>> Announcer: From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello, and welcome to theCUBE studios, in Palo Alto, California for another CUBE conversation where we go in depth with though leaders driving innovation across tech industry. I'm your host, Peter Burris. The whole concept of self service analytics has been with us decades in the tech industry. Sometimes its been successful, most times it hasn't been. But we're making great progress and have over the last few years as the technologies matures, as the software becomes more potent, but very importantly as the users of analytics become that much more familiar with what's possible and that much more wanting of what they could be doing. But this notion of self service analytics requires some new invention, some new innovation. What are they? How's that going to play out? Well, we're going to have a great conversation today with Stephanie McReynolds, she's Senior Vice President of Marketing, at Alation. Stephanie, thanks again for being on theCUBE. >> Thanks for inviting me, it's great to be back. >> So, tell us a little, give us an update on Alation. >> So as you know, Alation was one of the first companies to bring a data catalog to the market. And that market category has now been cemented and defined depending on the industry analyst you talk to. There could be 40 or 50 vendors now who are providing data catalogs to the market. So this has become one of the hot technologies to include in a modern analytics stacks. Particularly, we're seeing a lot of demand as companies move from on premise deployments into the cloud. Not only are they thinking about how do we migrate our systems, our infrastructure into the cloud but with data cataloging more importantly, how do we migrate our users to the cloud? How do we get self-service users to understand where to go to find data, how to understand it, how to trust it, what re-use can we do of it's existing assets so we're not just exploding the amount of processing we're doing in the cloud. So that's been very exciting, it's helped us grow our business. We've now seen four straight years of triple digit revenue growth which is amazing for a high growth company like us. >> Sure. >> We also have over 150 different organizations in production with a data catalog as part of their modern analytics stack. And many of those organizations are moving into the thousands of users. So eBay was probably our first customer to move into the, you know, over a thousand weekly logins they're now up to about 4,000 weekly logins through Alation. But now we have customers like Boeing and General Electric and Pfizer and we just closed a deal with US Air Force. So we're starting to see all sorts of different industries and all sorts of different users from the analytics specialist in your organization, like a data scientist or a data engineer, all the way out to maybe a product manager or someone who doesn't really think of them as an analytics expert using Alation either directly or sometimes through one of our partnerships with folks like Tableau or Microstrategy or Power BI. >> So, if we think about this notion of self- service analytics, Stephanie, and again it's Alation has been a leader in defining this overall category, we think in terms of an individual who has some need for data but is, most importantly, has questions they think data can answer and now they're out looking for data. Take us through that process. They need to know where the data is, they need to know what it is, they need to know how to use it, and they need to know what to do if they make a mistake. How is that, how are the data catalogs, like Alation, serving that, and what's new? >> Yeah, so as consumers, this world of data cataloging is very similar if you go back to the introduction of the internet. >> Sure. >> How did you find a webpage in the 90's? Pretty difficult, you had to know the exact URL to go to in most cases, to find a webpage. And then a Yahoo was introduced, and Yahoo did a whole bunch of manual curation of those pages so that you could search for a page and find it. >> So Yahoo was like a big catalog. >> It was like a big catalog, an inventory of what was out there. So the original data catalogs, you could argue, were what we would call from an technical perspective, a metadata repository. No business user wants to use a metadata repository but it created an inventory of what are all the data assets that we have in the organizations and what's the description of those data assets. The meta- data. So metadata repositories were kind of the original catalogs. The big breakthrough for data catalogs was: How do we become the Google of finding data in the organization? So rather than manually curating everything that's out there and providing an in- user inferant with an answer, how could we use machine learning and AI to look at patterns of usage- what people are clicking on, in terms of data assets- surface those as data recommendations to any end user whether they're an analytics specialist or they're just a self- service analytics user. And so that has been the real break through of this new category called data cataloging. And so most folks are accessing a data catalog through a search interface or maybe they're writing a SQL query and there's SQL recommendations that are being provided by the catalog-- >> Or using a tool that utilizes SQL >> Or using a tool that utilizes SQL, and for most people in a- most employees in a large enterprise when you get those thousands of users, they're using some other tool like Tableau or Microstrategy or, you know, a variety of different data visualization providers or data science tools to actually access that data. So a big part of our strategy at Alation has been, how do we surface this data recommendation engine in those third party products. And then if you think about it, once you're surfacing that information and providing some value to those end users, the next thing you want to do is make sure that they're using that data accurately. And that's a non- trivial problem to solve, because analytics and data is complicated. >> Right >> And metadata is extremely complicated-- >> And metadata is-- because often it's written in a language that's arcane and done to be precise from a data standpoint, that's not easily consumable or easily accessible by your average human being. >> Right, so a label, for example, on a table in a data base might be cust_seg_257, what does that mean? >> It means we can process it really quickly in the system. >> Yeah, but as-- >> But it's useless to a human being-- >> As a marketing manager, right? I'm like, hey, I want to do some customer segmentation analysis and I want to find out if people who live in California might behave differently if I provide them an offer than people that live in Massachusetts, it's not intuitive to say, oh yeah, that's in customer_seg_ so what data catalogs are doing is they're thinking about that marketing manager, they're thinking about that peer business user and helping make that translation between business terminology, "Hey I want to run some customer segmentation analysis for the West" with the technical, physical model, that underlies the data in that data base which is customer_seg_257 is the table you need to access to get the answer to that question. So as organizations start to adapt more self- service analytics, it's important that we're managing not just the data itself and this translation from technical metadata to business metadata, but there's another layer that's becoming even more important as organizations embrace self- service analytics. And that's how is this data actually being processed? What is the logic that is being used to traverse different data sets that end users now have access to. So if I take gender information in one table and I have information on income on another table, and I have some private information that identifies those two customers as the same in those two tables, in some use tables I can join that data, if I'm doing marketing campaigns, I likely can join that data. >> Sure. >> If I'm running a loan approval process here in the United States, I cannot join that data. >> That's a legal limitation, that's not a technical issue-- >> That's a legal, federal, government issue. Right? And so here's where there's a discussion, in folks that are knowledgeable about data and data management, there's a discussion of how do we govern this data? But I think by saying how we govern this data, we're kind of covering up what's actually going on, because you don't have govern that data so much as you have to govern the analysis. How is this joined, how are we combining these two data sets? If I just govern the data for accuracy, I might not know the usage scenario which is someone wants to combine these two things which makes it's illegal. Separately, it's fine, combined, it's illegal. So now we need to think about, how do we govern the analytics themselves, the logic that is being used. And that gets kind of complicated, right? For a marketing manager to understand the difference between those things on the surface is doesn't really make sense. It only makes sense when the context of that government regulation is shared and explained and in the course of your workflow and dragging and dropping in a Tableau report, you might not remember that, right? >> That's right, and the derivative output that you create that other people might then be able to use because it's back in the data catalog, doesn't explicitly note, often, that this data was generated as a combination of a join that might not be in compliance with any number of different rules. >> Right, so about a year and a half ago, we introduced a new feature in our data catalog called Trust Check. >> Yeah, I really like this. This is a really interesting thing. >> And that was meant to be a way where we could alert end users to these issues- hey, you're trying to run the same analytic and that's not allowed. We're going to give you a warning, we're not going to let you run that query, we're going to stop you in your place. So that was a way in the workflow of someone while they're typing a SQL statement or while they're dragging and dropping in Tableau to surface that up. Now, some of the vendors we work with, like Tableau, have doubled down on this concept of how do they integrate with an enterprise data catalog to make this even easier. So at Tableau conference last week, they introduced a new metadata API, they introduced a Tableau catalog, and the opportunity for these type of alerts to be pushed into the Tableau catalog as well as directly into reports and worksheets and dashboards that end users are using. >> Let me make sure I got this. So it means that you can put a lot of the compliance rules inside Alation and have a metadata API so that Alation effectively is governing the utilization of data inside the Tableau catalog. >> That's right. So think about the integration with Tableau is this communication mechanism to surface up these policies that are stored centrally in your data catalog. And so this is important, this notion of a central place of reference. We used to talk about data catalogs just as a central place of reference for where all your data assets lie in the organizations, and we have some automated ways to crawl those sources and create a centralized inventory. What we've added in our new release, which is coming out here shortly, is the ability to centralize all your policies in that catalog as well as the pointers to your data in that catalog. So you have a single source of reference for how this data needs to be governed, as well as a single source of reference for how this data is used in the organization. >> So does that mean, ultimately, that someone could try to do something, trust check and say, no you can't, but this new capability will say, and here's why or here's what you do. >> Exactly. >> A descriptive step that says let me explain why you can't do it. >> That's right. Let me not just stop your query and tell you no, let me give you the details as to why this query isn't a good query and what you might be able to do to modify that query should you still want to run it. And so all of that context is available for any end user to be able to become more aware of what is the system doing, and why is recommending. And on the flip side, in the world before we had something like Trust Check, the only opportunity for an IT Team to stop those queries was just to stop them without explanation or to try to publish manuals and ask people to run tests, like the DMV, so that they memorized all those rules of governance. >> Yeah, self- service, but if there's a problem you have to call us. >> That's right. That's right. So what we're trying to do is trying to make the work of those governance teams, those IT Teams, much easier by scaling them. Because we all know the volume of data that's being created, the volume of analysis that's being created is far greater than any individual can come up with, so we're trying to scale those precious data expert resources-- >> Digitize them-- >> Yeah, exactly. >> It's a digital transformation of how we acquire data necessary-- >> And then-- >> for data transformation. >> make it super transparent for the end user as to why they're being told yes or no so that we remove this friction that's existed between business and IT when trying to perform analytics. >> But I want to build a little bit on one of the things I thought I heard you say, and that is that the idea that this new feature, this new capability will actually prescribe an alternative, logical way for you to get your information that might be in compliance. Have I got that right? >> Yeah, that's right. Because what we also have in the catalog is a workflow that allows individuals called Stewards, analytics Stewards to be able to make recommendations and certifications. So if there's a policy that says though shall not use the data in this way, the Stewards can then say, but here's an alternative mechanism, here's an alternative method, and by the way, not only are we making this as a recommendation but this is certified for success. We know that our best analysts have already tried this out, or we know that this complies with government regulation. And so this is a more active way, then, for the two parties to collaborate together in a distributed way, that's asynchronous, and so it's easy for everyone no matter what hour of the day they're working or where they're globally located. And it helps progress analytics throughout the organization. >> Oh and more importantly, it increases the likelihood that someone who is told you now have self- service capability doesn't find themselves abandoning it the first time that somebody says no, because we've seen that over and over with a lot of these query tools, right? That somebody says, oh wow, look at this new capability until the screen, you know, metaphorically, goes dark. >> Right, until it becomes too complicated-- >> That's right-- >> and then you're like, oh I guess I wasn't really trained on this. >> And then they walk away. And it doesn't get adopted. >> Right. >> And this is a way, it's very human centered way to bring that self- service analyst into the system and be a full participant in how you generate value out of it. >> And help them along. So you know, the ultimate goal that we have as an organization, is help organizations become our customers, become data literate populations. And you can only become data literate if you get comfortable working with the date and it's not a black box to you. So the more transparency that we can create through our policy center, through documenting the data for end users, and making it more easy for them to access, the better. And so, in the next version of the Alation product, not only have we implemented features for analytic Stewards to use, to certify these different assets, to log their policies, to ensure that they can document those policies fully with examples and use cases, but we're also bringing to market a professional services offering from our own team that says look, given that we've now worked with about 20% of our installed base, and observed how they roll out Stewardship initiatives and how they assign Stewards and how they manage this process, and how they manage incentives, we've done a lot of thinking about what are some of the best practices for having a strong analytics Stewardship practice if you're a self- service analytics oriented organization. And so our professional services team is now available to help organizations roll out this type of initiative, make it successful, and have that be supported with product. So the psychological incentives of how you get one of these programs really healthy is important. >> Look, you guys have always been very focused on ensuring that your customers were able to adopt valued proposition, not just buy the valued proposition. >> Right. >> Stephanie McReynolds, Senior Vice President of Marketing Relation, once again, thanks for being on theCUBE. >> Thanks for having me. >> And thank you for joining us for another CUBE conversation. I'm Peter Burris. See you next time.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody, You're watching the Cube, the leader in live tech coverage, and we're covering the M I t CDO conference M I t. CDO. My name is David Monty in here with my co host, Paul Galen. Mike Stone breakers here. The legend is founder CTO of Of Tamer, as well as many other companies. Inventor Michael. Thanks for coming back in the Cube. Good to see again. Nice to be here. So this is kind of ah, repeat pattern for all of us. We kind of gather here in August that the CDO conference You're always the highlight of the show. You gave a talk this week on the top 10. Big data mistakes. You and I are one of the few. You were the few people who still use the term big data. I happen to like it. Sad that it's out of vogue already, but people associated with the doo doop it's kind of waning, but regardless, so welcome. How'd the talk go? What were you talking about. >> So I talked to a lot of people who were doing analytics. We're doing operation Offer operational day of data at scale, and they always make most of them make a collection of bad mistakes. And so the talk waas a litany of the blunders that I've seen people make, and so the audience could relate to the blunders about most. Most of the enterprise is represented. Make a bunch of the blunders. So I think no. One blunder is not planning on moving most everything to the cloud. >> So that's interesting, because a lot of people would would would love to debate that, but and I would imagine you probably could have done this 10 years ago in a lot of the blunders would be the same, but that's one that wouldn't have been there. But so I tend to agree. I was one of the two hands that went up this morning, and vocalist talk when he asked, Is the cloud cheaper for us? It is anyway. But so what? Why should everybody move everything? The cloud aren't there laws of physics, laws of economics, laws of the land that suggest maybe you >> shouldn't? Well, I guess 22 things and then a comment. First thing is James Hamilton, who's no techies. Techie works for Amazon. We know James. So he claims that he could stand up a server for 25% of your cost. I have no reason to disbelieve him. That number has been pretty constant for a few years, so his cost is 1/4 of your cost. Sooner or later, prices are gonna reflect costs as there's a race to the bottom of cloud servers. So >> So can I just stop you there for a second? Because you're some other date on that. All you have to do is look at a W S is operating margin and you'll see how profitable they are. They have software like economics. Now we're deploying servers. So sorry to interrupt, but so carry. So >> anyway, sooner or later, they're gonna have their gonna be wildly cheaper than you are. The second, then yet is from Dave DeWitt, whose database wizard. And here's the current technology that that Microsoft Azure is using. As of 18 months ago, it's shipping containers and parking lots, chilled water in power in Internet, Ian otherwise sealed roof and walls optional. So if you're doing raised flooring in Cambridge versus I'm doing shipping containers in the Columbia River Valley, who's gonna be a lot cheaper? And so you know the economies of scale? I mean, that, uh, big, big cloud guys are building data centers as fast as they can, using the cheapest technology around. You put up the data center every 10 years on dhe. You do it on raised flooring in Cambridge. So sooner or later, the cloud guys are gonna be a lot cheaper. And the only thing that isn't gonna the only thing that will change that equation is For example, my lab is up the street with Frank Gehry building, and we have we have an I t i t department who runs servers in Cambridge. Uh, and they claim they're cheaper than the cloud. And they don't pay rent for square footage and they don't pay for electricity. So yeah, if if think externalities, If there are no externalities, the cloud is assuredly going to be cheaper. And then the other thing is that most everybody tonight that I talk thio including me, has very skewed resource demands. So in the cloud finding three servers, except for the last day of the month on the last day of the month. I need 20 servers. I just do it. If I'm doing on Prem, I've got a provision for peak load. And so again, I'm just way more expensive. So I think sooner or later these combinations of effects was going to send everybody to the cloud for most everything, >> and my point about the operating margins is difference in price and cost. I think James Hamilton's right on it. If he If you look at the actual cost of deploying, it's even lower than the price with the market allows them to their growing at 40 plus percent a year and a 35 $40,000,000,000 run rate company sooner, Sooner or >> later, it's gonna be a race to the lot of you >> and the only guys are gonna win. You have guys have the best cost structure. A >> couple other highlights from your talk. >> Sure, I think 2nd 2nd thing like Thio Thio, no stress is that machine learning is going to