you by Accenture >>everyone to the ex Center Executive Summit here in AWS. Reinvent I'm your host, Rebecca Knight. I'm joined by John. Match it. He is the managing director. Applied Intelligence, North America Attic Center Thank you so much for coming on the Q. So we're gonna have a fun conversation about a I today. We tend to think of a I as this futuristic Star Trek Jetsons kind of thing. But in fact, a i a. I is happening here and now >>it's all around us. I think it's intricate zoologist, sort of blood into the fabric girl of our lives without really even knowing about, I mean, just to get here, Let me lives took a new burst. There's a I in the route navigation. We may have listened to Spotify, and there's a I and the recommendation engine. And if you want to check the weather with Alexa, there's a lot of agents in the natural language processing, and none of that was really impossible 10 years ago. So without even trying, just wake up and I sort of like in your system in your blood. >>So as consumers, we deal with a I every day. But it's all but businesses are also using a I, and it's already having an impact. >>I think >>what is absolutely true it and really interesting is that information is just the new basis of competition. Like like you know, companies used to compete with physical objects and look better cars and blenders and stereos and, you know, thermometers. But today, you know, they're all like on a device, and so information is how they compete. And what's interesting to me about that for our clients is that if you have a good idea, you can probably do it. And so you're limited, really by your own imagination on. So I just as an example of like how things are playing out a lover classroom, the farmer space to make better drugs, and every every form of company I know of is using some sort of machine learning a I to create better pharmaceuticals, the big ones, but also the new entrance. One of the companies that we followed numerator really issued company. What they've been able to do is like in just just a massive amount of data like all day, like good data, bad bias on buying >>its ingesting, this kind of data the data is about. >>It's about like drug efficacy, human health, the human genome like like like doctors visits like all this diverse information. And historically, if you put all that data together just to have a way to actually examine it, there's no way that was too much. Humans can't deal with it, but but But machine learning can. And so what? We just all this date up and we let the robots decided sort of less meaningful. And what's happened is you can now deal with instead, just a very fraction that data, but all of it. And the result, like in pharmaceuticals. Is it wearable? Come with new HIV drugs in six months? It used to be years and millions of dollars, tens of millions of dollars. But now it's, you know, it's months, and so it's really changing the way humans live. And certainly the associated industries. They're producing the drugs. >>So it's as you said, I was already being used to reimagine medicine. So many of the high tech jobs openings today are not necessarily in technology there in pharmaceuticals and automotive's. And these and these involved artificial intelligence, their skills in artificial intelligence. What can you tell us about how a eyes having an impact? And that's what I think. >>This is a really good question. What is interesting is that industry she wouldn't think, or digital companies are now actually digital competitors. I'll give you two examples. One is a lot of clients make liquefied natural gas. Now that that is a mucky business. It's full of science, like geology and chemistry and chemical engineering, and they work with these like small refineries. But the questions like, how we gonna get better if you make you know Ellen G. And so what they do is they use a I, and the way they do that is likely have these small refineries. Each piece of equipment has a sensor on it, so there may be 5000 sensors, and each sensor has three or four like bots looking at it, and one might be looking at vibration heat and and what they're doing is they're making predictions. Millions of predictions every every day about you know whether quality is good. The machine's about to have a problem that safety is jeopardise something like that. And so So you've gone from a place where, you know, the best competitors were chemists to the best competitors are actually using machine learning to make the plants work better. You know, another entry. We see this really was brewing. You know, you don't think no one would think brewing is like a digital business like his beer? The Egyptians may be right, like so everyone knows how to do it. So But think about if you make beer like how you're gonna get better and again do what you do is you begin to touch customers more effectively with better digital marketing, you know? Hey, I tow target to understand who your best customers are, how to make offers to them, had a price head of both new product introduction, and even had a formulate new brands of beer that might appeal to different segments of society. So brewing, like they're all about, like ml in the eye. And they really are, like a digital competitive these days, which I think it's interesting, like no one would have thought about that, you know, is they were consuming beer on a Friday with their friends >>and craft brewing is so hot right now. I mean, it is one of those things. As you said, it is attracting new, different kinds of segments of customers. >>Right? And so the questions like if you are a craft brewer like, how do you go find the people that that you want? So what we're doing is we're way have new digital ways to go touch them very personalized offer like, if you like running, you know we can We can give you an offer like fun run followed by a brew. But we know who you are and what you like your friends like to do to get very specific A CZ we like examined the segments of society to do very personal marketing. It's actually fun, like, you know, it gives you things to go Dio we did one event where he looked at cos we we had a a beer tasting with barbecue teach you no instruction. So if you wanna learn how to cook barbecue and also do a beer tasting can get 20 people together and you have a social experience and you you buy more the product. But what's interesting is like, Well, how do you find those people? How do you reach them? How do you identify these of the right folks? That'll actually participate? And that's where a I comes into play. >>So this is fascinating, and you just you just described a number of different industries and companies beer, brewers, liquefied natural gas, pharmaceuticals that are using a I to transform themselves. What is your What do you recommend for the people out there watching and say, I want to do that? How could I get on >>board or what we advise Companies are clients to really get good at three things, and the first is just to do things differently. So you got to go into your core operations and figure out how you can extract more cash and more profit from your existing operations. And so that's like we talked about natural gas, right? Like you could produce it more profitably and effectively, but that's not enough. The next thing you do step to would be to actually grow your core business. Everyone wants to leave to the new right away, but but you're getting all your cash and your legacy businesses and so like like we saw in the brewing history. If you can find new customers, more profitable customers interact with them, create a better digital experience with them, then you'll grow both your top line in your bottom line. But for our from our perspective, the reason you do both of those things is cash. Then make investments into New Net new businesses on DSO. The last thing you do is to do different things, so find in adjacency and grow. And it's important to talk about the role of a I and that because that's the way you develop outcomes with speed, right? Like you're not gonna build a factory and we're gonna build a service or some sort of, you know, information centric offerings. And so what we like to do is talk about like the wise pivot from your old legacy businesses. We generate cash and you make selective investments in the new and how you regulate that is a really important question, because you're too fast and you start the Lexie businesses like to slow, and you're gonna be sort of left out of the new economy. So doing those three things correctly with the right sort of managing processes is what we advise our clients to focus on. >>So I see all of this from the business side. But do you because you're also a consumer? Do you ever see any sort of concerns about privacy and security in the sense of why does anyone need to know if I like to run or I like barbecue with my beer? I mean, how do you How do you sort of think about those things and and talk to clients about those issues >>too? Well, I think, you know, actually, for censure. Ah, large part of our focus is what we call just ethical a eye on. And so it's important to us to actually have offerings that we think that we're comfortable with that are legally comfortable, but also just societally are acceptable. And it's actually like there's a lot of focus in this area, right, how you do it. And there's actually a lot to learn. Like like what we see, for example, is there could be biased in the data which effects the actual algorithm. So a lot of times were the folks in the algorithm, you need to go back to the data and look at that. But it's something we spend a lot of time on. Its important us because we to our consumers and we care about our privacy. >>So when you talk about the wise pivot and the regulation, this is a This is a big question. There's a lot of bills on the table in Washington. It's certainly dominating our national conversation, how we think about regulating thes new emerging technologies that that present a lot of opportunities, but also a lot of risks. So how how are you, how you are you a tech center thinking about regulation and working with regulators on these issues >>way get involved with talking to the government. They seek independent counsel, so we participate when they're seeking guidance and we'll give our offer. So we're a voice at the table. But you know, what I would say is there's a lot of discussion about privacy and ask. But if you look at, like, at a national level, particularly government, I think there used to be more focused just on the parts that are incontrovertibly not problematic with privacy. So I gave you the example of working with liquefied natural gas. Okay, we need better, eh? I'd run our factories better. There's a lot of a I that goes into those kind of problems or supply chain planning. Like, how do I predict demand more effectively, or where should I put my plants? And A. I is the new way supply chain is done right? And so there's There's very few of the consumer centric problems I think, actually is. A society like 90% of the use cases are gonna be in areas where they don't actually influence for privacy and a lot of art. Our time is actually working on those kind of use cases just to make you know the operations of our organization's Maur more effective than more efficient. >>So we talked about the very beginning of this conversation about the companies that are disrupting old industries. Using a lot of these technologies, I mean, is this is a I A case where you need to be using this you need to be using >>you need to be using it. My view, my personal view is that there is going to be no basis of competition in the future, except for a digital. It just is going to be the case. And so all of our clients, you know, they're at some state of maturity and they're all asking the question like, How did I grow up? I don't get more profitable. Like certainly the street. Once more results on DSO if you want to move quickly in the new space, is you. You you you only have 11 choice. Really? And that that is to get really, really, really good at managing in harnessing digital technologies, inclusive of >>a I >>two to compete in a different way. And so I mean, we're seeing really interesting examples were like, you know, like, retailers are getting into health care, right? Like, you see this like you go into Wal Mart and they have our Walgreens. They have, like a doc in the box, right? So we're seeing. But lots of companies that are making physical things that then turn around and use the developing service and what they used to use their know how they take everything they know about, like like something you know about, like healthcare or how to like, you know, offer service is to customers and retail setting, but then they need to do something different. And now how do I get the data and the know how to then offer, like a new differentiated health service? And so to do that, you know, you have a lot. You have a lot of understanding about your customers, but you need to get all the data sources in place. You may need certain help desk. You know you need ways to aggregate it on, and so you probably need a new partnerships that don't have. You probably need toe manage skill sets that you don't have. You may need to get involved with open source communities. You may need to be involved with universities that where they do research, so you'll need a different kind of partnerships to move a speed then companies have probably used in the past. But when they put all those those eco systems together, onda new emphasis on the required skill sets, they can take their legacy knowledge that's probably physically oriented and then create a service that can create. They can monetize their experience with the new service. What what we find usually doesn't work is just a monetized data. If you have a lot of data, it's not usually worth that much. But if you take the data and you create a new service that people care about, then you can monetize your legacy information that that that's what a lot of our class they're trying to do, think they've very mature and now, like Where do you go? And where they go is something may be nearby to their existing business, but it's not. It's not the same legacy business of the path for years. >>I want to take a little deeper on something you brought up about the skills, and there's a real skills gap in Silicon Valley and in companies in this area. How are you working with companies to make sure that they are attracting the right talent pool and retaining those workers once they have? Um, >>well, so this is, I think, one of the most important questions because, like what? What happened with technology in the past? We would put in these like ear piece systems, and that was a big part of our business, like 15 years ago. And once you learned one of those things, that's a P or oracle or, you know, like whatever your skill set was good for 10 years, You probably you were good. You could just, like, go to the work. But today it just just go down to like the convention center. Look at this vast array of like like >>humanity, humanity >>and new technologies. I mean, half these companies didn't even exist, like, five years ago, right? And so you're still set today is probably only good for a year. So I think the first thing you've got to realise is that there's got to be a new focus on actually cultivating talent as a strategy. It's it's the way to compete like people is your product, if you wanna look at that way. But we're doing actually starting very, uh, where we can very early in the process, like much beyond a corporation. So we work with charter schools over kids, we get them into college, we work with universities, we do a lot of internship. So we're trying to start, like, really early on when you ask a question like, what would our recommendation to the government be were actually advising, like, get kids involved in I t. Like earlier and so so we can get that problem resolved but otherwise, once companies work. I think you know you need your own talent strategy. But part of that might be again, like an eco system play like maybe you don't want all of those people and you'd rather sort of borrow on. And so I think, I think figuring out what your eco system is because I think I think in the future like competition will be like my eco system versus your eco system. And that's that is the way I think it's gonna work. And so thinking in an eco system way is, is what most of our clients need to do. >>Well, it's like you said about the old ways of it was a good idea for a good product versus good ideas. And I just keep looking. Thank you so much, John, for coming on the Cuba Really fascinating conversation >>was my pleasure. Thank you so much. >>I'm Rebecca Knight. Stay tuned for more of the cubes. Live coverage of the Accenture Executive Summit coming up in just a little bit

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

(hopeful futuristic music) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We are really excited to have this cube conversation here in the Palo Alto studio with a real close friend of theCUBE, and repeat alumni, Leslie Berlin. I want to get her official title; she's the historian for the Silicon Valley archive at Stanford. Last time we talked to Leslie, she had just come out with a book about Robert Noyce, and the man behind the microchip. If you haven't seen that, go check it out. But now she's got a new book, it's called "Troublemakers," which is a really appropriate title. And it's really about kind of the next phase of Silicon Valley growth, and it's hitting bookstores. I'm sure you can buy it wherever you can buy any other book, and we're excited to have you on Leslie, great to see you again. >> So good to see you Jeff. >> Absolutely, so the last book you wrote was really just about Noyce, and obviously, Intel, very specific in, you know, the silicon in Silicon Valley obviously. >> Right yeah. >> This is a much, kind of broader history with again just great characters. I mean, it's a tech history book, but it's really a character novel; I love it. >> Well thanks, yeah; I mean, I really wanted to find people. They had to meet a few criteria. They had to be interesting, they had to be important, they had to be, in my book, a little unknown; and most important, they had to be super-duper interesting. >> Jeff Frick: Yeah. >> And what I love about this generation is I look at Noyce's generation of innovators, who sort of working in the... Are getting their start in the 60s. And they really kind of set the tone for the valley in a lot of ways, but the valley at that point was still just all about chips. And then you have this new generation show up in the 70s, and they come up with the personal computer, they come up with video games. They sort of launch the venture capital industry in the way we know it now. Biotech, the internet gets started via the ARPANET, and they kind of set the tone for where we are today around the world in this modern, sort of tech infused, life that we live. >> Right, right, and it's interesting to me, because there's so many things that kind of define what Silicon Valley is. And of course, people are trying to replicate it all over the place, all over the world. But really, a lot of those kind of attributes were started by this class of entrepreneurs. Like just venture capital, the whole concept of having kind of a high risk, high return, small carve out from an institution, to put in a tech venture with basically a PowerPoint and some faith was a brand new concept back in the day. >> Leslie Berlin: Yeah, and no PowerPoint even. >> Well that's right, no PowerPoint, which is probably a good thing. >> You're right, because we're talking about the 1970s. I mean, what's so, really was very surprising to me about this book, and really important for understanding early venture capital, is that now a lot of venture capitalists are professional investors. But these venture capitalists pretty much to a man, and they were all men at that point, they were all operating guys, all of them. They worked at Fairchild, they worked at Intel, they worked at HP; and that was really part of the value that they brought to these propositions was they had money, yes, but they also had done this before. >> Jeff Frick: Right. >> And that was really, really important. >> Right, another concept that kind of comes out, and I think we've seen it time and time again is kind of this partnership of kind of the crazy super enthusiastic visionary that maybe is hard to work with and drives everybody nuts, and then always kind of has the other person, again, generally a guy in this time still a lot, who's kind of the doer. And it was really the Bushnell-Alcorn story around Atari that really brought that home where you had this guy way out front of the curve but you have to have the person behind who's actually building the vision in real material. >> Yeah, I mean I think something that's really important to understand, and this is something that I was really trying to bring out in the book, is that we usually only have room in our stories for one person in the spotlight when innovation is a team sport. And so, the kind of relationship that you're talking about with Nolan Bushnell, who started Atari, and Al Alcorn who was the first engineer