be a game is going to be a game changer for essentially everybody. And not only is it going to be autonomous vehicles. It's gonna be automatic. Check out. It's going to be drone delivery of most everything. Uh, and so you can, either. And it's gonna affect essentially everybody gonna concert of, say, categorically. Any job that is easy to understand is going to get automated. And I think that's it's gonna be majorly impactful to most everybody. So if you're in Enterprise, you have two choices. You can be a disrupt or or you could be a disruptive. And so you can either be a taxi company or you can be you over, and it's gonna be a I machine learning that's going going to be determined which side of that equation you're on. So I was a big blunder that I see people not taking ml incredibly seriously. >> Do you see that? In fact, everyone I talked who seems to be bought in that this is we've got to get on the bandwagon. Yeah, >> I'm just pointing out the obvious. Yeah, yeah, I think, But one that's not quite so obvious you're is a lot of a lot of people I talked to say, uh, I'm on top of data science. I've hired a group of of 10 data scientists, and they're doing great. And when I talked, one vignette that's kind of fun is I talked to a data scientist from iRobot, which is the guys that have the vacuum cleaner that runs around your living room. So, uh, she said, I spend 90% of my time locating the data. I want to analyze getting my hands on it and cleaning it, leaving the 10% to do data science job for which I was hired. Of the 10% I spend 90% fixing the data cleaning errors in my data so that my models work. So she spends 99% of her time on what you call data preparation 1% of her time doing the job for which he was hired. So data science is not about data science. It's about data integration, data cleaning, data, discovery. >> But your new latest venture, >> so tamer does that sort of stuff. And so that's But that's the rial data science problem. And a lot of people don't realize that yet, And, uh, you know they will. I >> want to ask you because you've been involved in this by my count and starting up at least a dozen companies. Um, 99 Okay, It's a lot. >> It's not overstated. You estimated high fall. How do you How >> do you >> decide what challenge to move on? Because they're really not. You're not solving the same problems. You're You're moving on to new problems. How do you decide? What's the next thing that interests you? Enough to actually start a company. Okay, >> that's really easy. You know, I'm on the faculty of M i t. My job is to think of news new ship and investigate it, and I come up. No, I'm paid to come up with new ideas, some of which have commercial value, some of which don't and the ones that have commercial value, like, commercialized on. So it's whatever I'm doing at the time on. And that's why all the things I've commercialized, you're different >> s so going back to tamer data integration platform is a lot of companies out there claim to do it day to get integration right now. What did you see? What? That was the deficit in the market that you could address. >> Okay, great question. So there's the traditional data. Integration is extract transforming load systems and so called Master Data management systems brought to you by IBM in from Attica. Talent that class of folks. So a dirty little secret is that that technology does not scale Okay, in the following sense that it's all well, e t l doesn't scale for a different reason with an m d l e t l doesn't scale because e t. L is based on the premise that somebody really smart comes up with a global data model For all the data sources you want put together. You then send a human out to interview each business unit to figure out exactly what data they've got and then how to transform it into the global data model. How to load it into your data warehouse. That's very human intensive. And it doesn't scale because it's so human intensive. So I've never talked to a data warehouse operator who who says I integrate the average I talk to says they they integrate less than 10 data sources. Some people 20. If you twist my arm hard, I'll give you 50. So a Here. Here's a real world problem, which is Toyota Motor Europe. I want you right now. They have a distributor in Spain, another distributor in France. They have a country by country distributor, sometimes canton by Canton. Distribute distribution. So if you buy a Toyota and Spain and move to France, Toyota develops amnesia. The French French guys know nothing about you. So they've got 250 separate customer databases with 40,000,000 total records in 50 languages. And they're in the process of integrating that. It was single customer database so that they can Duke custom. They could do the customer service we expect when you cross cross and you boundary. I've never seen an e t l system capable of dealing with that kind of scale. E t l dozen scale to this level of problem. >> So how do you solve that problem? >> I'll tell you that they're a tamer customer. I'll tell you all about it. Let me first tell you why MGM doesn't scare. >> Okay. Great. >> So e t l says I now have all your data in one place in the same format, but now you've got following problems. You've got a d duplicated because if if I if I bought it, I bought a Toyota in Spain, I bought another Toyota in France. I'm both databases. So if you want to avoid double counting customers, you got a dupe. Uh, you know, got Duke 30,000,000 records. And so MGM says Okay, you write some rules. It's a rule based technology. So you write a rule. That's so, for example, my favorite example of a rule. I don't know if you guys like to downhill downhill skiing, All right? I love downhill skiing. So ski areas, Aaron, all kinds of public databases assemble those all together. Now you gotta figure out which ones are the same the same ski area, and they're called different names in different addresses and so forth. However, a vertical drop from bottom to the top is the same. Chances are they're the same ski area. So that's a rule that says how to how to put how to put data together in clusters. And so I now have a cluster for mount sanity, and I have a problem which is, uh, one address says something rather another address as something else. Which one is right or both? Right, so now you want. Now you have a gold. Let's call the golden Record problem to basically decide which, which, which data elements among a variety that maybe all associated with the same entity are in fact correct. So again, MDM, that's a rule's a rule based system. So it's a rule based technology and rule systems don't scale the best example I can give you for why Rules systems don't scale. His tamer has another customer. General Electric probably heard of them, and G wanted to do spend analytics, and so they had 20,000,000 spend transactions. Frank the year before last and spend transaction is I paid $12 to take a cab from here here to the airport, and I charged it to cost center X Y Z 20,000,000 of those so G has a pre built classification system for spend, so they have parts and underneath parts or computers underneath computers and memory and so forth. So pre existing preexisting class classifications for spend they want to simply classified 20,000,000 spent transactions into this pre existing hierarchy. So the traditional technology is, well, let's write some rules. So G wrote 500 rules, which is about the most any single human I can get there, their arms around so that classified 2,000,000 of the 20,000,000 transactions. You've now got 18 to go and another 500 rules is not going to give you 2,000,000 more. It's gonna give you love diminishing returns, right? So you have to write a huge number of rules and no one can possibly understand. So the technology simply doesn't scale, right? So in the case of G, uh, they had tamer health. Um, solve this. Solved this classification problem. Tamer used their 2,000,000 rule based, uh, tag records as training data. They used an ML model, then work off the training data classifies remaining 18,000,000. So the answer is machine learning. If you don't use machine learning, you're absolutely toast. So the answer to MDM the answer to MGM doesn't scale. You've got to use them. L The answer to each yell doesn't scale. You gotta You're putting together disparate records can. The answer is ml So you've got to replace humans by machine learning. And so that's that seems, at least in this conference, that seems to be resonating, which is people are understanding that at scale tradition, traditional data integration, technology's just don't work >> well and you got you got a great shot out on yesterday from the former G S K Mark Grams, a leader Mark Ramsay. Exactly. Guys. And how they solve their problem. He basically laid it out. BTW didn't work and GM didn't work, All right. I mean, kick it, kick the can top down data modelling, didn't work, kicked the candid governance That's not going to solve the problem. And But Tamer did, along with some other tooling. Obviously, of course, >> the Well, the other thing is No. One technology. There's no silver bullet here. It's going to be a bunch of technologies working together, right? Mark Ramsay is a great example. He used his stream sets and a bunch of other a bunch of other startup technology operating together and that traditional guys >> Okay, we're good >> question. I want to show we have time. >> So with traditional vendors by and large or 10 years behind the times, And if you want cutting edge stuff, you've got to go to start ups. >> I want to jump. It's a different topic, but I know that you in the past were critic of know of the no sequel movement, and no sequel isn't going away. It seems to be a uh uh, it seems to be actually gaining steam right now. What what are the flaws in no sequel? It has your opinion changed >> all? No. So so no sequel originally meant no sequel. Don't use it then. Then the marketing message changed to not only sequel, So sequel is fine, but no sequel does others. >> Now it's all sequel, right? >> And my point of view is now. No sequel means not yet sequel because high level language, high level data languages, air good. Mongo is inventing one Cassandra's inventing one. Those unless you squint, look like sequel. And so I think the answer is no sequel. Guys are drifting towards sequel. Meanwhile, Jason is That's a great idea. If you've got your regular data sequel, guys were saying, Sure, let's have Jason is the data type, and I think the only place where this a fair amount of argument is schema later versus schema first, and I pretty much think schema later is a bad idea because schema later really means you're creating a data swamp exactly on. So if you >> have to fix it and then you get a feel of >> salary, so you're storing employees and salaries. So, Paul salaries recorded as dollars per month. Uh, Dave, salary is in euros per week with a lunch allowance minds. So if you if you don't, If you don't deal with irregularities up front on data that you care about, you're gonna create a mess. >> No scheme on right. Was convenient of larger store, a lot of data cheaply. But then what? Hard to get value out of it created. >> So So I think the I'm not opposed to scheme later. As long as you realize that you were kicking the can down the road and you're just you're just going to give your successor a big mess. >> Yeah, right. Michael, we gotta jump. But thank you so much. Sure appreciate it. All right. Keep it right there, everybody. We'll be back with our next guest right into the short break. You watching the cue from M i t cdo Ike, you right back

>> Live from Las Vegas. It's theCUBE covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back the theCUBE's live coverage of the AWS Executive Summit here at the Venetian in Las Vegas. I'm your host, Rebecca Knight. We are joined by Gene Reznik, the Chief Strategy Officer at Accenture. Thanks so much for coming on theCUBE, Gene. >> My pleasure, Rebecca. >> So, Accenture is calling this period of time that we are all living through a period of epic disruption. Define what that means for us. >> Sure, sure. So, well, I think we're living in a very disruptive age right now. But again, I think we believe over the next 10 years it's going to become even more epic. And I think what's driving that, some things are geopolitical in nature. Alright, uh. Sort of, everything between U.S. and China relations, what's happening in Europe, all of that. Of course, there's technological. Dynamics around artificial intelligence. Of course, there's data, there's privacy, there's security. And all that really compounding on each other. We believe it's creating an environment where it's just going to be very challenging for people, but also for companies to navigate. And I think leadership and big organizations and their teams have to be very thoughtful of how they navigate this time. And I think there's going to be some big winners and I think there's going to be some big losers. And I think we see companies today that have been around for hundreds of years challenged to really adapt, adjust, and transform to really be prepared for this next wave of change. >> So, as you said, it's a very restive time politically, technologically, business wise. How are companies approaching this? I mean, as you said, you have to be very thoughtful. You have to have a real strategy in terms of how you're going to approach this, an approach innovation. How would you say companies are doing? Give them a report card right now in terms of how industry is responding. >> Yeah, well I think the first thing we would sort of say, and we've done quite a bit of analysis, and study through Accenture research, and as you'd expect, different industries are under different amounts of pressure and disruption. Some, like music industry and book publishing and currently retail, are under tremendous pressure. And, many have not responded well. They were too slow. They saw the digital natives just really take away their businesses. Others are better protected. So we have really gone through and analyzed industry by industry how they are prepared for today and really what they need to do going forward. And I guess our assessment is it's very, very difficult, as you would expect, to take a big organization and transform it. And the issue is again, while a lot of it is technology, the people side, the culture side, the organizational side, the incumbency dimension, the shareholders, all those things that make change very difficult are at the core of the transformation agenda. >> And innovation is really sort of the answer to it all because once you're move innovative, then you are going to ride this wave of epic disruption. So, first of all, how would you, so many companies are saying we want to be more innovative. What's the answer to that? I mean, what does that mean to be more innovative? >> Yeah, it's a good point. So we have, Accenture has looked at this. We sort of codified something that we call the wise pivot. Which is how should an organization really pivot to transform their business. And it's got elements, we believe strongly that you have to transform the core. Innovation can't be on the edge. At some point, you have to transform the core which usually gets at cost reduction, using automation to transform the business, transform the core economics. Then you have to grow the core and that's really I think the hard part, which is gaining market share in the core business we believe, whether it's in automotive business, whether it's in healthcare, whether it's in even retail, you have to grow the core, cause ultimately that gives you the investment capacity to scale the new. So, how to orchestrate that journey in a methodical way, again, keeping in mind the organization and what it delivers today and not leaving different parts of the organization behind is what we work with our clients on. >> And, what separates the winners from the losers? So the companies that are doing this well, how are they focusing on their core? And the core competencies? >> Right. We believe investing is a very big thing. Right, so the hardest part of all of this, in terms of economically, I mean there's a lot of difficult dimensions, but economically, as the pressure mounts, the ability to invest diminishes for most companies. And they don't have the room to invest in the business that their future depends on. And really freeing up that room and making the difficult decisions, you may have seen there were some announcements of mass lay offs, even today right? It's some of the biggest companies in the world. They're trying to create the room to invest in their next generation business that will take them into the future. And I think that's really the hardest part. How do you ultimately create the capacity to invest? And how do you make those investments? Again, cause there's also a lot of other examples of companies that have invested in the wrong way or in the wrong thing, that ultimately didn't lead them to the future So those two elements, creating the room to invest, then investing it in the right things and the right ways is what we find is key. >> So you're talking of course about GM which announced today that it was laying off about 15 thousand white collar and blue collar jobs. And the reason they're doing this is because they're saying there's no longer any room for six passenger cars in this market. We want to focus on self-driving vehicles. Is that a good move? I mean, I know you're not a GM analyst here but at the same time, it sounds as though that is smart, as you said. It's making room for investing in the future. >> Yeah, and I think that GM is clearly seeing autonomous cars coming, sort of form factors, everything that they're doing. And again, I don't think particular it's that in GM's case but again you read it that way. You'll look at General Electric. They're restructuring their entire company to better compete in the new. You'll look at IBM. IBM is acquiring Red Hat to have the kind of assets to compete in the new. So I think the biggest companies in the world are really trying to sort of say what the next ten years, what is their business going to to be? And then how to they take what got them here which for many of them have been 50 to 100 year journeys. And really figure out how to restructure that, to give them the room to invest into building a new business. And really, that takes tremendous leadership by the entire, you know, by the CEO, the board, the entire executive team and the people. The people have to commit to go along for that ride, and endure some of the pain for the greater good. >> So it's really a change management issue here, but in terms of, you talked about leadership, it also takes the foresight to actually know what your business is in the future. So GM is saying autonomous vehicles, which an average layman can say, yeah, that looks as though that's where the car industry is going. But how does a company even begin to imagine it's future at this time where there are new technologies being invented everyday, which are disruptive as we started talking about in the earlier conversation. >> Yeah, I think that's a very good question. Cause also if you look at where's the money going. The money is going to the disrupters. Right, if you look at the top five, the Google, Amazon, Facebook, Apple, let's put Microsoft in there. Combined last year they invested over 70 billion dollars, and that's about 15 percent of all of the fortune of the global one thousand. So the capital, as measured by what companies are expending, what the start-up, the VCs are at an all time high. 155 billion dollars invested last year, double what it was in 2001. The IPO market is at an all time high. Right, then you have these things like division fund, which is a whole other investment vehicle to fuel technology. So the reality is, there's never been more money going in to create the next wave of disruption, which is why we believe many of the existing companies really need to create those partnerships where they benefit from that. They can't compete with it. They can't outguess it, right? They need to be making equal systems that ultimately enable them to leverage those investments, to really help power their next generation business. >> So as an ecosystem driven world, where is Accenture doing this kind of work? >> Yeah, so the good news for Accenture is we built our business, built services in an ecosystem kind of model. Initially with SAP, with Oracle, with Salesforce and now it's with companies like Amazon and the AWS. And I think our view, and what we try to work with our clients on, is really to create the construct. And by the way, a lot of these constructs are just now being formed. What does partnering with an AWS to create your next generation digital business, what does that look like? And there's some models emerging in terms of co-innovation. And I would tell you what Amazon has done, what Berkshire Hathaway and J.P. Morgan Chase is an example of partnering to transform healthcare. Interesting way to do that. You look at something, another Seattle company Starbucks partnering with Alibaba to basically power their entire business in China. So you're starting to see different constructs where big companies are really coming together in different ways. And then again, those partnership constructs, incentives, business models around that, I think that's really where the innovation is going to take place. How do you do that? How do you align your incentives? And how do you jointly benefit from that partnership? >> So you announced something today with your Applied Intelligence Center of Excellence in Seattle, Washington. Tell our viewers a bit little more about that. >> Well, first of all we look at AWS and we say, clearly this is a company that is really important. >> It's doing something right. >> It's doing a lot of things right, it's doing a lot of things right. And I think a lot of our clients are looking at them, are leveraging them. So, it's our responsibility then as a services organization to build up capabilities and skills, and make their, and enable our clients to really tap in to this tremendous innovation. So, yeah, we did announce at Applied Intelligence Center of Excellence in Seattle. It'll be one of many centers across the United States and globally with a simple premise of building skills, building proof of concepts, building use cases, building MVPs to really around different industries and different solutions sets so again, reimagine business processes, catalyze transformation, and really make it something that our clients can tap into. >> You are the Chief Strategy Officer, what is your piece of advice for companies out there, at AWS, at reInvent here, what's sort of your one piece of strategy advice in this period of epic disruption and this cloud world. >> Yeah, I would say that unburdening ourselves from the day to day, and really immersing ourselves in this amazing environment. Learning, really understanding what makes one of these, one of the greatest companies in the world tick. Understanding how they do things. Not only, and as you know, there's more to Amazon than just technology. Right? There's a very strong culture. There's a very strong customer centricity. And really sort of understanding that, and really trying to apply it to our respective businesses. And seeing how it could really be, make the pivot to the digital more effective. And that's what I would sort of say. Come with an open mind. Learn a lot, and take it forward. >> Great, well Gene Reznik thank you so much for coming on theCUBE. >> This is a lot of fun. >> My pleasure. Thank you, thank you. >> I'm Rebecca Knight. We will have more of theCUBE's live coverage of the AWS Executive Summit in just a little bit. (techno music)