there, it's a great example of that. And Nolan is exactly this very out there person, big curly hair, talkative, outgoing guy. After Atari he starts Chuck E. Cheese, which kind of tells you everything you need to know about someone who's dreaming up Chuck E. Cheese, super creative, super out there, super fun oriented. And you have working with him, Al Alcorn, who's a very straight laced for the time, by which I mean, he tried LSD but only once. (cumulative laughing) Engineer, and I think that what's important to understand is how much they needed each other, because the stories are so often only about the exuberant out front guy. To understand that those are just dreams, they are not reality without these other people. And how important, I mean, Al Alcorn told me look, "I couldn't have done this without Nolan, "kind of constantly pushing me." >> Right, right. >> And then in the Apple example, you actually see a third really important person, which to me was possibly the most exciting part of everything I discovered, which was the importance of the guy named Mike Markkula. Because in Jobs you had the visionary, and in Woz you had the engineer, but the two of them together, they had an idea, they had a great product, the Apple II, but they didn't have a company. And when Mike Markkula shows up at the garage, you know, Steve Jobs is 21 years old. >> Jeff Frick: Right. >> He has had 17 months of business experience in his life, and it's all his attack for Atari, actually. And so how that company became a business is due to Mike Markkula, this very quiet guy, very, very ambitious guy. He talked them up from a thousand stock options at Intel to 20,000 stock options at Intel when he got there, just before the IPO, which is how he could then turn around and help finance >> Jeff Frick: Right. >> The birth of Apple. And he pulled into Apple all of the chip people that he had worked with, and that is really what turned Apple into a company. So you had the visionary, you had the tech guy, you also needed a business person. >> But it's funny though because in that story of his visit to the garage he's specifically taken by the engineering elegance of the board >> Leslie Berlin: Right. >> That Woz put together, which I thought was really neat. So yeah, he's a successful business man. Yes he was bringing a lot of kind of business acumen value to the opportunity, but what struck him, and he specifically talks about what chips he used, how he planned for the power supply. Just very elegant engineering stuff that touched him, and he could recognize that they were so far ahead of the curve. And I think that's such another interesting point is that things that we so take for granted like mice, and UI, and UX. I mean the Atari example, for them to even think of actually building it that would operate with a television was just, I mean you might as well go to Venus, forget Mars, I mean that was such a crazy idea. >> Yeah, I mean I think Al ran to Walgreens or something like that and just sort of picked out the closest t.v. to figure out how he could build what turned out to be Pong, the first super successful video game. And I mean, if you look also at another story I tell is about Xerox Park; and specifically about a guy named Bob Taylor, who, I know I keep saying, "Oh this might be my favorite part." But Bob Taylor is another incredible story. This is the guy who convinced DARPA to start, it was then called ARPA, to start the ARPANET, which became the internet in a lot of ways. And then he goes on and he starts the computer sciences lab at Xerox Park. And that is the lab that Steve Jobs comes to in 1979, and for the first time sees a GUI, sees a mouse, sees Windows. And this is... The history behind that, and these people all working together, these very sophisticated Ph.D. engineers were all working together under the guidance of Bob Taylor, a Texan with a drawl and a Master's Degree in Psychology. So what it takes to lead, I think, is a really interesting question that gets raised in this book. >> So another great personality, Sandra Kurtzig. >> Yeah. >> I had to look to see if she's still alive. She's still alive. >> Leslie Berlin: Yeah. >> I'd love to get her in some time, we'll have to arrange for that next time, but her story is pretty fascinating, because she's a woman, and we still have big women issues in the tech industry, and this is years ago, but she was aggressive, she was a fantastic sales person, and she could code. And what was really interesting is she started her own software company. The whole concept of software kind of separated from hardware was completely alien. She couldn't even convince the HP guys to let her have access to a machine to write basically an NRP system that would add a ton of value to these big, expensive machines that they were selling. >> Yeah, you know what's interesting, she was able to get access to the machine. And HP, this is not a well known part of HP's history, is how important it was in helping launch little bitty companies in the valley. It was a wonderful sort of... Benefited all these small companies. But she had to go and read to them the definition of what an OEM was to make an argument that I am adding value to your machines by putting software on it. And software was such an unknown concept. A, people who heard she was selling software thought she was selling lingerie. And B, Larry Ellison tells a hilarious story of going to talk to venture capitalists about... When he's trying to start Oracle, he had co-founders, which I'm not sure everybody knows. And he and his co-founders were going to try to start Oracle, and these venture capitalists would, he said, not only throw him out of the office for such a crazy idea, but their secretaries would double check that he hadn't stolen the copy of Business Week off the table because what kind of nut job are we talking to here? >> Software. >> Yeah, where as now, I mean when you think about it, this is software valley. >> Right, right, it's software, even, world. There's so many great stories, again, "Troublemakers" just go out and get it wherever you buy a book. The whole recombinant DNA story and the birth of Genentech, A, is interesting, but I think the more kind of unique twist was the guy at Stanford, who really took it upon himself to take the commercialization of academic, generated, basic research to a whole 'nother level that had never been done. I guess it was like a sleepy little something in Manhattan they would send some paper to, but this guy took it to a whole 'nother level. >> Oh yeah, I mean before Niels showed up, Niels Reimers, he I believe that Stanford had made something like $3,000 off of the IP from its professors and students in the previous decades, and Niels said "There had to be a better way to do this." And he's the person who decided, we ought to be able to patent recombinant DNA. And one of the stories that's very, very interesting is what a cultural shift that required, whereas engineers had always thought in terms of, "How can this be practical?" For biologists this was seen as really an unpleasant thing to be doing, don't think about that we're about basic research. So in addition to having to convince all sorts of government agencies and the University of California system, which co-patented this, to make it possible, just almost on a paperwork level... >> Right. >> He had to convince the scientists themselves. And it was not a foregone conclusion, and a lot of people think that what kept the two named co-inventors of recombinant DNA, Stan Cohen and Herb Boyer, from winning the Nobel Prize is that they were seen as having benefited from the work of others, but having claimed all the credit, which is not, A, isn't fair, and B, both of those men had worried about that from the very beginning and kept saying, "We need to make sure that this includes everyone." >> Right. >> But that's not just the origins of the biotech industry in the valley, the entire landscape of how universities get their ideas to the public was transformed, and that whole story, there are these ideas that used to be in university labs, used to be locked up in the DOD, like you know, the ARPANET. And this is the time when those ideas start making their way out in a significant way. >> But it's this elegant dance, because it's basic research, and they want it to benefit all, but then you commercialize it, right? And then it's benefiting the few. But if you don't commercialize it and it doesn't get out, you really don't benefit very many. So they really had to walk this fine line to kind of serve both masters. >> Absolutely, and I mean it was even more complicated than that, because researchers didn't have to pay for it, it was... The thing that's amazing to me is that we look back at these people and say, "Oh these are trailblazers." And when I talked to them, because something that was really exciting about this book was that I got to talk to every one of the primary characters, you talk to them, and they say, "I was just putting one foot in front of the other." It's only when you sort of look behind them years later that you see, "Oh my God, they forged a completely new trail." But here it was just, "No I need to get to here, "and now I need to get to here." And that's what helped them get through. That's why I start the book with the quote from Raiders of the Lost Ark where Sallah asks Indy, you know basically, how are you going to stop, "Stop that car." And he says, "How are you going to do it Indy?" And Indy says, "I don't know "I'm making it up as I go along." And that really could almost be a theme in a lot of cases here that they knew where they needed to get to, and they just had to make it up to get there. >> Yeah, and there's a whole 'nother tranche on the Genentech story; they couldn't get all of the financing, so they actually used outsourcing, you know, so that whole kind of approach to business, which was really new and innovative. But we're running out of time, and I wanted to follow up on the last comment that you made. As a historian, you know, you are so fortunate or smart to pick your field that you can talk to the individual. So, I think you said, you've been doing interviews for five or six years for this book, it's 100 pages of notes in the back, don't miss the notes. >> But also don't think the book's too long. >> No, it's a good book, it's an easy read. But as you reflect on these individuals and these personalities, so there's obviously the stories you spent a lot of time writing about, but I'm wondering if there's some things that you see over and over again that just impress you. Is there a pattern, or is it just, as