>> Live from Bahrain, it's theCUBE. Covering AWS Summit Bahrain. Brought to you by Amazon Web Services. >> Hello everyone, welcome to theCUBE's exclusive coverage. We are here in Bahrain in the Middle East for exclusive coverage of AWS's new region in the area. I'm John Furrier, cohost of theCUBE. It's our first time in the Middle East, as we go out into the world and expand theCUBE's mission of bringing you the best content, extracting the signal from the noise, meeting new people, connecting with thought leaders, people creating innovation, creating a new cultural shift with cloud computing. It's a societal global phenomenon, it's a change that's going to impact society, culture, economics, and humans. And this is theCUBE coverage, we're going to continue with that we are excited to have Khalid Al Rumaihi who is the CEO of the Bahrain Economic Development Board. He's the man, and responsible with his team for all the success and vision of bringing an Amazon region into the area. Here in Bahrain, Amazon has announced a region that's going to come in. And we expect to see economic revitalization. We expect to see an amplification of culture. Welcome to theCUBE, thank you for joining me. >> Thanks for having me John. >> Thanks for inviting us, and thanks for having us here. Here in the middle of all the action. Teresa Carlson from Amazon had a vision and you aligned with that vision, you guys are like-minded individuals. You saw something special with digital. >> Right. >> And this is not new. It's not like you woke up one morning and said, hey, let's bring Amazon in. Take us through the history of how we got here with Amazon about to launch a region early 2019 in Bahrain. You guys have had a vision, take us through that. >> You know, I started in my position about three years ago. I remember March 2015, a little more than three years ago. And my first week on the job, was joining his highness the crown prince in a meeting with Teresa. And so, in that meeting, that's what kicked it really off. Teresa heard form his highness, who is the chairman of the Bahrain Economic Development board, the vision for the country. We deregulated our telecom sector about 13, 14 years ago. We were the first country to do that in the Middle East. Which meant that we introduced competition on broadband, on mobile. It dropped prices by about 50%. On connectivity in the country. That attracted Amazon. When they looked at the region, they said, here's a government that's allowing true competition and for a data center obviously broadband communication, and the competitiveness of that price is key. And she was also impressed with his royal highness's vision for the country going forward. We want to become a digital economy, we want to transform this economy from an oil-based economy, to one that is based on information. And so we had a common view. And we determined, at that point, that we were going to do everything in our power to translate the conversation we had there to a reality. And here we are, almost three years later, almost to have a region here. >> And you know, people know my rant and rave, I always talk about, data is the new oil, information is the new oil. In that data and information, digital assets are digital. It a life-blood now of society. Citizen are reacting. Everyone's now connected with mobile devices, you're starting to see autonomous vehicles, you're starting to see a cultural blending between the old world, and then digital. And citizens can get new services, there's more efficiencies but there's actually a better opportunity for the citizens. And also in general. How do you guys look at that when you guys have your meetings, and you're looking at the vision of the future, the citizen benefits. Whether it's an entrepreneur or someone who's just living life. >> Well you know, when we had this discussion with Amazon, we decided to do what we call a cloud first policy. And we decided that we were going to move the government work loads to the cloud. We were going to actually, challenge any government institution, why they're not using the cloud. And it's been phenomenal. Now, it's been phenomenal from a cost saving perspective, which we want to pass on to the citizens. So for the citizens, for be for them to be able to get government services on their mobile phone, to pay their electricity bill to do get their license. And the government, if it reduces its cost can pass that on to that citizen. But more importantly, it's going to allow innovation to take place in the government. We're going to be able to have our education data in the Ministry of Education, communicate with our labor data. We're going to be able to do education in a new way. So it is going to unleash innovation in the government and the way it offers its services. We think it's going to do the same for businesses and for startups. >> We didn't get a chance to film it yesterday, but we were part of with Teresa Carlson's team with you and your startup Bahrain. All the entrepreneurs from the community, very vibrant, talking General Keith Alexander was there, knows a thing or two about cyber and then we had an entrepreneur visionary in John Wood, who's been in the business, but he's also a visionary. He made a comment and you reacted to that around the impact of the AWS region coming here. He was almost like, there's a storm of innovation coming and you align with that. You said, you kind of reacted at dinner last night about it. What is your feeling of what this will bring to the region? 'Cause Amazon has proven that when they put a region out, there's unexpected consequences sometimes like things you might not see. What are you expecting for the impact. For AWS? >> I think it's a game changer. I mean, you said data is the new oil. If we think back to the 30s, this country was the first country to discover oil. When, at that time, Texaco and So Cal started a refinery and started extracting oil, all the industries that developed around it refineries, oilfield engineering, oilfield services. You know, I think we're seeing we're going to see that in the new digital economy with data. Amazon coming here is going to do several things. Number one, it's going to unleash this innovation, it's going to reduce latency for people who are storing data looking to retrieve that. It's going to create new jobs, data scientists. We estimate 10,000 jobs are going to come on the back of this, that is going to be for the entire region. And I get it, I emphasize this is going to be a game changer, not just for the kingdom of Bahrain, but for the entire Middle East. We're already seeing startups who are getting educated about what the cloud can do for them, and the scale, the scale that they can reach by going to the cloud early on, we've seen them in the United States. Why can't this region see a unicorn that is able to be a global leader, just by virtue of, going to the cloud and learning from Amazon. And Amazon, AWS shares our passion for the startup community and what this can do for that. >> I want to get to the what's going to attract business to come into Bahrain. But first about what startup impact Amazon has proven and I heard a comment from one of the startups, Amazon Web Services is for big companies. Whoa, whoa, yeah, big companies are using Amazon now, but they won, they were built on the backs of startups. When Amazon first started and startups still use Amazon. It is a dream for a startup, the cost to get a company up off the ground, the speed of innovation with Amazon has proven startups, this is a big opportunity. And so this is going to impact how you set policy and get out of the way entrepreneurs, do you help them? As you look at policy, is that almost a tough decision on your part? 'Cause you guys are used to helping entrepreneurs, very entrepreneur friendly, but almost do you get out of their way, do you help them? What's the strategy for the startups? How do you look at this, because if the acceleration comes in and the training kicks in, you're going to see a renaissance of entrepreneurs, >> Right? >> What do you do, get out of their way, help them out? What's that? >> You got to balance it. I think, you can't coddle them. You can't do everything for the entrepreneur, there's got to be that grit, the resilience, that hunger at the entrepreneur. I was an entrepreneur before I took this role, and I think you've really got to have that fire in your belly. So what we want to do is we want to create an ecosystem, but we don't want to spoon feed them. So what we've done is for instance, we launched a $100 million venture capital fund of funds. And we said, the government shouldn't invest in startups but let's create a fund of funds that will invite venture capitalists to base themselves here, but we're not going to tell these venture capitalist how to invest. So each startup has to pitch itself to these venture capitalists and make sure that there's justification for it. We're going to create, you know, training, we're going to create elements, the regulation. We introduced a bankruptcy law this year, that is going to allow people to fail and to restructure. So we're going to put the policy in place. We're going to allow capital to be there, we're going to look at our training and education. But again, it really is down to the entrepreneur, to, so you've got to mix you've got to balance it. You've got to say, the burden is also on you to think about what's the market opportunity. Here is what the country will do, but then the rest is up to you. And I think, we're going to see our young youth in the region. We're doing this because this region is transforming. This region needs to create jobs. There's about a 100 million jobs you need to create in the Middle East over the next couple years. You're not going to be able to create that in the normal way. So we want people to become employers become entrepreneurs, rather than just employees and looking for a nine to five job. So it's integral to the vision of the region. >> Entrepreneurship is the engine of innovation. All right, let's talk about the region. You know, we're first out here so I'm kind of new, fresh eyes and you see Dubai out there, you got Asia, China and all these in Hong Kong and Singapore. So you guys have a unique opportunity. Dubai is kind of like a New York, it's hustle bustle is built out. You guys have this feeling like a Silicon Valley vibe. >> Right? >> It feels very open, very friendly, so you don't have to compete with each other. And New York does things, Silicon Valley does things. So you have this entrepreneurial culture. The key is a global co-creation a connection. How are you going to attract businesses? Because there is demand in the US for domiciling in places outside the United States. There's been a lot of competition. >> Sure. >> So are you prepared for companies to come here work with you? I know you guys are doing a lot of work. What do you say to the folks out there saying, I need to have a presence. Can I domicile in Bahrain? What's it like? What's the opportunities for me to connect into a growing ecosystem around Bahrain? >> So I'd say first of all, on the region, I mean, just like in Asia, just like in the US, you can have multiple hubs. So you know Bahrain will be a hub alongside a Dubai or a Riyadh or a Kuwait and so forth or a Abu Dhabi. And our niche is, as a small country, we're going to be very agile. One of the reasons why Amazon chose Bahrain is because we have a team Bahrain approach. And I, you know, I came from the private sector, when you're talking to General Electric, you're not talking to one department in General Electric, especially if you're a large customer. The whole company's going to rally around you and bring a solution to you as a customer. We're going to do that as a country. So with Amazon we got all the various ministries and we took a team Bahrain approach and we said we're going to solve through the economic development board, we're going to solve for your problem. Mondelez, which chose to locate their $100 million facility in Bahrain, built a facility about 30 soccer pitches, and they did it within a year and a half. We reclaimed land and had the land ready for them. They called it 'cause they make Oreos, they call it turning ocean to Oreos. >> Yeah. >> And so it's that agility that is going to differentiate us. In terms of niche, we're very interested in FinTech. We think we're going to take a leadership position not only regionally, but globally in FinTech. We have exciting announcements that we're going to make in FinTech. It's a small country, we can be nimble, agile, startup friendly, and kind of innovate. And so we're determined to carve a niche in open banking, in crypto currency exchanges, interesting innovation areas that we think we can excel at. >> Cloud computing certainly is a driver, artificial intelligence, obviously clearly. The fodder for entrepreneurship because it allows you to do things with data at a scale with a cloud engine, talk about FinTech and banking you can't ignore blockchain and crypto currency, which is bubble-ish right now, and then was kind of cleaning itself out, sorting itself out, but when that starts to settle and it becomes legitimate in the sense of a global access to digital money, or software defined money. >> Right. >> And data, that could be an integral part. How do you guys look at that? I know that's something that everyone's talking about. People are looking to do token kind of business models and there's really hasn't been any leadership globally at all on. >> Right. >> This is a place people can domicile, here Malta, here, there and there. So how do you guys look at that market, are you thinking about it, are you kicking the tires, what's happening? >> We're looking at FinTech and saying, really, beyond all the logos and all that. We're looking to reduce the friction for a customer doing the simple things. Looking at aggregating your accounts, understanding how you're spending money, looking at how to transfer money, looking at how to raise capital. If we can look at reducing the friction for people around these challenges, these day to day challenges and use our country as a pilot for doing that. Then imagine the potential that once you illustrate the potential here, you could go replicate it elsewhere. So we're very interested in blockchain. So you talk about crypto currencies, I think the real interesting element is the blockchain opportunity in FinTech and beyond. How can you allow the distributed ledger to have multiple applications. We're going to introduce issuing car licensing by a blockchain. Land, real estate transactions via blockchain. In addition to that, we're looking at open banking and allowing open banking to be prevalent here and allowing entrepreneurs to plug in and get access to that data and innovate around that. So that's how we're thinking about innovation in FinTech. >> Really, thanks for coming on and spending the time. I know you're super busy, and thanks for hosting us with theCUBE as part of the Amazon contingent. I give you the final word for the folks watching out there. What should they know about Bahrain that they might not know about it? And how do they engage with you guys? What are you guys doing? How should someone contact you? How do we engage? And what's the secret sauce of the Bahrain plan? >> Well, first of all, I'm going to plug my institution. It's simple, look at bahrainedb.com. It's on the internet. It's going to give you everything you need about what Bahrain. And what I'd say is, this is a small, but you know in this, in today's world, a global world and interconnected world, small is beautiful. So we're a small, forward thinking country. We're in a region that is about $1.5 trillion in terms of just the Gulf Cooperation Council. And here is a great gateway for tapping into that opportunity. We're about 30 minutes from the kingdom of Saudi Arabia which is doing wonderful things with Vision 2030, and you can be in Bahrain accessing that opportunity. And so I'd invite you to come, look at our website and the Bahrainedb will help you translate that kind of opportunity to a reality. >> Khalid, Chief Executive of Economic Development Board in Bahrain. Bold move congratulations. Bold moves have bold payoffs. Big bet with Amazon. >> Thanks, for having me John. >> Thanks for coming on. It's theCUBE here, we're live in Bahrain here at the Ritz Carlton for AWS summit 2018 here in the Middle East. I'm John Furrier. We'll be back with more coverage after this short break. (upbeat music)

>> Announcer: Live from Las Vegas, it's the Cube. Covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome to day three of Knowledge18. You're watching the Cube, the leader in live tech coverage. Day three is when ServiceNow brings together its audience and talks about its platform, the creators, the developers, the doers get together in the room. Jeff Frick and I, my co-host, we've seen this show now, Jeff, for many, many years. I joked on Twitter today, it's not often you see a full room and this room was packed on day three. Unless Larry Ellison is speaking. Well, Larry Ellison is not here, but Pat Casey is. He's the Senior Vice President of DevOps at ServiceNow and a Cube alum, Pat, great to see you again. >> Absolutely, just glad to be back. >> So, my head is exploding. With all the innovation that's comin' out. I feel like I'm at a AWS re:Invent with Andy Jassy up on stage with all these features that are coming out. But wow, you guys are on it. And part of that is because of the platform. You're able to put out new features, but how's the week going? >> So far it's been great. But you're sort of right, we are super proud of this year. I think there's more new stuff that's valuable for our customers coming out this year than probably the three years prior to this. I mean you got the chat bot designer, and you got some great application innovation, you got Flow Designer, you've got the entire integration suite coming online, and then in addition to that you've got a whole new mobile experience coming out. Just all stuff that our customers can touch. You can go downstairs and see all that and they can get their hands on it. Super exciting. >> So consistent too with the messaging. We've been coming here, I this is our sixth year, with kind of the low-code and no-code vision that Fred had way at the beginning. To let lots of people build great workflows and then to start taking some of these crazy new applications like chat bots and integration platform, pretty innovative. >> Yeah, I think it's a mindset when you get down to it. I mean we, the weird failure mode of technology is technology tends to get built by by technologists. And I do this for a living. There's a failure mode where you design the tool you want to use. And those tend to be programmer tools 'cause they tend to get designed by programmers. It does take an extra mental shift to say no, my user is not me. My user is a different person. I want to build the tool that they want to use. And that sort of user empathy, you know Fred had that in spades. That was his huge, huge, huge strength. Among other things. One of his huge strengths. It's something that we're really trying to keep foreground in the company. And you see that in some of the new products we released as well. It's really aimed at our customers not at our developers. >> The other thing I think that's been consistent in all the interviews we've done, and John talked on the day one keynote one of his kind of three keys to success was try to stay with out of the box as much as you can as a rule, and we've had all the GMs of the various application stacks that you guys have, they've all talked consistently we really try to drive, even as a group our specific requests back into development on the platform level so we can all leverage it. So even though then the vertical applications you guys are building, it's still this drive towards leverage the common platform. >> Yeah, absolutely. And there is, what's the word I'm looking for? There's a lot of value in using the product the way it was shipped. For easiest thing is when it advances or when we ship you new features you can just turn 'em on, and it doesn't conflict with anything else you got going in there. There's always an element of, you know, this is enterprise software. Every customer's a little bit different. GE does not work the same way as Bank of America. So you probably never get away entirely from configuring, but doing the minimum that you can get away with, the minimum that'll let you put your business-specific