you said, just people working hard, putting one step in front of the other, and taking those risks that in hindsight are so big? >> I would say, I would point to a few things. I'd point to audacity; there really is a certain kind of adventurousness, at an almost unimaginable level, and persistence. I would also point to a third feature at that time that I think was really important, which was for a purpose that was creative. You know, I mean there was the notion, I think the metaphor of pioneering is much more what they were doing then what we would necessarily... Today we would call it disruption, and I think there's a difference there. And their vision was creative, I think of them as rebels with a cause. >> Right, right; is disruption the right... Is disruption, is that the right way that we should be thinking about it today or are just kind of backfilling the disruption after the fact that it happens do you think? >> I don't know, I mean I've given this a lot of thought, because I actually think, well, you know, the valley at this point, two-thirds of the people who are working in the tech industry in the valley were born outside of this country right now, actually 76 percent of the women. >> Jeff Frick: 76 percent? Wow. >> 76 percent of the women, I think it's age 25 to 44 working in tech were born outside of the United States. Okay, so the pioneering metaphor, that's just not the right metaphor anymore. The disruptive metaphor has a lot of the same concepts, but it has, it sounds to me more like blowing things up, and doesn't really thing so far as to, "Okay, what comes next?" >> Jeff Frick: Right, right. >> And I think we have to be sure that we continue to do that. >> Right, well because clearly, I mean, the Facebooks are the classic example where, you know, when he built that thing at Harvard, it was not to build a new platform that was going to have the power to disrupt global elections. You're trying to get dates, right? I mean, it was pretty simple. >> Right. >> Simple concept and yet, as you said, by putting one foot in front of the other as things roll out, he gets smart people, they see opportunities and take advantage of it, it becomes a much different thing, as has Google, as has Amazon. >> That's the way it goes, that's exactly... I mean, and you look back at the chip industry. These guys just didn't want to work for a boss they didn't like, and they wanted to build a transistor. And 20 years later a huge portion of the U.S. economy rests on the decisions they're making and the choices. And so I think this has been a continuous story in Silicon Valley. People start with a cool, small idea and it just grows so fast among them and around them with other people contributing, some people they wish didn't contribute, okay then what comes next? >> Jeff Frick: Right, right. >> That's what we figure out now. >> All right, audacity, creativity and persistence. Did I get it? >> And a goal. >> And a goal, and a goal. Pong, I mean was a great goal. (cumulative laughing) All right, so Leslie, thanks for taking a few minutes. Congratulations on the book; go out, get the book, you will not be disappointed. And of course, the Bob Noyce book is awesome as well, so... >> Thanks. >> Thanks for taking a few minutes and congratulations. >> Thank you so much Jeff. >> All right this is Leslie Berlin, I'm Jeff Frick, you're watching theCUBE. See you next time, thanks for watching. (electronic music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Voiceover: Live from Las Vegas, it's theCUBE covering VMworld 2017, brought to you by VMware and it's ecosystem partners. (electronic music) >> Hello everyone, welcome back to live coverage. This is theCUBE at VMworld 2017, our eighth year covering VMworld, going back to 2010. I'm John Furrier, co-host of theCUBE, and my co-host this segment, Justin Warren, industry analyst, and our guest, Alan Cohen, Chief Commercial Officer, COO for Illumio. Great to see you, CUBE alumni. Special guest appearance, guest analyst appearance, but also Chief Commercial Officer, Illumio is a security start-up, growing. Thanks for coming on. >> It's not even a startup anymore. >> Justin: It's technically a startup. >> John: After five years, it's not a startup. >> It's not a startup right, you raise $270 million, it's not exactly a startup. >> (laughs) That's true. Well, welcome back. >> Alan: Thank you. >> Welcome back from vacation. Justin and I were talking before you came on, look at, let's go get you on and get some commentary going. >> Alan: Okay. >> You're an industry vet, again, in security, some perspective, but industry perspective, you've seen this VMware cycle many times. What's your analysis right now, obviously stock's 107, they don't to a cloud, no big catback, so it's good. You've made a decision. What's your take on this? >> I've been coming to VMworld for a long time, as you guys have as well, and from my perspective, this was probably the biggest or most significant transition in the history of the company. If you think about the level of dialogue, obviously there's a lot about NSX, which came from the Nicira, I'm always happy about. But, if you hear about, talking about cloud, and kind of talking about a post-infrastructure world, about capabilities, about control, about security, about being able to manage your compute in multiple environments, this is, I think, the beginning of a fundamentally different era. I always think about VMware, this is the company that defined virtualization. No one will argue with that point, so when they come out and they start talking about how are your computes going to operate in multiple environments? And how you're going to put that together, this is not cloud-washing, this is a fairly, all right they have fully acknowledged that the cloud is not a fad, the cloud is not for third tier workloads, this is mainstream computing. I think this is the third wave of computing and VMware is starting to put its markers down for the type of role that it intends to play in this transition. >> Yeah, I agree. >> We have to argue if you don't agree (laughs). >> I'll mostly agree with you, how about that? >> All right that's good. >> At this show, VMware has stopped apologizing for existing. I think, previously, they've been trying to say, "No, no we're a cloud too, "in fact, we're better than cloud "and you shouldn't be using it." It forced customers to choose between two of their children, really, like which one do you love more? And customers don't like that. Whereas at this show, I think it's finally being recognized that customers want to be able to use cloud, as well as use VMware, so that they're taking a more partnership approach to that and it's more about the ecosystem. And, agree, they're not about the infrastructure so much, they're not about the Hypervisor, they're about what you run on top of that. But, I still think there's a lot of infrastructure in that because VMware is fundamentally an infrastructure. >> Alan: Well, you got to get paid, right? >> That's right, (Alan laughs) and there's a lot of stuff out there that's already on VMware. What do you think about the approach? Like with cloud, they have a lot of people doing things in new ways and you mentioned this is the third wave of computing that we're doing it a new way. A lot of VMware stuff is really the whole reason it was popular is that we have people doing things a particular way on physical hardware and then they kept doing more or less the same thing, only on virtual hardware. What do you say about people who are still essentially going to be doing virtual hardware, they're just running it on cloud now? That's not really changing much. >> The way I think about it is: Are you going to be the Chevy Volt or are you going to be a Tesla? What I mean by that, and by the way now GM has the Bolt, which is their move toward Tesla, which is that if you look at the auto industry, they talk about hybrid and you talk about it, and you talk to Elon Musk and he goes, "Hybrids are bullshit." Either you're burning gas, or you're using electricity. To me, this cloud movement is about electricity, which is: I'm going to use cloud-native controls, I'm going to use cloud-native services, I'm going to be using Python and Ruby, and I'm going to have scripting, and I'm going to act like DevOps. And so, cloud is not just a physical place where I rent cycles from Amazon or Azure, it is a way of computing that's got a distributed, dynamic, heterogeneous, and hybrid. When you're in your virtualization on top of cloud, you're still in your Chevy Volt moment, but when you say, "I'm going to actually be native "across all of these environments," then you're really moving into the Tesla movement. >> Hold on. Let me smoke a little bit, I'll pass it over to you because that's complete fantasy. Right now the reality is, is that-- >> It's legal here in LA, in Las Vegas. >> (laughs) I don't think so yet, is it? >> Only outside. >> You can go to Walgreens across the street. >> Whatever you're smoking is good stuff. No, I agree, cloud obviously as a future scenario, there's no debate, but the reality is, like the Volt, Tesla is a one-trick pony. So, greenfield-- >> But, once again, I'm not disagreeing with you, John, but my point is that VMware and most of the IT industry is not there. Most companies don't have DevOps people, you run up and down, you go to all of these shows, ask these guys how many of these guys does Ruby, Python, real scripting, they don't do that. They still have Lu-Wise and management consults and they have the old IT, but this is the beginning movement-- >> They've got legacy bag, I mean we call it legacy baggage in the business, we know what that is. >> Heritage systems. (all laugh) >> Well, Gelsinger was here, I had him in at one o'clock and I kind of, sometimes VMware, they make the technical mistake in PR, they don't really get sometimes where to position things, but the Google announcement was very strategic intent, but they kind of made it a land grab and they tried to overplay their hand, in my opinion, on that one thing, it's strategic intent. This audience, they're not DevOps ready, they're Ops trying to do Dev, so they're not truly ready. So, it's okay to say, "Here's Amazon. "Great, that's today, if you want to do that, "let's get going, checking the boxes, "we're hitting the milestones." And then to dump a headroom deal announcement, that's more headroom, which is cool, but not push it on the Ops guys. >> Here's the opportunity and here's the risk: If Amazon is a $16 billion a year business, it's a rounding error in IT spend. When you take the hype away, nothing against it, and I love that prices