needs in there, and being really sure of it, you need to do it, it's the right approach to take. The failure mode of technologists, the other one, is we like writing technology. So give me a platform and I'm going to just write stuff. Applying that only when it makes sense to the business is where you really need to be. Especially in this day and age. >> Well I wanted to ask you about that 'cause you guys talk about many applications one platform. But you used to be one platform one app. >> Pat: Yep. >> So as you have more, and more, and more apps, how are you finding it regarding prioritization of features, and capabilities? I imagine the GMs like any company are saying, hey, this is a priority. >> Sure. >> And because you have a platform there's I'm sure a lot more overlap than if you're a stovepipe development organization. But nonetheless you still got to prioritize. Maybe talk about that a little bit. >> Sure, you end up with two different levels of it though. At one level, you tend to want to pick businesses to go into, which you're aligned with the technology stack you have. I don't think we're going to go into video streaming business. It's a good business, but it's not our business. >> Too bad, we could use some of that actually. >> Well, maybe next year. (laughs) But when you get down to it we mostly write enterprise business apps. So HR is an enterprise business app, CSM, SecOps, ITSM, they're all kind of the same general application area. So we don't tend to have something which is totally out to lunch. But you're right in the sense that A, what's important to CSM might be less important to ITSM. And so we do prioritize. And we prioritize partly based on what the perceived benefit across the product line is. If something that a particular BU wants that five other BUs are going to benefit from that's pretty valuable. If only them, not so much. And part of it too is based on how big the BUs are. You know if you're an emerging product line you probably get few less features than like Feryl Huff. Like she has a very big product line. Or Pabla, he has a very big product line. But there's also an over-investment in the emerging stuff. Because you have to invest to build the product lines out. >> The other thing I think is you guys have been such a great opportunity is I just go back to those early Fred interviews with the copy room and the color paper 'cause nobody knows what that is anymore. >> Pat: Yep. >> But workflow just by its very nature lends itself so much to leveraging, AI, and ML, so you've already kind of approached it while trying to make work easier with these great workflow tools, but what an opportunity now to apply AI and machine learning to those things over time. So I don't even have to write the rules and even a big chunk of that workflow that I built will eventually go away for me actually having to interact with it. >> Yeah, there's a second layer to it too, which I'll call out. The workflows between businesses are different. But we have the advantage that we have the data for each of the businesses. So we can train AI on this is the way this particular workflow works at General Electric and use that bot at GE and train a different bot at maybe at Siemens. You know it's still a big industrial firm. It's a different way of doing it. That gives us a really big advantage over people who commingle the data together. Because of our architecture, we can treat every customer uniquely and we can train the automation for the unique workflows for that particular customer. It gives a much more accurate result. >> So thinking about, staying on the theme of machine intelligence for a moment, you're not a household name in the world of AI, so you've done some acquisitions and-- >> Pat: Yep. >> But it's really becoming a fundamental part of your next wave of innovation. As a technologist, and you look out at the landscape, you obviously you see Google, Apple, Facebook, IBM, with Watson, et cetera, et cetera, as sort of the perceived leaders, do you guys aspire to be at that level? Do you need to be? What's the philosophy and strategy with regard to implementing AI in the road map? >> Well if you cast your eyes forward to where we think the future's going to be, I do think there are going to be certain core AI services that they're going to call their volume plays. You need a lot of engineers, a lot of resources, a lot of time to execute them. Really good voice-to-text is an example. And that's getting pretty good. It's almost solved at this point. A general case conversational agent, not solved yet. Even the stuff you see at Google I/O, it's very specialized. It does one thing really well and it's a great demo, but ask it about Russian history, no idea what to talk about. Whereas, maybe you don't know a lot about Russian history, you as a human would at least have something interesting to say. We expect that we will be leveraging other people's core AI services for a lot of stuff out there. Voice-to-text is a good example. There may well be some language parsing that we can do out there. There may be other things we never even thought of. Maybe stuff that'll read text for you and give you back summaries. Those are the kinds of things that we probably won't implement internally. Where you never know, but that's my guess, where you look at where we think we need to write our own code or own our own IP, it's where the domain is specific to our customers. So when I talked about General Electric having a specific workflow, I need to be able to train something specific for that. And if you look at some other things like language processing, there's a grammar problem. Which is a fancy way of saying that the words that you use describing a Cube show are different than the words that I would use describing a trade show. So if I teach a bot to talk about the Cube, it can't talk about trade shows. If you're Amazon, you train your bot to talk in generic language. When you want to actually speak in domain-specific language, it gets a lot harder. It's not good at talking about your show. We think we're going to have value to provide domain-specific language for our customers' individualized domains. I think that's a big investment. >> But you don't have to do it all as well. We saw two actually interesting use cases talking to some of your customers this week. One was the hospital in Australia, I don't know if you're familiar with this, where they're using Alexa as the interface, and everything goes into the ServiceNow platform for the nurses. >> Yep. >> And so that's not really your AI, it's kind of Amazon's AI, that's fine. And the other was Siemens taking some of your data and then doing some stuff in Azure and Watson, although the Watson piece was, my take away was it was kind of a fail, so there's some work to be done there, but customers are going to use different technologies. >> Pat: Oh, they will. >> You have to pick your spots. >> You know we're, as a vendor, we're pretty customer-centric. We love it when you use our technology and we think it's awesome, otherwise we wouldn't sell it. But fundamentally we don't expect to be the only person in the universe. And we're also not, like you've seen us with our chat bot, our chat bot, you can use somebody else's chat client. You can use Slack, you can use Teams, you can use our client, we can use Jabber. It's great. If you were a customer and want to use it, use it. Same thing on the AI front. Even if you look at our chat bot right now, there's the ability to plug in third-party AIs for certain things even today. You can plug it in for language processing. I think out of box is configured for Google, but you can use Amazon, you can use Microsoft if you want to. And it'll parse your language for you at certain steps in there. We're pretty open to partnering on that stuff. >> But you're also adding value on top of those platforms, and that's the key point, right? >> The operating model we have is we want it to be transparent to our customers as to what's going on in the back end. We will make their life easy. And if we're going to make their life easy by behind the scenes, integrating somebody else's technology in there, that's what we're going to do. And for things like language processing, our customers never need to know about that. We know. And the customers might care if they asked because we're not hiding it. But we're not going to make them do that integration. We're going to do it for them, and just they click to turn it on. >> Pat, I want to shift gears a little bit in terms of the human factors point of all this. I laugh, I have an Alexa at home, I have a Google at home, and they send me emails suggesting ways that I should interact with these things that I've never thought of. So as you see kind of an increase in chat bots and you see it increase in things like voice-to-text and these kind of automated systems in the background, how are you finding people's adoption of it? Do they get it? Do the younger folks just get it automatically? Are you able to bury it such where it's just served up without much thought in their proc, 'cause it's really the behavior thing I think's probably a bigger challenge than the technology. >> It is and frankly it's varied by domain. If you look at something like Voice that's getting pretty ubiquitous in the home, it's not that common in a business world. And partly there frankly is just you've got a background noise problem. Engineering-wise, crowded office, someone's going to say Alexa and like nobody even knows what they're talking about. >> Jeff: And then 50 of 'em all-- >> Exactly. There's ways to solve that, but this is actual challenge. >> Right. >> If you look at how people like to interact with technologies, I would argue we've already gone through a paradigm shift that's generational. My generation by default is I get out a laptop. If you're a millennial your default is you get out your phone. You will go to a laptop and the same says I will go to a phone, but that's your default. You see the same thing with how you want to interact. Chat is a very natural thing on the phone. It's something you might do on a full screen, but it's a less common. So you're definitely seeing people shifting over to chat as their preferred interaction paradigm especially as they move onto the phones. Nobody wants to fill out a form on a phone. It's miserable. >> Jeff: Right. >> I wonder if we could, so when Jeff and I have Fred on, we always ask him to break out his telescope. So as the resident technologist, we're going to ask you. And I'm going to ask a bunch of open-ended questions and you can pick whatever ones you want to answer, so the questions are, how far can we take machine intelligence and how far should we take machine intelligence? What are the things that machines can do that humans really can't and vice versa? How will humans and machines come together in the future? >> That's a broad question. I'll say right now that AI is probably a little over-marketed. In that you can build really awesome demos that make it seem like it's thinking. But we're a lot further away from an actual thinking machine, which is aware of itself than I think it would seem from the demos. My kids think Alexa's alive, but my son's nine, right? There's no actual Alexa at the end of it. I doubt that one's going to get solved in my lifetime. I think what we're going to get is a lot better at faking it. So there's the classical the Turing test. The Turing test doesn't require that you be self-aware. The Turing test says that my AI passes the Turing test if you can't tell the difference. And you can do that by faking it really well. So I do think there's going to be a big push there. First level you're seeing it is really in the voice-to-text and the voice assistance. And you're seeing it move from the Alexas into the call centers into the customer service into a lot of those rote interactions. When it's positive it's usually replacing one of those horrible telephone mazes that everybody hates. It gets replaced by a voice assist, and as a customer you're like that is better. My life is better. When it's negative, it might replace a human with a not-so-good chat. The good news on that front is our society seems to have a pretty good immune system on that. When companies have tried to roll out less good experiences that are based on less good AI, we tend to rebel, and go no, no, we don't want that. And so I haven't seen that been all that successful. You could imagine a model where people were like, I'm going to roll out something that's worse but cheaper. And I haven't seen that happening. Usually when the AI rolls out it's doing it to be better at something for the consumer perspective. >> That's great. I mean we were talking earlier, it's very hard to predict. >> Pat: Of course. >> I mean who would have predicted that Alexa would have emerged as a leader in NLP or that, and we said this yesterday, that the images of cats on the internet would lead to facial recognition. >> I think Alexa is one example though. The thing I think's even more amazing is the Comcast Voice Remote. Because I used to be in that business. I'm like, how could you ever have a voice remote while you're watching a TV and watching a movie with the sound interaction? And the fact that now they've got the integration as a real nice consumer experience with YouTube and Netflix, if I want to watch a show, and I don't know where it is, HBO, Netflix, Comcast, YouTube, I just tell that Comcast remote find me Chris Rock the Tamborine man was his latest one, and boom there it comes. >> There's a school of thought out there, which is actually pretty widespread that feels like the voice technologies have actually been a bit of a fail from a pure technologies standpoint. In that for all the energy that we've spent on them, they're sort of stuck as a niche application. There's like Alexa, my kids talk to Alexa at home, you can talk to Siri, but when these technologies were coming online, I think we thought that they would replace hard keyboard interactions to a greater degree than they have. I think there's actually a bit of a learning in there that people are not as, we don't mandatorily, I'm not sure if that's a real word, but we don't need to go oral. There's actually a need for non-oral interfaces. And I do think that's a big learning for a lot of the technology is that there's a variety of interface paradigms that actual humans want to use, and forcing people into any one of them is just not the right approach. You have to, right now I want to talk, tomorrow I want to text, I might want to make hand gestures another time. You're mostly a visual media, obviously there's talking too, but it's not radio, right? >> You're absolutely right. That's a great point because when you're on a plane, you don't want to be interacting in a voice. And other times that there's background noise that will screw up the voice reactions, but clearly there's been a lot of work in Silicon Valley and other places on a different interface and it needs to be there. I don't know if neural will happen in our lifetime. I wanted to give you some props on the DevOps announcement that you sort of pre-announced. >> We did. >> It's, you know CJ looked like he was a little upset there. Was that supposed to be his announcement? >> In my version of the script, I announced it and he commented on my announcement. >> It's your baby, come on. So I love the way you kind of laid out the DevOps and kind of DevOps 101 for the audience. Bringing together the plan, dev, test, deploy, and operate. And explaining the DevOps problem. You really didn't go into the dev versus the ops, throwing it over the wall, but people I think generally understand that. But you announced solving a different problem. 500 DevOps tools out there and it gets confusing. We've talked to a bunch of customers about that. They're super excited to get that capability. >> Well, we're super, it's one of those cases where you have an epiphany, 'cause we solved it internally. >> Dave: Right. >> And we just ran it for like three years, and we kept hearing customers say, hey, what are you guys going to do about DevOps? And we're never like quite sure what they mean, 'cause you're like, well what do you mean? Do you want like a planning tool? And then probably about a year ago we sort of had this epiphany of, oh, our customers have exactly the same problem we do. Duh. And so from that it kind of led us to go down the product road of how can we build this kind of management layer? But if you look across our customer base and the industry, DevOps is almost a rebellion. It's a rebellion against the waterfall development model which has dominated things. It's a rebellion against that centralized control. And in a sense it's good because there's a lot of silliness that comes out of those formal development methodologies. Slow everybody down, stupid bureaucracy in there. But when you apply it in an enterprise, okay some of the stuff in there, you actually did need that. And you kind of throw the baby out with the bathwater. So adding that kind of enterprise DevOps layer back in, you still do get that speed. Your developers get to iterate, you get the automated tests, you get the operating model, but you still don't lose those kind of key things you need at the top enterprise levels. >> And most of the customers we've talked to this week have straight up said, look, we do waterfall for certain things, and we're not going to stop doing waterfall, but some of the new cool stuff, you know. (laughs) >> Well if you look at us, it's at the, if you take the microscope far enough away from ServiceNow, we're waterfall in that every six months we release. >> Dave: Yeah, right. >> But if you're an engineer, we're iterating in 24-hour cycles for you. 24-hour cycles, two-week sprints. It's a very different model when you're in the trenches than from the customer perspective. >> And then I think that's the more important part of the DevOps story. Again, there's the technology and the execution detail which you outlined, but it's really more the attitudinal way that you approach problems. We don't try to solve the big problems. We try to keep moving down the road, moving down the road. We have a vision of where we want to get, but let's just keep moving down the road, moving down the road. So it's a very, like you said, cumbersome MRD and PRD and all those kind of classic things that were just too slow for 2018. >> Nobody goes into technology to do paperwork. You go into technology to build things to create, it's a creative outlet. So the more time you can spend doing that, and the less time you're spending on overhead, the happier you're going to be. And if you fundamentally like doing administration, you should move into management. That's great. That's the right job for you. But if you're a hands on the keyboard engineer, you probably want to have your hands on the keyboard, engineering. That's what you do. >> Let's leave on a last thought around the platform. I mentioned Andy Jassy before and AWS. He talks about the flywheel effect. Clearly we're seeing the power of the platform and it feels like there's the developer analog to operating leverage. And that flywheel effect going from your perspective. What can we expect going forward? >> Well, I mean for us there's two parallel big investment vectors. One is clearly we want to make the platform better for our apps. And you asked earlier about how do we prioritize from our various BUs, and that is driving platform enhancements. But the second layer is, this is the platform our customers are using to automate their entire workflow across their whole organization. So there's a series of stuff we're doing there to make that easier for them. In a lot of cases, less about new capabilities. You look at a lot of our investments, it's more about taking something that previously was hard, but possible, and making it easier and still possible. And in doing that, that's been my experience, is Fred Luddy's experience, the easier you can make something, the more successful people will be with it. And Fred had an insight that you could almost over-simplify it sometimes. You could take something which had 10 features and was hard to use, and replace with something that had seven features and was easy to use, everyone would be super happy. At some level, that's the iPhone story, right? I could do more on my Blackberry, it just took me an hour of reading the documentation to figure out how. >> Both: Right, right. >> But I still miss the little side wheel. (laughs) >> Love that side wheel. All right, Pat, listen thanks very much for coming. We are humbled by your humility. You are like a rock star in this community, and congratulations on all this success and really thanks for coming back on the Cube. >> Thank you very much. It's been a pleasure meeting you guys again. >> All right, great. Okay, keep it right there, everybody. We'll be back with our next guest. You're watching the Cube live from ServiceNow Knowledge K18, #know18. We'll be right back. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering IBM Think 2018. Brought to you by IBM. >> Welcome back to theCUBE. We are live at the inaugural IBM Think 2018 event. I'm Lisa Martin with Dave Vellante. And our first guest, on day one of our coverage, is Jerry Thompson, the Chief Revenue Officer of Identity Guard. Hey Jerry, welcome to theCUBE. >> Thank you, well, it's a pleasure to be here. >> So tell us about Identity Guard. What are you guys, what do you do and how are you working with IBM? >> Yeah, Identity Guard is a, is a subsidiary of Intersections. We are a publicly-traded company and we're only in the identity and privacy space. So we, today, protect about 1.4 million people's identities. They, it's a subscription-based service. And two and a half years ago, we made the decision to, to basically invent identity 2.0 and the only way to do that was to use artificial intelligence technology, so we went to Watson to do that. >> This is a giant leap that you mentioned. >> Huge. >> So let's kind of, maybe, break that down a little bit and really talk about what you're doing here that was really transformative. >> Yeah, so, identity protection companies today only look at structured data. And, basically, we look at structured data and we look at it in arrears, so we can't do anything proactive or preventive. We knew if we used Watson in an AI technology, we could monitor unstructured data, which is probably 90% of all the data out there about any of us. And in order, in doing so, we could do preventive and predictive analysis of your personal information, privacy and your identity. So there was a quantum leap to go from just reacting to actually proactively protecting people's identity and privacy. >> So could you take us through, sort of, the journey that you went on to go from, sort of, where you were to where you are now and where you're headed? >> Yeah so, I mean, it starts like every other company with Watson. We took the tour of the Watson building. Went upstairs to the glass conference rooms and in that conference room, waiting for us, was the CIO of Watson. >> Dave: When was this? >> Two and a half years ago. >> Okay. >> And we explained the problem we were trying to solve. And from that day forward, IBM has been an amazing partner for us, amazing partner. So we did all of the things. We went through a Scrum, we wrote some product code, we did, you know, proof of concept, and when we were convinced that we could actually reinvent this industry, we went all-in. >> Keep going. >> And that was two and a half years ago. >> So, so, so a lot of people would say "Okay, Watson's a heavy lift, "you got to have a lot of services." It sounds like you did but the outcome is really what you're driving toward. So what was the outcome you were looking for and what'd you have to do to get there? >> Yeah so, I mean, at the highest level, we wanted to protect not only your financial and credit data, but all of the data that's out there about you and your partner, spouse, wife and kids. And in order to do that you need a processing engine that actually is intelligent. So that was the journey in Watson. We have found it to be not a big, heavy lift. We had the right kind of data scientists and we knew the problems we were trying to solve. Not in the abstract, in the particular. We defined the stories and the categories that we wanted to play in. We defined the product as we wanted to launch it. We knew it was going to be a one to two year run because you have to invent it, create it, then you have to play with it, right? You have to run it through the machine, so, >> Iterate. >> Right, and iterate. So, in order to do that, we knew the timeframe so we were never frustrated. And, along that journey, we came up with other things that we thought would be amazing to include in the service so, like cyberbullying technology, geolocation technology. All kinds of other things where only Watson would help us do that. >> And, and the data scientists were on your team >> Our team, yeah. or IBM brought those to the table? Okay, so you >> Yeah, no, IBM always let us reference their, but we have a handful in Virginia and some more in California in our development center. >> So you're one of the lucky ones who had a team, a bench, of data scientists >> Yes. >> at your disposal to go, is that right? >> Yeah, I wouldn't say a deep bench, but we've added to it over time, as you, as you get into the way you want to solve this problem. >> And, and how, specifically, are you using Watson? Can you give us, add some color on the APIs that you're using >> Sure. >> and how you're applying them? >> So we use natural-language processing because we pour amazing amount of data through the Watson funnel. Social media data, geolocation, Alchemy News. And we need the natural-language to actually jump and, and search for key words and key intimates. We use emotion analysis API, sentiment analysis API for context. So we're reading social media posts, your kids' posts. Your kid might say "Boy, I killed it "on the soccer field today." That's not a threat, right, that's just a statement. You have to add context to the statement. In order to do that, we use emotion and sentiment APIs. We use visual image recognition for inappropriate things that might be coming through. We use Alchemy News, which I believe is Discovery today. We're in the process, with the help of IBM, to create a library, a language, around emojis. Some emojis can be very threatening in the way they're used and the context they're used. You have to be able to read it, intelligently read it, and then put it in context to the string of texts or Instagram posts or whatever, that are going back and forth. So we, we've really taken this holistic view of what Watson can do, help us do for unstructured data and, in that process, it made our ability to monitor structured data better. We learned a lot. So we actually got benefits on both sides of our business. >> So you talked about this quantum leap that, that you made to identity 2.0. Also, what you're doing, in your space is quite pioneering in that, you're >> Yes. >> the only, first and only company, in the space that's using AI. Cyberbullying is such a hot, very challenging topic and, and sadly one that's very much needed in terms of identity. >> Right. >> But why do you think it is that, that Identity Guard is, is so pioneering in this space? >> Yeah, you know, we've always been, we, first of all, Identity Guard invented the identity business 23 years ago. We're the first ones to ever do it, first ones to do credit scores, reports. So we've always innovated in this space. The, the challenge for us as a public company, our biggest competitor is the credit bureaus, right? And the credit bureaus are low-cost providers and, and, candidly, I think they stamp out innovation in our field because they just want it to be about credit data. They don't want it to be about other things. So it was time for somebody to take this leap to predictive and preventive technologies, not just reactive. The rear view mirror can tell you a lot but it can't help you protect today, and that's what we've been doing in our space. >> Well the dossier from a credit bureau is so limited. >> Right. >> It doesn't provide context. You know, your score goes up or down for weird reasons. 'Cause people are doing credit pulls or whatever it is. You don't really have a context of what's going on there. So, so my question to you, Jerry, is where do you see innovation going in this space? Obviously data is involved and the credit bureaus have data but where is innovation going to come from in the next five to 10 years? >> Yeah, you know, I think it's the, we're going to figure out how to harvest data that's out there and then score that data so that we can help you and your family stay safe. Nobody today wants to have no internet, right? The internet's opened up an amazing amount of capability for people. But, but you have to have a way to play in it without it being too dangerous. And I believe we can use Watson. That's our, it's been our theory from day one. We can use Watson to level the playing field, right? Not, not really get an advantage, but to level the playing field, especially for families where not everybody is aware of all of the malfeasance that's out there on the internet, right? >> Right. >> People are always looking to harvest our data and to use it in a malicious way. Especially kids and minors, right? They're at risk for cyber, you know, predation and stalking and cyberbullying and, and parents today know it's a big issue. >> Okay, go ahead please Lisa. >> I was just going to say, in terms of expectations, you're saying it's to level the playing field with the cyber criminals, the stalkers, in the next, you know, can we look at timeframe? Think that you'll get ahead of that to start actually preventing some of this cyberbullying going on? >> You know, I, that's a good question. I will tell you right now, our ambition is to level the playing field. It's tilted this way today. I think what will happen is technology's like geolocation. It seems, first of all geolocation is not really relevant without Watson Discovery, right? You need all of this massive data going on in the locations that you're relevant in to help us protect you. But I believe, based on the early science that we're doing with IBM, that we can actually help a kid, somebody's stalking them from, you know, four states away but it says it's the little boy across town, we can actually stop things like that happening using the processing and the algorithms that we're doing using Watson. So there are, there are relevant areas that I think we can have a massive impact on the privacy and the protection of people and their families. >> I want to come back to innovation, so data is clearly a key component of that. You're extending the data model into unstructured data. I'm hearing that, correct? >> Yes. >> Also, AI, machine intelligence is another part of that. What about scale? Scale and network effects >> Yeah. >> and that sort of component of innovation. >> That had to be >> Does that come from cloud, is that where it's coming from? >> That had to be part of this. So we, along with all of our competitors in the existing 1.0 business, we use a hard-coded platform. >> Right. >> Right, I mean, if you want to change something, you have to get out a sledgehammer and a chisel and it takes a year. We built Watson using AWS, so we've used all the best tools, the fastest tools. We've run scale testing, you know, and, and the beautiful thing about our business, we're a digital business, right, so our factory's open 24 hours a day, 365 days a year. Our shopping carts never close. You can always, you know, subscribe to the Identity Guard With Watson service. So we needed the cloud to give us the scale. We also needed the platform to be able to plug in and unplug the APIs. Some partners may not want social media monitoring. Some partners may not want this, so we didn't have to hard-code our product. We actually built three services and we can unplug any of the services. >> So, when you say you're a digital business, it strikes me that your data model is not in a bunch of silos. >> Correct. >> You've got a data model that's accessible, maybe through sets of APIs, et cetera, that your human experts can go attack. >> Correct. >> Is that a fair assertion? >> Yeah, that's fair. One other thing about Watson. We were going to use Watson from day one, I was convinced. And I was the one that took the company on this journey. But the other thing I like about Watson is that you don't, Watson doesn't keep the data, right? We talked to the other big players in this field and one of their mandates is, they always keep the data. All of it. And, and Watson shreds the data and we don't keep all the data. So think of all the social media and other data that flows through this funnel. People out there want to keep it so then they can reverse profile consumers or cohorts or, Watson shreds the data. You're not in the, you're not in the spoofing or spying business, nor are we. So that was also a really important consideration. >> Yeah, I said that at the top, that you're, you're going to hear this from Ginni tomorrow. I can almost guarantee ya, she's going to say that we're not in the business of trying to re-mine your data and re-target. >> Right. >> But, so that was, I was going to ask you why Watson. That was one reason. What about the quality of the, of the machine intelligence? >> Yeah. >> You hear a lot, you know, you hang around Silicon Valley, "Oh yeah, Watson." How does it compare, in your view? >> Yeah. >> You're a practitioner who's, you know, you're familiar with all this. >> So they have more refined, first of all, more APIs, right? More, some of them not relevant to us, the medical ones, which are amazing and fascinating, >> Yeah, but, yeah. >> but they had more structured APIs and a better road map on where they were going. And what we found from day one is that, if we defined something, they would say "We'll jump in and help", right? It's really important when you're the first one, you know, the tip of the spear, you don't know, you don't know what you don't know. And we found from day one, the IBM team has treated us like we're General Electric, right? Or General Motors, right? We're just, you know, a couple of hundred million dollar company trying to make a big difference in a important space. And they have treated us like a Fortune 100 company from day one and really appreciate it. >> So as >> And their science is so good. >> Sorry there, as the CRO, going from identity 1.0 to 2.0, this journey that you're on. You mentioned competition. How many, talk to us about the actual financial impact to the company that you can say that you've been able to achieve on this journey to identity 2.0. Presumably, leaving some of your competition back in the 1.0 land. >> Yeah, yeah, actually, our competition will be behind us for at least a couple years 'cause it takes a couple years. You know, you don't do this quickly. So we are out, we launched, we launched Watson in December. We actually launched, we distribute our product through partners, most of it, 90%. 10%, people come to our site and sign up online but we launched 21 partners in January, 11 in February, 13 in March we'll launch. So by the end of the year, we predict we'll have about 200 Watson partners distributing our product, which would give us a huge head start and advantage over anybody else. Once you see what we're doing and you see what else, the 1.0 version, it's almost impossible to pick 1.0. It's impossible, right? So our job is to get more, create more awareness in the distribution channels so that people are, are understand that Watson is out there and available. >> And, and this is a subscription service, I think you said, upfront? >> Yeah. >> And you've got different tiers, etc? >> Yes, yes. >> And you guys have a couple of, of sessions >> that you're participating in at the event? >> We do. >> Yeah, I know that we're on tomorrow afternoon and I believe Wednesday morning. >> Great. >> So, yeah. >> Well Jerry, thanks so much for stopping by theCUBE >> You're welcome. >> and sharing what you guys at Identity Guard are doing with data, >> Thank you. >> I mean, it's fascinating. >> Appreciate you talking to us. >> Dave: Thanks for coming on. >> Yeah, thanks, pleasure. >> And we want to thank you for watching theCUBE. I'm Lisa Martin with Dave Vellante again. This is day one of theCUBE's three days of coverage at the inaugural IBM Think 2018. Stick around, we'll be right back with our next guest after a short break. (bright music)

>> Announcer: Live from Madrid, Spain. It's theCube covering HPE Discover Madrid 2017. Brought to you by Hewlett-Packard Enterprise. >> And we're back at HPE Discover Madrid 2018. This is theCube, the leader in live tech coverage. I'm Dave Vellante, with my co-host Peter Burris. This is day two of the event. Johannes Koch is here, he's the Vice President and Managing Director of Central Eastern Europe, Middle East and Africa for Hewlett-Packard Enterprise, and he's joined by Ali Saleh, he's the Senior Vice President and Chief Commercial Officer at GE Digital for Middle East and Africa. Gentlemen, thanks so much for coming to theCube. >> Appreciate it. Thank you. >> Thank you for having us. >> Johannes, let's start off with you. GE, HPE, what are you guys all about, what are you doing together? Talk about the partnership and the alliance. >> So, you know, it started actually one month ago, I suppose and it was meetings that we had with General Electric to understand the customer requirements in cybersecurity, and what we figured is in this world of IoT, Internet of Things there is an increased requirement for security. And there was, from our perspective, lots of solutions out there but it's quite difficult for customers to understand the landscape and who to turn to. And we also figured that in this world, nobody can serve every requirement of a customer, so this is how we figured out with GED that we have a joint interest here, to serve in the Central/Eastern European and then mainly in the Middle East and Africa part our customer base. And this is how it started and I think what I can say is it has accelerated incredibly during the last two months since we signed the joint agreement. We've been building a channel, we've been having lots of meetings with customers and built a really nice pipeline in the meantime, also I think here the show reflects an incredible interest by our customers. So I think we are in a very good state at the moment having a lot of interest, probably all the key customers in our region having this on their agenda. >> Ali, maybe you could just describe the situation, the industrial expansion if you will in Middle East and Africa, what you guys are seeing in terms of the big trends, and what the opportunity looks like. >> Well thank you. You know, GE has been in the Middle East, Africa, for over 80 years in some countries, and we have deep relationships on industrial side, whether it's power, oil and gas, aviation, healthcare and others. And our customers are thinking a lot about cost, quality, and access, and productivity is top of mind, and they've discovered that their industrial assets are smart and capable but the data are not being collected. So when we collaborate with ecosystem of partners, and we fetch the data and get connected, and get insights from the machine to make them able to make the right decision at the right time and then it drives optimization. This is top of mind. They want to see how they can do better for less. >> Okay, so the customers at GE Digital, the customers are going digital, they have all these devices, instruments, machines, and they're moving in a new direction you guys are trying to lead in. What are the challenges that they're facing, what are they asking your help on, what are the big problems that they're trying to solve? >> So, everyone wants to talk about productivity and calls out. The challenge is that not everyone is ready for digital transformation. Some do not feel there is a burning platform, and those that are ready when they feel there is a burning platform, they don't have a plan, they don't have a playbook. So it's important that we collaborate and help our partners and customers understand their current state and heat map and desired state and pinpoint to quick wins so that they get it and they see incremental improvement. And asset performance management has been an easy way for us to say, "Your asset is underutilized "compared to your industrial entitlement "you can do 10x better," and this gets their attention, and this is where we see the power of one in the industrial age is relevant, one percent. In our market, in the world free market, when we talk to them about one or two percent productivity they laugh at us. They say, "Talk to us about ten or twenty." Because there has been a lot of gap in productivity and efficiency. >> Are you able to, I mean, it's only been nine months, but are you able to start to see any kind of customer results at this point? Do you have some examples even early wins with customers? >> So to be exact, the start of the relationship in a formal way-- >> Was it, what'd you say? >> --is two months. >> I thought I heard nine months. >> No, no, we started our first conversations and until it was over, it came to the agreement-- >> So it was brand new, in terms of... >> It is really brand new and I think what we can say is, I think we have 180 partners already engaged. We have probably more than 500 customer contacts in the region already so with large accounts. And we have a pipeline that is multi-million dollar in size. So we're expecting the first close within our first quarter, which ends at January 2018. I think there's no question that there is a big market opportunity out there, right? And I think the show here, I think for me, even accelerated things, because I think in the past, digital transformation was sort of limited to a few industries, we always give travel industry, we take banking sometimes but here, I think what became transparent to many of the customers that we had here, that there is no industry that is sort of immune against what is happening out there and specifically also that the sensors and the devices out there require special attention. And I think with the, specifically on the OT side, we have a solution now with GED that we can basically roll out across our territory. >> So I wanna talk about three things very quickly, I'm gonna lay this out and ask you if in fact this is going to catalyze that much more attention. Number one is a lot of the industry in the Middle East and Africa are natural resource industries, where the historical ways of doing things have been relatively inefficient. So there's a lot of opportunity to use IoT and related things to bring more efficiency, better practices, overall resource management. Number two is, that the technology's now capable of doing that in places where you don't necessarily have the best infrastructure. Aruba technology, for wireless, some of the other things are now possible, that adds to it. And number three, we've seen some recent steps in liberalizing some of the countries that have the most opportunity to do some things differently. You know, Robert Mugabe, no longer in Zimbabwe, the new prince in Saudi Arabia talking about liberalizing things. Are you seeing these come together in a way that would encourage people to think new ways, do new things, use information perhaps differently than it did before? What do you think, is this a confluence, is this a moment? >> Well I agree with you, and absolutely. Today, our customers and partners in region are more ready than before, and they're pulling hard. And when we put our act together as an ecosystem of partners we make it easier on them to make the right decision. When we talk productivity, productivity comes from people, from process operation, from industrial entitlement. And when we talk about the digital thread that brings it all together whether we look at the culture and vision and mission and people utilization, look at the process defined or not, and how we can optimize it, look at the industrial entitlement, and tell them, "Compared to your peers, "this is where