are cheaper at Amazon and you can buy a Dot in the fruit aisle, that would totally-- >> John: I think the margins are like 60% (laughs). >> On your cloud. >> My wife took a picture of a rib steak and it said $18, now $13.99, I said, "Fantastic, thank you, Jeff Bezos. "We're eating well, "and we're going to have a little extra money." What I think this transition is not about infrastructure, it's about how IT people do their job. >> John: That's a main point. >> Justin: That's a big, big change. >> Yeah. >> Okay, in this show today doing your job, Justin I want you to comment on this because you were talking with Stu about it. I'm a VMware customer, what do I care about right now in my world? Just today. >> Well, in my world I've got conflicting things, I need to get my job done now. There's nothing different about the IT job, really, which is a shame because some of it needs to change, but there is a gradual realization that it's not about IT, it's not about building infrastructure for the sake of, "Because I like shiny infrastructure." It's, "I'm being paid by my business "to do IT things in service of the business." I have customers who are buying Apples, or using Apple docs, you're laundering. >> In IT you're paid for an outcome. You don't create the outcome. The way IT works is business creates the outcome, IT helps fulfill the outcome, unless you work-- >> John: Is IT a department today? >> Yeah, it's still a department. >> It's still a department? >> Yeah, it is, but it's a department in the same way that, well finance is important, but it's actually the business. Sales is part, they're all integrated. In a really well-run business, they're all integrated. >> How do you know what a real business is? You go to a building, you go to the main offices, you visit the marketing floor, you visit the IT floor. Tell me what the decor is like. They'll tell you what they care about in a business. (John laughs) I've been in a lot of IT shops, not the beautiful shiny glass windows because it's perceived as a back office cost center. >> Digital transformation is always about taking costs, that's table stakes, but now some of the tech vendors need to understand that as you get more business focused, you got to start thinking about driving top line. >> You're also thinking about being in the product. For example, my company, we have three of the four top SAS vendors, as Illumio customers, we do the micro-segmentation for them. We're not their micro-segmentation, we're a component in the software they sell you guys. >> Justin: You're an input. >> Yeah, you are a commodity in the mix of what somebody's building and I think that's going to be one of the changes. The move to cloud, it's not rent or buy, it's not per hour per server, or call Michael Dell and send me a bunch of Q-series, or whatever the heck it's called, it's increasingly saying, "We have these outcomes, we have these dates, "we have these deliverables, "what am I doing to support that and be part of that?" >> Justin: That's it, it's a support function. It's a very important support function, but there's very few businesses, like digital transformation, I don't like that as a term-- >> What the heck does that mean? >> It means something to do with fingers. >> Alan: You use it a lot, what does it really mean, digital transformation? >> To me, first of all, I'm not a big hype person, I like the buzz word in the sense that it does have a relevance now in terms of doing business digitally means you're completely 100% technology-enabled in your business. That means IT is a power function, not a cost center, it's completely native, like electricity in the company-- >> Unless, let's say I have two customers, I have the Yellow Cab company of Las Vegas and I have Uber or Lyft as a customer. My role, as a technologist, or technology provider, is dramatically different in either one of those-- >> Digital transformation to me is a mindset of things like, "I'm going to do a blockchain, "I'm going to start changing the game, "I'm going to use technology "to change the value equation for my customer." It's not IT conversation in the sense of, let's buy more servers to make something happen for the guy who had a request in that saying, "Let's use technology digitally to change the outcomes." >> But, given that, if we assume that that's true, then there's two ways of doing that. Either we have the IT people need to learn more about business, or the business people need to learn more about IT. >> That's right. >> Which one do you think should happen? Traditionally-- >> I think they're on a collision course. >> I don't think you can survive as a senior executive in most businesses anymore by saying, "Oh, I'll get my CIO in here." >> I would like to believe that that's true, but when people say that it should be a strategic resource and so on, and yet we spend decades outsourcing IT to someone else. If it's really truly important to your business, why aren't you doing it yourself? >> Justin, it's a great question and here's my observations, just thinking out loud here. One, just from a Silicon Valley perspective, looking at entrepreneurial as a canary in the coalmine, you've seen over the past 10-15 years, recently past 10, entrepreneurs have become developer entrepreneurs, product entrepreneurs, have become very savvy on the business side. That's the programmer. When we see Travis with Uber, no VC, they got smart because they could educate themselves. AngelList, Venture Hacks, there's a lot of data out there, so I see some signs of developers specifically building apps because user design is really important, they are leading into, what I call, the street MBA. They're not actually getting an MBA, they don't read the Wall Street Journal, but they're learning about some business concepts that they have to understand to program. IT I think is still getting there, but not as much as the developers. >> Here's a great question that I've learned over the years, and look, I'm coming out of the IT side, as we all are. When I visit a customer and I try to sell them my product, my first question is, "If I didn't exist, what would you do? "And if you don't buy my product, what happens in your business?" And if they're saying, "I have this other alternative." Or it's like, "Ah, we'll do it next year." I mean, maybe I can sell them some product, but what they're really telling me is, "I don't matter." >> All right, let's change the conversation a little bit, just move to another direction I want to get your thoughts on. And I should have, on the intro, given you more prompts, Alan. You were also involved in Nicira, the startup that VMware had bought-- >> Alan: Before all this NSX stuff, I was early. >> Hold on, let me finish the intro. We've interviewed Martin Casado. Stu talks to us all the time, I'm sure Chess has been hearing on the other set, "Oh, hey Martin Casado." It was a great interview, of course they're on theCUBE directory. But, you were there when it was just developing and then boom, software-defined networking, it's going to save the world. NSX has become very important to VMware, what's your thoughts on that? What does the alumni from Nicira and that folks that are still here and outside of VMware think about what's it's turned into? Is it relevant? And where is it going? >> Look, I could have not predicted five years ago when Nicira was acquired by VMware, it would be the heart of everything that their CEO and their team is talking about, if you want to know if that's important, go to the directory of sessions and one out of every three are about NSX. But, I think what it really means is there's a recognition that the network component, which is what really NSX represents, is the part that's going to allow them to transcend the traditional software-defined data center. I have two connections, so Steve Herrod is my investor, Steve is the inventor of the software-defined data center. That was the old Kool-Aid, not the new Kool-Aid. We've left the software-defined data center, we've moved into this cloud era and for them NSX is their driving force on being able to extend the VMware control plane into environments they used to never play in before. That's imminently clear. >> John: Justin, what's your take on NSX? >> NSX is the compatibility mechanism for being able do VMware in multiple places, so I think it's very, very important for VMware as a company. I don't think it's the only solution to that particular problem of being able to have networks that move around, it's possible to do it in other ways. For example, cloud-native type things, will do the networking thing in a different way. But, the network hasn't really undergone the same kind of change that happened in server or it did in storage, it's been pretty much the same for a long, long time. >> You've had an industry structurally dominated by one company, things don't change when-- >> Justin: And it still is, yeah. >> John: Security, security, because we've got a little bit of time I want to get to security. You guys are in the security space. >> Thanks for noticing. >> (laughs) I still don't know what you did, I'm only kidding. Steve Harrod is your investor, former CEO of VMware, very relevant for folks watching. Guys, security Pat Gelsinger said years ago it should be a duo, we've got to fix this. Nothing has really happened. What is the state of the union, if you will, of security? Where the frig is it going? What the hell's going on with security? >> There's two issues with that. If we put our industry analyst hat on, security is the largest segment of IT where nobody owns 5% market share, so there's not gorilla force that can drive that. VMware was the gorilla force driving virtualization, Cisco drove networking, EMC, in the early days, drove storage, but when you get to security you have this kind of-- >> John: Diluted. >> It's like the Balkans, it's like feudal states. >> Justin: It's a ghastly nightmare. >> What I think what Pat was talking about, which we also subscribe to, there are some movements in security, which micro-segmentation is one of them, which are kind of reinstalling a form of forensic hygiene into saying, "Your practices, if they occur, "they will reduce the risk profile." But, I think 50% of the security solutions and categories-- >> So, if I've lost my teeth, I don't get cavities. That kind of thing going on. >> If you're a doctor and you're making rounds in the hospital, you wash your hands or you put on gloves. >> And that's where we are. That is the stage we are at with security is we're at the stage where surgeons didn't believe they should wash their hands because they knew better and they'd say, "No, this couldn't possibly be making patients sick." People have finally realized that people get sick and the germ theory is real and we should wash our hands. >> Your network makes you sick. Your network is the carrier. Everything that's happening in network is effectively the Typhoid Mary of security. (John laughs) We're building flat, fast, unsegmented Layer 3 networks, which allow viruses to move at the speed of light across your environment. So, movements like, what's that called App Defense? >> Justin: App Defense, yep. >> App Defense or micro-segmentation from Illumio and Vmware, are the kind of new hygiene and new practices that are going to reduce the wide-spread disease growing. >> From an evolution theory, then the genetics of networks are effed up. This is what you're saying, we need to fix-- >> No, the networks are getting back to what they were supposed to do. Networks move packets from point A to point D. >> The dumb network? >> Alan: Yes, the dumber the better. >> Okay. You agree? >> Alan: Dumb them down. >> Dumb networks, smart end points. Smart networks doesn't scale as well as smart end points, and we're seeing that with edge computing, for example. Distributed networking is a hard problem and there is so much compute going out there, everything has a computer in it, they're just getting tinier and tinier. If we rely on the network to secure all of that, we're doomed. >> Better off at the end point. And this fuels the whole IoT edge thing, straight up one of the key wave slides out there. >> What you're going to have is a lot of telemetry points and you're going to have a lot of enforcement points. Our architecture is compatible with this, VMware is moving in this direction, other people are, but the people that are clinging to the gum up my network with all kinds of crap, because actually people want it to go the other way. If you think about it, the Internet was built to move packets from point A to point B in case of a nuclear war and, other than routing, there wasn't a whole lot-- >> We still might have that problem (laughs) >> Yeah, well there's always that (laughs). >> Fingers crossed. >> Guys, we got to break, next segment. Al, I'll give you the last word, just give a quick plug for Illumio. Thanks for coming on and being a special guest analyst, as usual, great stuff. Little slow from vacation, you're usually a little snappier. >> Alan: Little slow off the vacation mark. >> Yeah, come on. Back in Italy-- >> Too much Brunello di Motalcino, yeah. >> John: (laughs) Quick plug for Illumio, do a quick plug. >> We're really great to be here. John, you and I talked recently, Illumio is growing very rapidly, clearly we are probably emerging as one of the leaders in this micro-segmentation movement. >> John: A wannabe gorilla. >> What's that? >> You're a wannabe gorilla, go big or go home. >> We are, well, gorillas have to start as little gorillas first, we're not a wannabe gorilla, we're just gorillas growing really rapidly. It takes a lot more food at the zoo to keep us going. About 200 people growing rapidly, just moved into Asia, Pat, we got a guy in your part of the world we work with. >> First of all, it's not a zoo, it's still a jungle. The zoo is not yet established. >> That's true. We're going to establish the zoo. Things are great at Illumio. We have amazing things on the floor here today of, basically the system will actually write its own security policy for you. It's a lot of movement into machine learning, a lot of good stuff. >> All right. Guys, thanks so much. Alan Cohen with Illumio, >> Alan: Thank you. >> Chief Commercial Officer. And Justin Warren, analyst, I'm John Furrier. More live coverage from VMworld after this short break. (electronic music)

(upbeat music) [Announcer] Live from the Wigwam in Pheonix, Arizona, it's the Cube. Covering Data Platforms 2017. Brought to you by Cue Ball. >> Hey welcome back everybody, Jeff Frick here with the Cube. We are coming down to the end of a great day here at the historic Wigwam at the Data Platforms 2017, lot of great big data practitioners talking about the new way to do things, really coining the term data ops, or maybe not coining it but really leveraging it, as a new way to think about data and using data in your business, to be data-driven, software-defined, automated solution and company. So we're excited to have Saket Saurabh, he is the, and I'm sorry I butchered that, Saurabh. >> Saurabh, yeah. >> Saurabh, thank you, sorry. He is the co-founder and CEO of Nexla, and welcome. >> Thank you. >> So what is Nexla, tell us about Nexla for those that aren't familiar with the company. Thank you so much. Yeah so Nexla is a data operations platform. And the way we look at data is that data is increasingly moving between companies and one of the things that is driving that is the growth in machine learning. So imagine you are an e-commerce company, or a healthcare provider. You need to get data from your different partners. You know, suppliers and point-of-sale systems, and brands and all that. And the companies, when they are getting this data, from all these different places, it's so hard to manage. So we think of, you know just like cloud computing, made it easy to manage thousands of servers, we think of data ops as something that makes it easy to manage those thousands of data sources coming from so many partners. So you've jumped straight past the it's a cool buzz term in way to think about things, into the actual platform. So how does that platform fit within the cloud, and on Prim, is it part of the infrastructure, sits next to the infrastructure, is it a conduit? How does that work? >> Yeah, we think of it as, if you think of maybe machine learning or advanced analytics as the application, then data operations is sort of an underlying infrastructure for it. It's not really the hardware, the storage, but it's a layer on top. The job of data operations is to get the data from where it is to where you need it to be, and in the right form and shape. So now you can act on it. >> And do you find yourself replacing legacy stuff, or is this a brand new demand because of all the variant and so many types of datasets that are coming in that people want to leverage. >> Yeah, I mean to be honest, some of this has always been there in the sense that the day you connected a database to a network data started to move around. But if you think of the scale that has happened in the last six or seven years, none of those existing systems were ever designed for that. So when we talk about data growing at at a Moore's Law rate, when we talk about everybody getting into machine learning, when we talk about thousands of data sets across so many different partners that you work with, and when we think that reports that you get from your partners is no more sufficient, you need that underlying data, you can not basically feed that report into an algo. So when you look at all of these things we feel like it is a new thing in some ways. >> Right. Well, I want to unpack that a little bit because you made an interesting comment, before you turned on the cameras you just repeated, that you can't run an algorithm on a report. And in a world where we've got all the shared data sets, and it's funny too right, because you used to run a sample, now you want, you said, the raw. Not only all, but the raw data, so that you can do with it what you wish. Very different paradigm. >> Yeah. >> It sounds like there's a lot more, and you're not just parsing what's in the report, but you have to give it structure that can be combined with other data sources. And that sounds like a rather challenging task. Because the structure, all the metadata, the context that gives the data meaning that is relevant to other data sets, where does that come from? >> Yeah, so what happens, and this has been how technology companies have started to evolve. You want to focus on your core business. And therefore you will use a provider that processes your payments, you will use a provider that gives you search. You will use a provider that provides you the data for example for your e-commerce system. So there are different types of vendors you're working with. Which means that there's different types of data being involved. So when I look at for example a brand today, you could be say, a Nike, and your products are being sold on so many websites. If you want to really analyze your business well, you want data from every single one of those places, where your data team can now access it. So yes, it is that raw data, it is that metadata, and it is the data coming from all the systems that you can look at together and say when I ran this ad this is how people reacted to it, this was the marketing lift from that, this is the purchase that happened across these different channels, this is how my top line or bottom line was affected. And to analyze everything together you need all the data in a place. >> I'm curious on what do you find on the change in the business relationship. Because I'm sure there were agreements structured in another time which weren't quite as detailed, where the expectations in terms of what was exchanged wasn't quite this deep. Are you seeing people have to change their relationships to get this data? Is it out there that they're getting it, or is this really changing the way that people partner in data exchange, on like the example that you just used between say Nike and Foot Locker, to pick a name. >> Yeah, so I think companies that have worked together have always had reports come in, so you would get a daily report of how much you sold. Now just a high-level report of how much you sold is not sufficient anymore. You want to understand where was it bought, in which city, under what weather conditions, by what kind of user and all that stuff. So I think what companies are looking at, again, they have built their data systems, they have the data teams, unless they give the data their teams cannot be effective and you cannot really take a daily sales report and feed that into your algorithm, right? So you need very fine-grained data for that. So I think companies are doing this where, hey you were giving me a report before, I also need some underlying data. Report is for a business executive to look at and see how business is doing, and the underlying data is really for that algorithm to understand and maybe identify things that a report might not. >> Wouldn't there have been already, at least in the example of sell-through, structured data that's been exchanged between partners already like vendor-managed inventory, or you know where like a downstream retailer might make their sell-through