you can be." We have their attention. And with the push from the government for productivity and utilization and do more for less, this is becoming top of mind, everyone is talking about it. So, when we partner together and we say, "This is the playbook, this is how you can start, "and this is the edge to cloud solution in a secure way." And we link it to the industrial entitlement, and let's underline industrial, because when we speak the healthcare language and the power language and the oil and gas language we get their attention. >> Excellent, so there's an increasing interest, and you anticipate that there's going to be new action with their pocketbooks. >> Johannes: Yes. And I would add, I think we, this is not an easy marketplace but you can have some outstanding projects. And we have, in the region, you may have heard about, there was in the private investment fund, when the crown prince did announce the NEOM project. Or, we have in Dubai Smart City as a project, with the city of Dubai which are all projects that probably would not happen in Western Europe. So there is potential, there are bigger things happening, and I think there is also an understanding that this is a way how to leapfrog, to your point, to leapfrog technology. And I think that is what can happen. What we need to be careful of is where to invest, because there are lots of ideas out there, and to understand what are the real things, and what are the things that we need to make happen. This is, I think, the challenge. >> And they wouldn't happen in Western Europe because, what? The maturity of the infrastructure, the space limitations, the appetite? >> Johannes: I think, to give the example of Smart City. >> Dave: Yeah. >> So I think we have a lot of, in my remit we have lots of discussion on Smart City. But it's usually you have to find the city that is willing to pay a POC. >> Pete: 12 layers of bureaucracy. >> And exactly. And you need to talk to each and every city individually, whereas here, if you have a decision maker to say, "Yes, we do this." >> Pete: Yep. >> Dave: Right. >> And then we do it. >> Dave: You cut the line. >> And the answer is about readiness. When you go to a large enterprise that's very successful, you meet the CEO and you quickly conclude whether they're ready for digital transformation or not, are they gonna make this top of mind for them? Are they gonna give you time? Are they gonna talk about productivity? Or is this going to be an IT discussion, and they're gonna treat you as a supplier? Those that are ready, we roll up our sleeves, and we put in our dedicated resources to help them look at the transformation. When the government official is pushing and mandating for calls out, then obviously everyone wants to copy and talk about it. And this makes it easier for us to execute. >> You're talking, again, big numbers. Not one percent, ten percent, so that's the nirvana. How confident are you that you can actually have that type of impact? >> So we've got data points, right? If you look at healthcare in the Saudi Ministry of Health, we've been collaborating on looking at operating room optimization or emergency room optimization, without touching digitization. Looking at the process and utilization of appointments, no-show, and the way the clinical governance is taking place, we're showing 40 percent improvement. If you look at, the factory of the future with Obeikan in Saudi Arabia, we've got asset performance improvement project, and they already yielded a 12 percent improvement, and the entitlement is up to 20, that we're working on. When you transform something, it's iterative, right? When you transform something that you have not pushed for efficiency before it's easy for the first iteration to show an incremental change. >> Pete: Yup. >> That challenge will be for the change to last. And this is where digitization makes it last and makes it impactful. And when we look at the HPE relationship with the MOH on the electronic medical record, we've got right now two active projects with two hospitals, and it's all powered by Predix, and HP peripherals are being deployed to the site. And if we go to the Saudi Electricity Company, we've got a project now on asset performance management across all their assets and again HPE peripherals are also deployed and it's all about GE ecosystem of Predix-enabled solutions. >> So I've had the pleasure and honor of speaking in front of a relatively large group of CIOs a couple times in Africa in the past few years. And I always was surprised by the degree to which they suggested that the necessity of change in this region, and the fact that a little bit of technology can have an enormous impact, the degree to which we might actually see some leadership technology come out of this region. What do you think, are the types of issues, the types of problems that could be solved with this technology in Africa, the types of problems that solving them there could actually start driving the industry in different directions? Solving new classes, whole new classes of problems. Do you think that this type of technology can have a transformative effect, not only in Africa but more broadly? >> Absolutely, this is a way for systems to leapfrog. If you look at Kenya right now, they've got a transformation project for 98 hospitals. And they've got massive shortage of radiologists. So right now, we're replacing equipment in 98 hospitals but tele-radiology is the answer for the shortage of radiologists. If you look to South African Discovery, what Discovery is doing is best in class, and I haven't seen any other insurance company looking at the ecosystem the way they do it. So, absolutely, we're seeing pockets of excellence in Africa, and this can be a way to leapfrog. >> You said you started the conversations around security. >> Johannes: Cybersecurity. >> What was that conversation like? Why was that the starting point? I mean, obviously it's important, but why? >> To be honest, I would have to leave this to you, but I think it was because mainly there was we saw the customer interest. >> Yeah. >> And I would say, probably a year or two years ago, you would have not seen this as a very typical HPE alliance. We were technology people. We were software or hardware people. What you see in, and I mentioned in the beginning, in the world of IoT, things are blurring a bit. What is happening on the edge is very much in the business of General Electric. So I think this builds automatically the new ecosystem. When you look here at Discovery, the alliance has become more and more industrial companies. It's linked to industrial 4.0, car industries and all that. Everywhere where data is being created, we need to have the partnership because and that is because the data that is being created at the edge also needs to be computed at the edge. If we want to be successful, we gotta say, "We cannot limit ourselves to the "data centers the rest is the others." And this is where I think we find the very good connection point because now we have software that actually can operate at the edge. I think you have good examples on that. >> Yeah absolutely, if you look at the pain point in Middle East, Africa, majority of our partners and customers are government entities and for them top of mind securing their large industrial assets is important. And in the operations space there hasn't been much done on security, where you can go into a hospital and simulate light flickering with a voltmeter. And you can take over the temperature and play with it. Today there's a lot of smart sensors out there, but we're securing the IT firewall, but within the hospital, or within the plant, we can do a lot of crazy stuff. And we owe it to our partners to show our capability. That's what we do within our factories, and our platform is designed around security for operations. So the easier interlock with HPE is our ability to get closer to the edge and peripherals, and ensure the operation is secure and that's the first experiment but then, obviously, we're expanding beyond that to other opportunities. >> Dave: Excellent. Alright, gentlemen, we have to leave it there. Thanks so much for coming to theCube. >> Johannes: Thank you very much. >> Sharing your story, good luck with the partnership. >> Thank you. >> Dave: Hope you can come back, maybe in Las Vegas or maybe next year at this event, give us the update. >> For sure. Thank you very much. >> Thank you. Appreciate it. >> Dave: Okay. Keep it right there, everybody. We'll be back with our next guest. Dave Vellante, Peter Burris, this is theCube live from Madrid HPE Discover 2017. (upbeat music)

>> Narrator: Live from Berlin, Germany, it's theCUBE. Covering NetApp Insight 2017. Brought to you by NetApp. (techno music) Welcome back to theCUBE's live coverage of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my cohost Peter Burris. We have two guests for this segment. We have Matt Watts, he is the director and data strategist and director of technology at NetApp, and Kenneth Cukier, a senior editor at The Economist, and author of the best-selling book Big Data, and author of a soon to be best-selling book on AI. Welcome. Thank you. Thank you much for coming on the show. Pleasure to be here. So, this is the, we keep hearing NetApp saying this is the day of the data visionary. I'd love to hear both of you talk about what a data visionary is, and why companies, why this is a necessary role in today's companies. Okay, so I think if you look at the generations that we've been through in the late nineties, early 2000's, it was all about infrastructure with a little bit of application and some data associated to it. And then as we kind of rolled forward to the next decade the infrastructure discussion became less. It became more about the applications and increasingly more about the data. And if we look at the current decade that we're in right now, the infrastructure discussions have become less, and less, and less. We're still talking about applications, but the focus is on data. And what we haven't seen so much of during that time is the roles changing. We still have a lot of infrastructure people doing infrastructure roles, a lot of application people doing application roles. But the real value in this explosion of data that we're seeing is in the data. And it's time now that companies really look to put data visionaries, people like that in place to understand how do we exploit it, how do we use it, what should we gather, what could we do with the information that we do gather. And so I think the timing is just right now for people to be really considering that. Yeah, I would build on what Matt just said. That, functionally in the business and the enterprise we have the user of data, and we have the professional who collected the data. And sometimes we had a statistician who would analyze it. But pass it along to the user who is an executive, who is an MBA, who is the person who thinks with data and is going to present it to the board or to make a decision based on it. But that person isn't a specialist on data. That person probably doesn't, maybe doesn't even know math. And the person is thinking about the broader issues related to the company. The strategic imperatives. Maybe he speaks some languages, maybe he's a very good salesperson. There's no one in the middle, at least up until now, who can actually play that role of taking the data from the level of the bits and the bytes and in the weeds and the level of the infrastructure, and teasing out the value, and then translating it into the business strategy that can actually move the company along. Now, sometimes those people are going to actually move up the hierarchy themselves and become the executive. But they need not. Right now, there's so much data that's untapped you can still have this function of a person who bridges the world of being in the weeds with the infrastructure and with the data itself, and the larger broader executives suite that need to actually use that data. We've never had that function before, but we need to have it now. So, let me test you guys. Test something in you guys. So what I like to say is, we're at the middle of a significant break in the history of computing. The first 50 years or so it was known process, unknown technology. And so we threw all our time and attention at understanding the technology. >> Matt: Yeah. We knew accounting, we knew HR, we even knew supply-chain, because case law allowed us to decide where a title was when. [Matt] Yep. But today, we're unknown process, known technology. It's going to look like the cloud. Now, the details are always got to be worked out, but increasingly we are, we don't know the process. And so we're on a road map of discovery that is provided by data. Do you guys agree with that? So I would agree, but I'd make a nuance which is I think that's a very nice way of conceptualizing, and I don't disagree. But I would actually say that at the frontier the technology is still unknown as well. The algorithms are changing, the use cases, which you're pointing out, the processes are still, are now unknown, and I think that's a really important way to think about it, because suddenly a lot of possibility opens up when you admit that the processes are unknown because it's not going to look like the way it looked in the past. But I think for most people the technology's unknown because the frontier is changing so quickly. What we're doing with image recognition and voice recognition today is so different than it was just three years ago. Deep learning and reinforcement learning. Well it's going to require armies of people to understand that. Well, tell me about it. This is the full-- Is it? For the most, yes it's a full employment act for data scientists today, and I don't see that changing for a generation. So, everyone says oh what are we going to teach our kids? Well teach them math, teach them stats, teach them some coding. There's going to be a huge need. All you have to do is look at the society. Look at the world and think about what share of it is actually done well, optimized for outcomes that we all agree with. I would say it's probably between, it's in single percents. Probably between 1% and 5% of the world is optimized. One small example: medical science. We collect a lot of data in medicine. Do we use it? No. It's the biggest scandal going on in the world. If patients and citizens really understood the degree to which medical science is still trial and error based on the gumption of the human mind of a doctor and a nurse rather than the data that they actually already collect but don't reuse. There would be Congressional hearings everyday. People, there would be revolutions in the street because, here it is the duty of care of medical practitioners is simply not being upheld. Yeah, I'd take exception to that. Just, not to spend too much time on this, but at the end of the day, the fundamental role of the doctor is to reduce the uncertainty and the fear and the consequences of the patient. >> Kenneth: By any means necessary and they are not doing that. Hold on. You're absolutely right that the process of diagnosing and the process of treatment from a technical standpoint would be better. But there's still the human aspect of actually taking care of somebody. Yeah, I think that's true, and think there is something of the hand of the healer, but I think we're practicing a form of medicine that looks closer to black magic than it does today to science. Bring me the data scientist. >> Peter: Alright. And I think an interesting kind of parallel to that is when you jump on a plane, how often do you think the pilot actually lands that plane? He doesn't. No. Thank you. So, you still need somebody there. Yeah. But still need somebody as the oversight, as that kind of to make a judgment on. So I'm going to unify your story, my father was a cardiologist who was also a flight surgeon in the Air Force in the U.S., and was one of the few people that was empowered by the airline pilots association to determine whether or not someone was fit to fly. >> Matt: Right. And so my dad used to say that he is more worried about the health of a bus driver than he is of an airline pilot. That's great. So, in other words we've been gah-zumped by someone who's father was both a doctor and a pilot. You can't do better than that. So it turns out that we do want Sully on the Hudson, when things go awry. But in most cases I think we need this blend of the data on one side and the human on the other. The idea that the data just because we're going to go in the world of artificial intelligence machine learning is going to mean jobs will be eradicated left and right. I think that's a simplification. I think that the nuance that's much more real is that we're going to live in a hybrid world in which we're going to have human beings using data in much more impressive ways than they've ever done it before. So, talk about that. I mean I think you have made this compelling case that we have this huge need for data and this explosion of data plus the human judgment that is needed to either diagnose an illness or whether or not someone is fit to fly a plane. So then where are we going in terms of this data visionary and in terms of say more of a need for AI? Yeah. Well if you take a look at medicine, what we would have is, the diagnosis would probably be done say for a pathology exam by the algorithm. But then, the health care coach, the doctor will intervene and will have to both interpret this for, first of what it means, translate it to the patient, and then discuss with the patient the trade-offs in terms of their lifestyle choices. For some people, surgery is the right answer. For others, you might not want to do that. And, it's always different with all of the patients in terms of their age, in terms of whether they have children or not, whether they want the potential of complications. It's never so obvious. Just as we do that, or we will do that in medicine, we're going to do that in business as well. Because we're going to take data that we never had about decisions should we go into this market or that market. Should we take a risk and gamble with this product a little bit further, even though we're not having a lot of sales because the profit margins are so good on it. There's no algorithm that can tell you that. And in fact you really want the intellectual ambition and the thirst for risk taking of the human being that defies the data with an instinct that I think it's the right thing to do. And even if we're going to have failures with that, and we will, we'll have out-performance. And that's what we want as well. Because society advances by individual passions, not by whatever the spreadsheet says. Okay. Well there is this issue of agency right? So at the end of the day a human being can get fired, a machine cannot. A machine, in the U.S. anyway, software is covered under the legal strictures of copywriting. Which means it's a speech act. So, what do you do in circumstances where you need to point a finger at something for making a stupid mistake. You keep coming back to the human being. So there is going to be an interesting interplay over the next few years of how this is going to play out. So how is this working, or what's the impact on NetApp as you work with your customers on this stuff? So I think you've got the AI, ML, that's kind of one kind of discussion. And that can lead you into all sorts of rat holes or other discussions around well how do we make decisions, how do we trust it to make decisions, there's a whole aspect that you have to discuss around that. I think if you just bring it back to businesses in general, all the businesses that we look at are looking at new ways of creating new opportunities, new business models, and they're all collecting data. I mean we know the story about General Electric. Used to sell jet engines and now it's much more about what can we do with the data that we collect from the jet engines. So that's finding a new business model. And then you vote with a human role in that as well, is well is there a business model there? We can gather all of this information. We can collect it, we can refine it, we can sort it, but is there actually a new business model there? And I think it's those kind of things that are inspiring us as a company to say well we could uncover something incredible here. If we could unlock that data, we could make sure it's where it needs to be when it needs to be there. You have the resources to bring to bed to be able to extract value from it, you might find a new business model. And I think that's the aspect that I think is of real interest to us going forward, and kind of inspires a lot of what we're doing. Great. Kenneth, Matt, thank you so much for coming on the show. It was a really fun conversation. Thank you. Thank you for having us. We will have more from NetApp Insight just after this. (techno music)