data accessible to suppliers who actually take ownership of the inventory and are responsible for stocking it at optimal levels. >> Yeah, I think Walmart was the innovator in that, with the POS link system, back in the day, for retail. But the point is that this need for data to go from one company to their partners and back and forth is across every sector. So you need that in e-commerce, you need that in fintech, we see companies who have to manage your portfolio needs to connect with different banks and brokerages you work with to get the data. We see that in healthcare across different providers and pharmaceutical companies, you need that. We see that in automotive. If every care generates data, an insurance company needs to be able to understand that and look at it. >> This, it's a huge problem you're addressing, because this is the friction between inter-company applications. And we went through this with the B2B marketplaces, 15 plus years ago. But the reason we did these marketplace hubs was so that we could standardize the information exchange. If it's just Walgreens talking to Pfizer, and then doing another one-off deal with, I don't know, Lily, I don't know if they both still exist, it won't work for connecting all of pharmacy with all of pharma. How do you ensure standards between downstream and upstream? >> Yeah. So you're right, this has happened. When we do a wire transfer from one person to another, some data goes from a bank to another bank, still takes hours to get that, it's very tiny amount of data. That has all exploded, we are talking about zetabytes of data now every year. So the challenge is significantly bigger. Now coming to standards, what we have found, that two companies sitting together and defining a standard almost never works. It never works because applications change, systems change, the change is the only constant. So the way we've approached it at our company is, we monitor the data, we sit on top of the data and just learn the structure as we observe data flowing through. So we have tons of data flowing through and we're constantly learning the structure, and are identifying how the structure will map to the destination. So again, applying machine learning to see how the structure is changing, how the data volume is changing. So you are getting data from somewhere say every hour, and then it doesn't show up for two hours. Traditionally systems will go down, you may not even find for five days that the data wasn't there for that. So we look at the data structure, the amount of data, the time when it comes, and everything to instantly learn and be able to inform the downstream systems of what they should be expecting, if there is a change that somebody needs to be alerted about. So a lot of innovation is going in to doing this at scale without necessarily having to predefine something in a tight box that cannot be changed. Because it's extremely hard to control. >> All right, Saket, that's a great explanation. We're going to have to leave it there, we're out of time. And thank you for taking a few minutes out of your day to stop by. >> Thank you. >> All right. Jeff Frick with George Gilbert, we are at Data Platforms 2017, Pheonix Arizona, thanks for watching. (electronic music)

>> Announcer: Live from Munich, Germany, it's theCUBE, covering DataWorks Summit Europe 2017. Brought to you by HORTONWORKS. (electronic music) (crowd) >> Okay, welcome back everyone, we are here live in Munich, Germany, for DataWorks 2017, formerly Hadoop Summit, the European version. Again, different kind of show than the main show in North America, in San Jose, but it's a great show, a lot of great topics. I'm John Furrier, my co-host, Dave Vellante. Our next guest is John Kreisa, Vice President of International Marketing. Great to see you emceeing the event. Great job, great event! >> John Kreisa: Great. >> Classic European event, its got the European vibe. >> Yep. >> Germany everything's tightly buttoned down, very professional. (laughing) But big IOT message-- >> Yes. >> Because in Germany a lot of industrial action-- >> That's right. >> And then Europe, in general, a lot of smart cities, a lot of mobility, and issues. >> Umm-hmm. >> So a lot of IOT, a lot of meat on the bone here. >> Yep. >> So congratulations! >> John Kreisa: Thank you. >> How's your thoughts? Are you happy with the event? Give us by the numbers, how many people, what's the focus? >> Sure, yeah, no, thanks, John, Dave. Long-time CUBE attendee, I'm really excited to be here. Always great to have you guys here-- >> Thanks. >> Thanks. >> And be participating. This is a great event this year. We did change the name as you mentioned from Hadoop Summit to DataWorks Summit. Perhaps, I'll just riff on that a little bit. I think that really was in response to the change in the community, the breadth of technologies. You mentioned IOT, machine learning, and AI, which we had some of in the keynotes. So just a real expansion of from data loading, data streaming, analytics, and machine learning and artificial intelligence, which all sit on top and use the core Hadoop platform. We felt like it was time to expand the conference itself. Open up the aperture to really bring in the other technologies that were involved, and really represent what was already starting to kind of feed into Hadoop Summit, so it's kind of a natural change, a natural evolution. >> And there's a 2-year visibility. We talk about this two years ago. >> John Kreisa: Yeah, yeah. >> That you are starting to see this aperture open up a little bit. >> Yeah. >> But it's interesting. I want to get your thoughts on this because Dave and I were talking yesterday. It's like we've been to every single Hadoop Summit. Even theCUBE's been following it all as you know. It's interesting the big data space was created by the Hadoop ecosystem. >> Umm-hmm. >> So, yeah, you rode in on the Hadoop horse. >> Yeah. >> I get that. A lot of people don't get them. They say, Oh, Hadoop's dead, but it's not. >> No. >> It's evolving to a much broader scope. >> That's right. >> And you guys saw that two years ago. Comment on your reaction to Hadoop is not dead. >> Yeah, wow (laughing). It's far from dead if you look at the momentum, largest conference ever here in Europe. I think strong interest from them. I think we had a very good customer panel, which talked about the usage, right. How they were really transforming. You had Walgreens Booth's talking about how they're redoing their shelf, shelving, and how they're redesigning their stores. Don Ske-bang talking about how they're analyzing, how they replenish their cash machines. Centrica talking about how they redo their... Or how they're driving down cost of energy by being smarter around energy consumption. So, these are real transformative use cases, and so, it's far from dead. Really what might be confusing people is probably the fact that there are so many other technologies and markets that are being enabled by this open source technologies and the breadth of the platform. And I think that's maybe people see it kind of move a little bit back as a platform play. And so, we talk more about streaming and analytics and machine learning, but all that's enabled by Hadoop. It's all riding on top of this platform. And I think people kind of just misconstrue that the fact that there's one enabling-- >> It's a fundamental element, obviously. >> John Kreisa: Yeah. >> But what's the new expansion? IOT, as I mentioned, is big here. >> Umm-hmm. >> But there's a lot more in connective tissue going on, as Shawn Connelly calls it. >> Yeah, yep. >> What are those other things? >> Yeah, so I think, as you said, smart cities, smart devices, the analytics, getting the value out of the technologies. The ability to load it and capture it in new ways with new open source technology, NyFy and some of those other things, Kafka we've heard of. And some of those technologies are enabling the broader use cases, so I don't think it's... I think it's that's really the fundamental change in shift that we see. It's why we renamed it to DataWorks Summit because it's all about the data, right. That's the thing-- >> But I think... Well, if you think about from a customer perspective, to me anyway, what's happened is we went through the adolescent phase of getting this stuff to work and-- >> Yeah. >> And figuring out, Okay, what's the relationship with my enterprise data warehouse, and then they realize, Wow, the enterprise data warehouse is critical to my big data platform. >> Umm-hmm. >> So what's customers have done as they've evolved, as Hadoop has evolved, their big data platforms internally-- >> Umm-hmm. And now they're turning to to their business saying, Okay, we have this platform. Let's now really start to go up the steep part of the S-curve and get more value out of it. >> John Kreisa: Umm-hmm. >> Do you agree with that scenario? >> I would definitely agree with that. I think that as companies have, and in particularly here in Europe, it's interesting because they kind of waited for the technology to mature and its reached that inflection point. To your point, Dave, such that they're really saying, Alright, let's really get this into production. Let's really drive value out of the data that they see and know they have. And there's sort of... We see a sense of urgency here in Europe, to get going and really start to get that value out. Yeah, and we call it a ratchet game. (laughing) The ratchet is, Okay, you get the technology to work. Okay, you still got to keep the lights on. Okay, and oh, by the way, we need some data governance. Let's ratchet it up that side. Oh, we need a CDO! >> Umm-hmm. >> And so, because if you just try to ratchet up one side of the house (laughing) (cross-talk)-- >> Well, Carlo from HPE said it great on our last segment. >> Yeah. >> And I thought this was fundamental. And this was kind of like you had a CUBE moment where it's like, Wow, that's a really amazing insight. And he said something profound, The data is now foundational to all conversations. >> Right. >> And that's from a business standpoint. It's never always been the case. Now, it's like, Okay, you can look at data as a fundamental foundation building block. >> Right. >> And then react from there. So if you get the data locked in, to Dave's point about compliance, you then can then do clever things. You can have a conversation about a dynamic edge or-- >> Right. >> Something else. So the foundational data is really now fundamental, and I think that is... Changes, it's not a database issue. It's just all data. >> Right, now all data-- >> All databases. >> You're right, it's all data. It's driving the business in all different functions. It's operational efficiency. It's new applications. It's customer intimacy. All of those different ways that all these companies are going, We've got this data. We now have the systems, and we can go ahead and move forward with it. And I think that's the momentum that we're seeing here in Europe, as evidence by the conference and those kinds of things, just I think really shows how maybe... We used to say... I'd say when I first moved over here, that Europe was maybe a year and a half behind the U.S., in terms of adoption. I'd say that's shrunk to where a lot of the conversations are the exact same conversations that we're having with big European companies, that we're having with U.S. companies. >> And, even in... >> Yeah. >> Like we were just talking to Carlo, He was like, Well, and Europe is ahead in things like certain IOT-- >> Yeah. >> And Industrial IOT. >> Yeah. >> Yeah. >> Even IOT analytics. Some of the... Tesla not withstanding some of the automated vehicles. >> John Kreisa: Correct. >> Autonomous vehicles activity that's going on. >> John Kreisa: That's right. >> Certainly with Daimler and others. So there's an advancement. It almost reminds me of the early days of mobile, so... (laughing) >> It's actually, it's a good point. If you look at... Squint through some of the perspectives, it depends on where you are in the room and what your view is. You could argue there are many things that Europe is advanced on and where we're behind. If you look at Amazon Web Services, for instance. >> Umm-hmm. >> They are clearly running as fast as they can to deploy regions. >> Umm-hmm. >> So the scoop's coming out now. I'm hearing buzz that there's another region coming out. >> Right. >> From Amazon soon (laughing). They can't go fast enough. Google is putting out regions again. >> Right. >> Data centers are now pushing global, yet, there's more industrial here than is there. So it's interesting perspective. It depends on how you look at it! >> Yeah, yeah, no, I think it's... And it's perfectly fair to say there are many places where it's more advanced. I think in this technology and open source technologies, in general, are helping drive some of those and enable some of those trends. >> Yeah. >> Because if you have the sensors, you need a place to store and analyze that data whether it's smart cars or smart cities, or energy, smart energy, all those different places. That's really where we are. >> What's different in the international theater that you're involved in because you've been on both sides. >> Yep. >> As you came from the U.S. then when we first met. What's different out here now? And I see the gaps closing? What other things that notable that you could share? >> Yeah, yeah, so I'd say, we still see customers in the U.S. that are still very much wanting to use the shiniest, new thing, like the very latest version of Spark or the very latest version of NyFy or some other technologies. They want to push and use that latest version. In Europe, now the conversations are slightly different, in terms of understanding the security and governance. I think there's a lot more consciousness, if you will, around data here. There's other rules and regulations that are coming into place. And I think they're a little bit more advanced in how they think of-- >> Yeah. >> Data, personal data, how to be treated, and so, consequently, those are where the conversations are about the platform. How do we secure it? How does it get governed? So that you need regulations-- >> John Furrier: It's not as fast, as loose as the U.S. >> Yeah, it's not as fast. And you look and see some of the regulations. (laughing) My wife asked me if we should set up a VPIN on our home WiFi because of this new rule about being able to sell the personal data. I've said, Well, we're not in the U.S., but perhaps, when we move to the U.S. >> In order to get the right to block chain (laughing). (cross-talk) >> Yeah, absolutely (cross-talk). >> John Furrier: Encrypt everything. >> (laughing) Yeah, exactly. >> Well, another topic is... Let's talk about the ecosystem a little bit. >> Umm-hmm. >> You've got now some additional public brethren, obviously Cloudera's, there's been a lot of talk here about-- >> Umm-hmm. Tow-len and Al-trex-is have gone public. >> Yeah. >> The ecosystem you've evolved that. IBM was up on stage with you guys. >> Yeah, yep. >> So that continues to be-- >> Gallium C. >> Can we talk about that a little bit? >> Gallium C >> Gallium C. >> We had a great... Partners are great. We've always been about the ecosystem. We were talking about before we came on-screen that for us it's not Marney Partnership. They're very much of substance, engineering to try to drive value for the customers. It's where we see that value in that joint value. So IBM is working with us across all of the DataWorks Summit, but, even in all of the engineering work that we're doing, participated in HDP 2.6 announcement that we just did. And I'm sure what you covered with Shawn and others, but those partnerships really help drive value for the customer. >> Umm-hmm. For us, it's all making sure the customer is successful. And to make a complete solution, it is a range of products, right. It is whether it's data warehousing, servers, networks, all of the different analytics, right. There's not one product that is the complete solution. It does take a stack, a multitude of technologies, to make somebody successful. >> Cloudera's S-1, was file, what's been part of the conversation, and we've been digging into, it's great to see the numbers. >> Umm-hmm. >> Anything surprise you in the S-1? And advice you'd give to open source companies looking to go public because, as Dave pointed out, there's a string now of comrades in arms, if you will, Mool-saw, that's doing very well. >> Yeah, yeah. >> And Al-trex-is just went public. >> Yeah. >> You guys have been public for a long time. You guys been operating the public open-- >> Yeah. >> Both open source, pure open source. But also on the public markets. You guys have experience. You got some scar tissue. >> John Kreisa: (laughing) Yeah, yeah. >> What's your advice to Cloudera or others that are... Because the risk certainly will be a rush for more public companies. >> Yeah. >> It's a fantastic trend. >> I think it is a fantastic trend. I completely agree. And I think that it shows the strength of the market. It shows both the big data market, in general, the analytics market, kind of all the different components that are represented in some of those IPOs or planned IPOs. I think that for us, we're always driving for success of the customer, and I think any of the open source companies, they have to look at their business plan and take it step-wise in approach, that keeps an eye on making the customer successful because that's ultimately what's going to drive the company success and drive revenue for it and continue to do it. But we welcome as many companies as possible to come into the public market because A: it just allows everybody to operate in an open and honest way, in terms of comparison and understanding how growth is. But B: it's shows that strength of how open source and related technologies can help-- >> Yeah. >> Drive things forward. >> And it's good for the customer, too, because now they can compare-- >> Yes! >> Apples to Apples-- >> Exactly. >> Visa V, Cloudera, and what's interesting is that they had such a head start on you guys, HORTONWORKS, but the numbers are almost identical. >> Umm-hmm, yeah. >> Really close. >> Yeah, I think it's indicative of the opportunity that they're now coming out and there's rumors of other companies coming out. And I think it's just gives that visibility. We welcome it, absolutely-- >> Yeah. >> To show because we're very proud of our performance and now are growth. And I think that's something that we stand behind and stand on top of. And we want to see others come out and show what they got. >> Let's talk about events, if we can? >> Yeah. >> We were there at the first Hadoop Summit in San Jose. Thrilled to be-- >> John Kreisa: In a few years. >> In Dublin last year. >> Yeah. >> So what's the event strategy? I love going into the local flavor. >> Umm-hmm. >> Last year we had the Irish singers. This year we had a great (laughing) locaL band. >> John Kreisa: (laughing) Yeah, yeah, yeah. >> So I don't know if you've announced where next year's going to be? Maybe you can share with us some of the roll-out strategies? >> Yeah, so first of all, DataWorks Summit is a great event as you guys know, And you guys are long participants, so it's a great partnership. We've moving them international, of course, we did a couple... We are already international, but moving a couple to Asia last year so-- >> Right. >> Those were a tremendous success, we actually exceeded our targets, in terms of how many people we thought would go. >> Dave: Where did you do those? >> We were in Melburn in Tokyo. >> Dave: That's right, yeah. >> Yeah, so in both places great community, kind of rushed to the event and kind of understanding, really showed that there is truly a global kind of data community around Hadoop and other related technologies. So from here as you guys know because you're going to be there, we're thinking about San Jose and really wanting to make sure that's a great event. It's already stacking up to be tremendous, call for papers is all done. And all that's announced so, even the sessions we're really starting build for that, We'll be later this year. We'll be in Sydney, so we're going to have to take DataWorks into Sydney, Australia, in September. So throughout the rest of this year, there's going to be continued building momentum and just really global participation in this community, which is great. >> Yeah. >> Yeah. >> Yeah, it's fantastic. >> Yeah, Sydney should be great. >> Yeah. >> Looking forward to it. We're going to expand theCUBE down under. Dave and I are are excited-- >> Dave: Yeah, let's talk about that. >> We got a lot of interest (laughing). >> Alright. >> John, great to have you-- >> Come on down. >> On theCUBE again. Great to see you. Congratulations, I'm going to see you up on stage. >> Thank you. >> Doing the emcee. Great show, a lot of great presenters and great customer testimonials. And as always the sessions are packed. And good learning, great community. >> Yeah. >> Congratulations on your ecosystem. This is theCUBE broadcasting live from Munich, Germany for DataWorks 2017, presented by HORTONWORKS and Yahoo. I'm John Furrier with Dave Vellante. Stay with us, great interviews on day two still up. 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