>> Announcer: Live from Berlin, Germany, it's theCUBE! Covering NetApp Insight 2017. Brought to you by NetApp. (rippling music) Welcome back to theCUBE's live coverage of NetApp Insight. I'm Rebecca Knight, your host, along with my cohost Peter Burris. We are joined by Brett Roscoe. He is the Vice President for Solutions and Service Marketing at NetApp, and Laura Dubois, who is a Group Vice President at IDC. Thanks so much for coming on the show. Yeah, thanks for having us. Thank you for having us. So, NetApp and IDC partner together and worked on this big research project, as you were calling it, a thought leadership project, to really tease out what the companies that are thriving and being successful with their data strategies are doing, and what separates those from those that are merely just surviving. Do you want to just lay the scene for our viewers and explain why you embarked on this? Well, you know, it's interesting. NetApp has embarked on its own journey, right, its own transformation. If you look at where the company's been really over the past few years in terms of becoming a traditional storage company to a truly software, cloud-focused, data-focused company, right? And that means a whole different set of capabilities that we provide to our customers. It's a different, our customers are looking at data in a different way. So what we did was look at that and say we know that we're going through a transformation, so we know our customers are going through a journey themselves. And whatever their business model is, it's being disrupted by this digital economy. And we wanted a way to work with IDC and really help our customers understand what that journey might look like, where they might be on that path, and what are the tools and what are the engagement models for us to help them along that journey? So that was really the goal, was really, it's engagement with our customers, it's looking and being curious about where they are on their journey on digital, and how do they move forward in that, in doing all kinds of new things like new customer opportunities and new business and cost optimization, all that kind of stuff. So that's really what got us interested in the project to begin with. Yeah, and I would just add to that. Revenue's at risk of disruption across pretty much every industry, and what's different is the amount of revenue that's at risk within one industry to the next. And all of this revenue that's at risk, is really as a consequence of new kinds of business models, new kinds of products and services that are getting launched new ways of engaging with customers. And these are some of the things that we see thrivers doing and outperforming merely just survivors, or even just data resisters. And so we want to understand the characteristics of data thrivers, and what are they doing that's uniquely different, what are their attributes versus companies that are just surviving. So let's tease that out a little bit. What are these data thrivers doing differently? What are some of the best practices that have emerged from this study? Well I mean, I think if you look at there's a lot of great information that came out of the study for us in terms of what they're doing. I think in a nutshell, it's really they put a focus on their data and they look at it as an asset to their business. Which means a lot of different things in terms of how is the data able to drive opportunities for them. I mean, there's so many companies now that are getting insights from their data, and they're able to push that back to their customer. I mean, NetApp is a perfect example of that. We actually do that with our customers. All the telemetry data we collect from our own systems, we provide that information back to our customers so they can help plan and optimize their own environments. So I think data is certainly, it's validated our theory, our message of where we're going with data, but I think the data focus, I mean, there's lot of other attributes, there's the focus of hiring chief data officers within the company, there's certainly lots of other attributes, Laura, that you can comment on. Yeah, I mean, we see new roles emerging around data, right, and so we see the rise of the data management office. We see the emergence of a Chief Data Officer, we see data architects, certainly data scientists, and this data role that's increasingly integrated into sort of the traditional IT organization, enterprise, architecture. And so enterprise, architecture and these data roles very, very closely aligned is one, I would say, example of a best practice in terms of the thriver organizations, is having these data champions, if you will, or data visionaries. And certainly there's a lot of things that need to be done to have a successful execution, and a data strategy as a first place, but then a successful execution around data. And there's a lot of challenges that exist around data as well. So the survey highlighted that obviously data's distributed, it's dynamic and it's diverse, it's not only in your private cloud but in the public cloud, I think it's at 34% on average of data is in a public cloud. So, how to deal with these challenges is, I think, also one of the things that you guys wanted to highlight. Yeah, and I think the other big revelation was the thrivers, one of the aspects, so not their data focus but also they're making business decisions with their data. They tend to use that data in terms of their operations and how they drive their business. They tend to look for new ways to engage with their customers through a digital or data-driven experience. Look at the number of mobile apps coming out of consumer, really B to C kind of businesses. So there's more and more digital focus, there's more and more data focus, and there's business decisions made around that data. So, I want to push you guys on this a little bit. 'Cause we've always used data in business, so that's not new. There's always been increasing amounts of data being used. So while the volume's certainly new, it's very interesting, it's by itself not that new. What is new about this? What is really new about it that's catalyzing this change right now? Have you got some insights into that? Well, I would just say if you look at some of the largest companies that are no longer here, so you've got Blockbuster, you've got Borders Books and Music, you've got RadioShack, look at what Amazon has done to the retail industry. You look at what Uber is doing to the transportation industry. Look at every single industry, there's disruption. And there's the success of this new innovative company, and I think that's why now. Yes, data has always been an important attribute of any kind of business operation. As more data gets digital, combine that with innovation and APIs that allow you to, and the public cloud, allow you to use that as a launch pad for innovation. I think those are some of the things about why now. I mean, that would be my take, I don't know-- Yeah, I think there's a couple things. Number one, I think yes, businesses have been storing data for years and using data for years, but what you're seeing is new ways to use the data. There's analytics now, it is so easy to run analytics compared to what it was just years ago, that you can now use data that you've been storing for years and run historical patterns on that, and figure out trends and new ways to do business. I think the other piece that is very interesting is the machine learning, the artificial intelligence, right? So much of the industry now, I mean, look at the automotive industry. They are collecting more information than I bet they ever thought they would, because the autonomous driving effort, all of that, is all about collecting information, doing analytics on information, and creating AI capabilities within their products. So there's a whole new business that's all new, there's whole new revenue streams that are coming up as a result of leveraging insights from data. So let me run something by ya, 'cause I was looking for something different. It used to be that the data we were working was what I call stylized data. You can't go out here in Berlin and wander the streets and find Accounting. It doesn't exist, it's human-made, it's contrived. HR is contrived. We have historically built these systems based on transactions, highly stylized types of data. There's only so much you can do with it. But because of technology, mobile, IOT, others, we now are utilizing real world data. So we're collecting an entirely new class of data that has a dramatic impact in how we think about business and operations. Does that comport with what the study said, that study respondents focusing on new types of data as opposed to just traditional sources of data? We certainly looked at correlations of what data thrivers are doing by different types of data. I would say, in terms of the new types of data that are emerging, you've got time series data, stream data, that's increasingly important. You've got machine-generated data from sensors. And I would say that one thing that the thrivers do better than merely just survivors, is have processes and procedures in place to action the data. To collect it and analyze it, as Brett pointed out, is accessible, and it's easy. But what's not easy to is to action results out of that data to drive change and business processes, to drive change in how things are brought to market, for example. So, those are things that data thrivers are doing that maybe data survivors aren't. I don't know if you have anything to add to that. Yeah, no, I think that's exactly right. I think, yes, traditional data, but it's interesting because even those traditional data sets that have been sitting there for years have untapped value. >> Peter: Wikibon knew types of data. That's right. But we've also been doing data warehousing, analytics for a long time. So it seems as though, I would guess, that the companies that are leading, many that you mentioned, are capturing data differently, they're using analytics and turning data into value differently, and then they are taking action based on that data differently. And I'm wondering if across the continuum that you guys have identified, of thrivers all the way down to survivors, and you mentioned one other, data-- >> Laura: resisters. resisters, and there was, anyways. So there's some continuum of data companies. Do they fall into that pattern, where I'm good at capturing data, I'm good at generating analytics, but I'm not good at taking action on it? Is that what a data resister is? So a data resister is sort of the one extreme. Companies that don't have well-aligned processes where they're doing digital transformation on a very ad hoc basis, it's not repeatable. They're somewhat resistant to change. They're really not embracing that there's disruption going on that data can be a source of enablement to do the disrupting, not being disrupted. So they're kind of resisting those fundamental constructs, I would say. They typically tend to be very siloed. Their IT's in a very siloed architecture where they're not looking for ways to take advantage of new opportunities across the data they're generating, or the data they're collecting, rather. So that would be they're either not as good at creating business value out of the data they have access to. Yes, that's right, that's right. And then I think the whole thing with thrivers is that they are purposeful. They set a high level objective, a business-level objective that says we're going to leverage data and we're going to use digital to help drive our business forward. We are going to look to disrupt our own business before somebody disrupts it for us. So how do you help those data resistors? What's your message to them, particularly if they may not even operate with the belief that data is this asset? I mean, that's the whole premise of the study. I think the data that comes out, like you know, hey data thrivers, you're two times more likely to draw two times more profitability to there's lots of great statistics that we pulled out of this to say thrivers have a lot more going for them. There is a direct corelation that says if you are taking a high business value of your data, and high business value of the digital transformation that you are going to be more profitable, you're going to generate more revenue, and you're going to be more relevant in the next 10 to 20 years. And that's what we want to use that, to say okay where are you on this journey? We're actually giving them tools to measure themselves by taking assessments. They can take an assessment of their own situation and say okay, we are a survivor Okay, how do we move closer to being a thriver? And that's where NetApp would love to come in and engage and say let us show you best practices, let us show you tools and capabilities that we can bring to bear to your environment to help you go a little bit further on that journey, or help you on a path that's going to lead you to a data thriver. Yeah, that's right, I agree with that. (laughs) What is the thing that keeps you up at night for the data resister, though, in the sense of someone who is not, does not have, maybe not even capturing and storing the data but really has no strategy to take whatever insights the data might be giving them to create value? I don't know, that's a hard question. I don't know, what keeps you up at night? Well, I think if I were looking at a data resister, I think the stats, the data's against them. I mean, right? If you look at a Fortune 500 company in the 1950s, their average lifespan was something like 40 years. And by the year 2020, the average lifespan of an S&P 500 company is going to be seven years, and that's because of disruption. Now, historically that may have been industrial disruption, but now it's digital disruption, and that right there is, if you're feeling like you're just a survivor, that ought to keep a survivor up at night. If I can ask too. It's, for example, one of the reasons why so many executives say you have to hire millennials, because there's this presumption that millennials have a more natural affinity with data, than older people like me. Now, there's not necessarily a lot of stats that definitely prove that, but I think that's one of the, the misperceptions, or one of the perceptions, that I have to get more young people in because they'll be more likely to help me move forward in an empirical style of management than some older people who are used to a very, very different type of management practice. But still there are a lot of things that companies, I would presume, would need to be able to do to move from one who's resisting these kinds of changes to actually taking advantage of it. Can I ask one more question? Is it that, did the research discover that data is the cause of some of these, or just is correlated with success? In other words, you take a company like Amazon, who did not have to build stores like traditional retailers, didn't have to carry that financial burden, didn't have to worry so much about those things, so that may be starting to change, interestingly enough. Is that, so they found a way to use data to alter that business, but they also didn't have to deal with the financial structure of a lot of the companies they were competing with. They were able to say our business is data, whereas others had said our business is serving the customer with these places in place. So, which is it? Do you think it's a combination of cause and effect, or is it just that it's correlated? Hmm. I would say it's probably both. We do see a correlation, but I would say the study included companies whose business was data, as well as companies that were across a variety of industries where they're just leveraging data in new ways. I would say there's probably some aspects of both of that, but that wasn't like a central tenent of the study per se, but maybe that will be phase two. Maybe we'll mine the data and try and find some insights there. Yeah, there's a lot more information that we can glean from this data. We think this'll be an ongoing effort for us to kind of be a thought leader in this area. I mean, the data proved that there was 11% of those 800 respondents that are thrivers, which means most people are not in that place yet. So I think it's going to be a journey for everyone. Yes, I agree that some companies may have some laws of physics or some previous disruptions like brick and mortar versus online retail, but it doesn't mean there's not ways that traditional companies can't use technology. I mean, you look at, in the white paper, we used examples like General Electric and John Deere. These are very traditional companies that are using technology to collect data to provide insights into how customers are using their products. So that's kind of the thought leadership that any company has to have, is how do I leverage digital capabilities, online capabilities, to my advantage and keep being disruptive in the digital age? I think that's kind of the message that we want them to hear. Right, and I would just add to that. It's not only their data, but it's third-party data. So it's enriching their data, say in the case of Starbucks. So Starbucks is a company that certainly has many physical assets. They're taking their customer data, they're taking partner data, whether that be music data, or content from the New York Times, and they're combining that all to provide a customer experience on their mobile app that gives them an experience on the digital platform that they might have experienced in the physical store. So when they go to order their coffee in their mobile pay app, they don't have to wait in line for their coffee, it's already paid for and ready when they go to pick it up. But while they're in their app, they can listen to music or they can read the New York Times. So there's a company that is using their own data plus third party data to really provide a more enriched experience for their company, and that's a traditional, physical company. And they're learning about their customers through that process too. Exactly, exactly, right. Are there any industries that you think are struggling more with this than others? Or is it really a company-specific thing? Well, the research shows that companies in ever industry are facing disruption, and the research shows that companies in every industry are reacting to that disruption. There are some industries that tend to have, obviously by industry they might have more thrivers or more resisters, but nothing I can per se call out by industry. I think retail is the one that you can point to and say there's an industry that's really struggling to really keep up with the disruption that the large, people like Amazon and others have really leveraged digital well advanced of them, well in advance of their thought process. So I think the white paper actually breaks down the data by industry, so you can kind of look at that, I think that will provide some details. But I think every, there is no industry immune, we'll just put it that way. And the whole concept of industry is undergoing change as well. That's true, that is true, everything's been disrupted. Great, well, Brett and Laura thank you so much for coming on our show. We had a great conversation. Thank you. Enjoy your time. You're watching theCUBE, we'll have more from NetApp Insight after this. (rippling music)

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

>> Narrator: Live from Las Vegas, it's theCUBE covering HPE Discover 2017 brought to you by Hewlett Packard Enterprise. >> Welcome back, everyone. We are here live in Las Vegas for HPE Discover 2017 exclusives look at angle cube coverage, our seventh year. I'm John Furrier with my co-host Dave Vellante. Our next guest is Tripp Partain, HPE CTO for GE General Electric and Anthony Rokis, VP of Software Engineering at Predix with GE Digital. Guys, welcome back to theCUBE. Good to see you. >> Thanks guys. >> Thanks for coming on. Obviously, GE has really been on the front end of IOT. You guys have been doing extremely well and changing over, bringing digital to analog, kind of connecting those worlds. What's your take on this intelligent Edge? You got to love the messaging. You got to love the messaging with HP. >> God, it's great. I think this is really starting to take off. If you look at our positioning, we really are going after the Edge, right. And with Predix being our forefront in the Predix system, we really believe in the opportunity here. I think, as you heard Meg speak yesterday, the engagement between GE Digital and HPE is getting stronger, we're finding more and more synergies over time. And both our strategy and their strategy are really starting to line up very nicely, both Edge and computing in general. >> I had a chance a couple years ago to host a panel with your CEO Jeff Amels, and United Airlines, Hospital in Chicago and at that time it was really hardcore, tangible dollars on the line. I mean, we're talking highly instrumented devices and machinery that you guys are in and there were some significant dollars involved. Just getting the data is a very low-hanging fruit, but big numbers, this is now going mainstream where everyone's kind of having this awakening moment, Tripp, where it's kind of like, "Hey, we're just going mainstream." So what's next for you guys, as the world starts getting up to speed on IOT, what's next for GE? What are you guys doing now to go onto the next level? What's that next tier of digital IOT for you guys? >> Yeah, honestly in my view and GE's view if you look at what we've done in the past, it's really the foundations getting in place. It's censor-enabled devices getting assets. The censor is more progressive, and that's kind of been the first sort of step, right. Then we get into how do we collect that data? Where you think GE is headed now, are the smart analytics. It's the outcomes that are going to drive those big dollars in productivity. It's really getting into the digital industrial revolution area. To date, it's been a lot of the foundation getting in place, and I think that's where you're going to see tremendous growth over time is. When you unleash data scientists on wealth of information, the outcomes in the productivity, the world and the economy is going to see is going to be great. >> I love that quote, with Jeff Immelt. We refer to it all the time. I went to bed an industrial giant, and woke up, you know a software company. And so it clearly underscores the transformation. We were talking off-camera about the study that we did many, many years ago. I mean, the numbers are staggering. It's in the trillions. But one of the things that we found was this notion of, and we talk about it all the time and I'd love to get your take on it is the IT and OT. They're not talking to each other. Typically, they're not birds of a feather. What are you seeing, Tripp, in your experience with customers in terms of those organizational, let's start with the IT side and we can talk about the OT side. >> Yeah, and as we've had our partners show up with GE continue to develop, the one thing we've found is we have a lot of similar customers. And in these same customers are extremely large customers, but what's interesting we don't talk to any of the same people. Right, on the HPE side we tended to talk to the IT teams and data center and GE would be out in the factory floor or out in the field and more industrial. But in order to really fulfill the IOT promise, the two groups are really having to come together and I think it's taking time for messaging to really sort out there. And one of the things that we're really doing, taking advantage of our partnership to help solve the problem is when we have IT teams come to visit HPE, we bring along GE operational experts to actually talk about the business side of the outcome so it's not just an IT conversation. And really intentionally crossing those paths and leveraging our partner in GE to bring that capability to us so we can have a holistic conversation to the customer. >> So who's in charge here, who's driving the bus? Is it the OT guys, or the IT guys, or somebody above? >> It's both, there's two drivers. >> Uh oh. >> Two hands, four hands on the wheel almost. If you look at the OT side, there's a lot of challenges we're facing where HPE and the IT community is coming to help. For instance, data sovereign team, right. So one of the challenges we have is a lot of our companies, our customers, want data sovereignty and this is where IT has solved that problem for us and on the OT side, we need to figure out how do we store, maintain, analyze that data within a country. And again, that's why we're bringing the IT companies with us and partner to help us. >> So when a plane flies from Spain, crosses France, Germany, and ends up in Ireland, where is the data? (laughs) >> Very good question. >> Well it infringes the data, because there's sort of a data love triangle going on. You've obviously got devices installed, HPE brings equipment, and the customer. So, talk about the conversations that you're having with your customers. I mean, who owns the data. The factory says, "Hey, wait a minute it's a system. That's my data." GE obviously has to do predictive maintenance and same with HPE. There's all this data flowing. What about data, I don't know, ownership or IP, what are those conversations like? >> Yeah, I can say certainly from the GE side it's always been our stance that the customer owns their data, right. We are running a multi-tenency environment and a platform. And they own that data. How that data is stored, we can help facilitate, right. We offer Cloud store and a couple other technologies that allow that. But at the end of the day in a multi-tenent environment, the customer owns that data. And we will facilitate with HPE where that data needs to reside based on the customer's need. >> So you're not trying in any way to monetize that data? I mean, I'm astounded, why not? >> I think the monetization really comes in with how you empower the customer to get the value out of the data. And in a former life, I worked through the data monetization world and there is certain amounts of value in the data itself. There's also value in helping the customer determine what their data can offer to them and the business cases that we're able to jointly present to the customer and the value that that generates still allows for us to monetize the process by which we help enable the customer to really bring these data assets together. Really understand areas that they may have seen silos of the data before, but they weren't looking holistically at it and being able to, in a very timely fashion correlate between that and then actually see a different answer to a problem where yes, this meter may be reading 80 and it should be 60, but if I throttle it to 70 and I get 10% more output, it's worth running at 70 because of the benefit on the revenue. So you actually can make trade offs across certain areas where you weren't able to do that. >> But Predix is informing models, is it not, I mean. >> Yeah, I mean at the end of the day, we're taking that data and for the customer created an outcome. Right, the analytic, the information that we can derive out of it to make a more productive or a more efficient outcome of running operation, that's where we get the monetization from. >> If data's a new oil then you need to refine it, was your point about the monetization question. That's interesting because we see the same thing where if you make the data freely available or you treat it as an asset to the customer, it's how it's monetized in its effect. Or there's a tacticle, let's monetize our data. So depending on how you look at it, there's different approaches, right? I mean this is kind of the key thing. >> Right, and even though this is not the way now, if you follow the history of how other industries have dealt with the data. So I came out of credit services long ago, and it's very common now, in the credit services industry for data to be monetized and leveraged like for credit reports and for that whole banking financial process to take place, but it didn't start that way. So my guess is, as we continue to show value to the customer of their own data as they then start to think about, "Wow but if I could do comparables between my data and industry data that would help me even more." I expect that the customers that today that are worried about who owns the data, will eventually start asking players like HPE and GE Digital to help them solve that problem. And they'll evolve to that sort of data monetization like a lot of the other industries have. >> A whole new digital just creates a whole new way to look at things, it's not a linear supply chain anymore whether relative to data or what not, so super cool. Final question for you guys and I appreciate you coming on theCUBE and sharing your insights. What's next for the partnership with HPE and GE Digital? Obviously, the digital transformation's in full swing impacting business transformation, impacting the Dev Ops aspect of Cloud. All this cool stuff's happening, true private Cloud's on fire, hybrid's the doorway to Multi Cloud. A lot of cool stuff happening, what's next for you guys? >> Yeah I think from our side we're really excited about the partnership on the Edge, right. When we start looking at the computing requirements and needs at the Edge, close to the asset, low latency that's where HPE and GE are really going to start to partner very heavily and you're going to see a lot more engagement at that level. So I think the Edge is going to be our focal point. >> Oh absolutely, and I think the uniqueness we bring to the market with our Edge line converged systems, we're able to do things at the Edge, leveraging GE Predix and then also bringing in other third party partners in conjunction and now you have enough computer power in the right form factor that can all sit and reside at the Edge, process at the Edge and solve the problems there locally. Doesn't take away from the Cloud aspect, doesn't take away from being able to have a macro view across multiple scenarios. But if I'm on an oil rig in the middle of the North Sea, you know it's going to be very important for me to have everything I need in the right form factor at the lowest power utilization possible and still solve my problems. >> And can process all the data right there. Guys, we are pushing it to the Edge here theCUBE goes out to the events, that's the Edge of the action. We'll bring you all the great videos. Thanks for coming on, this is theCUBE live coverage from the Edge at HPE Discover 2017. I'm John Furrier, Dave Vellante. Be right back with more, stay with us. (digital music)

>> Commentator: Live from Palo Alto, it's theCUBE, covering Women Transforming Technology 2017, brought to you by VMware. >> Welcome back to theCUBE's coverage of Women Transforming Technology held at VMware. I'm your host Rebecca Knight. Joining me today is Cornelia Davis. She is the Senior Director of Technology at Pivotal which is the Palo Alto-based company that provides Agile development services on an open source platform. Thank you so much for joining us. >> Thank you for having me. I'm so happy to be here. >> So before the cameras were rolling, you started telling me a little bit about your personal story. You're a woman in tech who loves the tech, but you said for the past three years, you've also become an activist and an evangelist for getting more women into this business. Tell us about that transformation. >> Yes, I'll tell you a little bit about that story. I have the gray hair to prove it. I've been doing this for some time. I actually was a woman studying computer science back in the day where we were getting close to equity. >> Rebecca: There was a time when it was-- >> Yeah, there was so back in the '80s, I was majoring in computer science and I think that we were close to 40% at the time, although I have to say even before I was in college, I was always the girl who was out playing soccer with the boys at lunch time. Gender never really seemed to make much of a difference to me but anyway, I got a degree in computer science and then I spent 25 years in the industry and sure, there were times where I would notice that I was the only woman in the room. Actually I would say maybe three or four years ago, I went to a customer opening where they were catering to the developer community and in the room there were 250 developers, I was the only woman. I mean seriously, I was the only woman of 250 and I was like wow. But other than notice it and chuckle about it and even have some of those experiences where maybe somebody assumed that I was the HR person and not the technologist, those types of things, I never really did anything about it. And then about three years ago, I had the great fortune of meeting Robin Hauser Reynolds and Stacy Hartmann who are the two women behind the movie Code: Debugging the Gender Gap, you've seen it? >> Rebecca: Yes, yes. >> A fantastic film, a fantastic piece and had this opportunity to meet them and got involved in the film and Pivotal became a sponsor. They did some of the filming. They did some interviewing of people at Pivotal and it was through that experience and then I got to go to some of the screenings and participate in panels and so on and it was through that experience that I started to understand that it wasn't just curiosity, that it was actually declining, the numbers were declining and that it was a real serious problem. And so after being in the industry for 25 years and not really doing anything about it, I've become an activist and so I spend a lot of time jabbing on about this. I'll give you another example. Last year in January, Pivotal brought most of the company together here in the Bay Area. We brought about 1,200 people into the Bay Area for worldwide kickoff. And the very first talk that they had after our CEO spoke was a talk on diversity and they actually invited me to come up and speak about gender diversity or lack thereof in technology and talked about the Girls Who Code and some of those great programs out there. >> I want to get back to Girls Who Code because I know that you're passionate about it, but I want to also just get back to the moment that you described where you went from chuckling about being the only woman in the room and saying, "Oh it's not silly," to really feeling, "Hey this isn't right. "I want things to be different." What was that moment? Are you trying to recreate that moment for other women as a wake up call? How would you describe your activism? >> I don't know that it was a moment, but the thing that catalyzes me, the thing that makes me really passionate about doing this is that I have this tremendous opportunity. The way that I came into computing personally was at the end of my sophomore year in high school when we were signing up for classes the following year, I was looking at what might I sign up for and I signed up for a computer programming class and then I went off and I joked around that I went off and had a bitchin' summer. That's the stuff we said in the '80s. I went off and had a bitchin' summer. >> We should bring that word back. Let's do it, Cornelia. >> It's a good word. And I came back and had this computer class on my schedule and I was like, "Uh no, no, no, no. "There is no way I'm doing this." And I skipped class for the first two or three days and then I finally went and curiosity got the better of me. I tried it out and I was hooked. Literally that was the moment, not for my activism, but that was the moment where I had like, "Oh my gosh, this is going to change everything. "This is what want to I do." And that's what brought me to computing and that's what makes me an activist now because I didn't realize for those 25 years that other people didn't have those opportunities, that they were actually systemically being discouraged from having those opportunities and so I think that's at the core of my activism is I want people to have the opportunity because I love what I do so much and I think I was mentioning before before we started rolling the cameras that I've been a technologist my whole career. Occasionally I've branched off and tried to do maybe a little bit more leadership or a little bit more of that, but I love the tech so much and it's such a great wonderful career to be in, self-sustaining and all of those things, I want other people to have that opportunity. That's what gets me going. >> I was reading a bio where you're a self-described propeller head and you can find her knee deep in the code and now you want to inspire the next generation and so you've gotten involved with Girls Who Code. Tell us more. >> Yes so it wasn't actually through the film. I think it was just simply, it was serendipitous, right around the time that I was starting to awaken to what was going on in the industry. Working for Pivotal, Pivotal in our San Francisco office, it's a very cool office. It's very different from what I saw in most of my career which was cube farms. It's a very open floor plan, very hip, just a cool place to be. >> What the rest of us East Coasters envisions Silicon Valley to be. >> Yeah, it's really pretty cool. And so the Girls Who Code, for those of you who might be watching that don't know about the Girls Who Code, it's an organization that really targets high school girls and their flagship program is in the summer they have a seven-week immersion program where they bring girls in and they basically code, they learn to code from nine to five every day for seven weeks. It's a pretty intensive program. Well about three years ago, we weren't sponsoring at that level, but we would be a field trip location. One of our close partners, investors, customers, is General Electric. They hosted a group of these 20 girls in their San Ramon office. They came to us for a couple of summers as a field trip location and of course the girls loved it. They walk off the elevator there's snacks, there's drinks. We parent programmed with them. It's a really cool experience. And then last summer, we actually took the next step and hosted our own groups so we had a group of 20 young women who were here in our Palo Alto office for seven weeks learning to code and I had the wonderful opportunity to spend time with them several times throughout the summer and I actually commute to the Bay Area, not everyday but I commute to the Bay Area and the days that I was coming up here in part to see the girls, I'd wake up at four in the morning for my flight and I'd be like, "I get to spend time with the girls today," and I saw it. I saw the girls who in the first week were clearly there because their parents made them be there and they're sitting there like this and they've got the same attitude that I had when I was in high school the first three days like I am not doing this and the same people are standing up at the graduation ceremony at the end of the seven weeks saying, "This changed my life." And one of those young women I'm spending a little bit more time with is now a computer science major at Northwestern, early decision. It's just fantastic to see that light up. That's what gets me going. >> Now why high school? I get high school in the sense that they're old enough to take on a summer job like internship, but what is it about that age do you think that is so critical? >> Yeah so that age, I'll be honest with you, I think is almost too late for a lot of girls because we are able to reach, I just mentioned, that there were girls in there whose parents forced them into that. They had already self-selected out. Just like I had when I was in high school. I had self-selected out. I was way too cool to be in computing and so in some ways high school is a little bit too late. However, I think you nailed it, is that there's an opportunity there that they're mature enough that you can do something as immersive as a seven-week program and these girls are tremendous. These girls after a seven-week program are going back to their high schools and being the president of their Girls Who Code after school clubs and teaching them and I was just spending some time, we had a hangout with them recently where they said when their friends are asking, "What are you going to do this summer?" And the girls said, "I have no idea, "but you know what you should do "is you should do Girls Who Code." She said, "That's all I want to do. "I just want to do Girls Who Code all over again." And so I think you're right, I think it's opportunistic in that they're ready, but unfortunately I think it, like I said, it self-selects a lot of people out. I think fundamentally the thing that we need to do to reach the younger grades, the younger students, is it needs to be part of the curriculum. It absolutely 100% needs to be part of primary school curriculum so that they can get hooked and understand what it is before they self-select out because they're self-selecting out based on a perception and the image that they have of what it is, the Silicon Valley show, that's a perception. Sure it's satyr but young people see that and they don't see it as that. It just looks like something where there's a whole bunch of misbehaving men treating women poorly. >> So on that actually Cornelia, what do you make of the really distressing news that we're hearing that's not necessarily new, there has been the Uber bombshell of last week, but what we know about the culture here and maybe why there were so many women and it was almost 50/50 and then we started to see a drastic change and lower numbers of women in computer science and a lot of women just saying, "Ew, I don't want to be part of that. "I don't want that for my career." What do you say to them and what do you say to the men who are not even knowingly discouraging them from that kind of career? >> Oh, I love what you just said, not even knowingly. One of the things that I spend a lot of time talking with folks about every chance I get is implicit bias. I think that there's definitely overt sexism and in the last week we've seen that big in the news and that is a huge problem. I think I've heard statistics of whatever 60% of women have some level of relatively overt sexism, 100% of us get the implicit, the non-overt, and people who are well-meaning saying things, when they say for example, I was just chatting with a young lady a couple of weeks ago. She's a sophomore in college and she was telling me that last summer during her internship, within the first week or two, her boss was talking to her about her career plans moving forward and was already encouraging her to go more into management than into technology. This person was not evil, wasn't trying to keep women out of technology or keep women out of the most technical parts of a technology career, but he really genuinely believed that, "Maybe women are better at that and not so good at this," and it's really just our implicit biases. So I think that's a big part of it. And for the last year or two, I've been talking about implicit bias and I've been talking about the compensating mechanisms so first of all recognizing your implicit biases and then being conscious about them and then consciously combating them. I've become in the last several months, I would say six months, I've become more and more interested in the idea of how do we actually change those implicit biases. >> And this is men and women. It's not just the men here. >> No question because when I've had conversations where I've spoken for example on implicit bias, I've had women come up to me afterward and say, "I signed my son up for a coding camp. "I never even thought about signing up my daughter." >> Rebecca: Oh, that hurts. >> And I was like, "So you're signing her up now, right?" She's like, "Oh yeah, oh yeah, yeah, yeah." And so I think it's really interesting to start thinking about how do we actually get rid of them? It's one thing to recognize them and then fight them, but it's another thing to get rid of them. I think the only way we can get rid of them goes back to the statistics that we talked about early on which is I am surprised when I see a woman technologist. That's just the way our brains work. We categorize things. >> We have an idea in our head of what that person looks like. >> We put things in buckets. We wouldn't be able to function in this world with so many different inputs unless we put things into buckets and we just put things into buckets largely based on statistics. And so I'm becoming increasingly interested in really amplifying the voice of women in technology because when we hear women's voices in technology, women who are up there not talking about what we're talking about today which is the gender imbalance, but talking about the tech itself, then we start to normalize, then we start to re-categorize things in our brains so that we're not surprised when we hear a woman talking about something deeply technical or somebody who's doing particle physics or something like that, we're not surprised anymore and say, "Wow she's a rocket scientist," it's normal. That's what I'm interested in doing is getting that to be the norm, not the exception. I think the first step what I would say to people, what I do say to men and women across the industry is first of all recognize it and then let's see what we can do to change it. >> Cornelia Davis, thank you so much. That's good advice, that's good advice. And we'll be right back with theCUBE's coverage of Women Transforming Technology here at VMware. (modern techno music)

>> Welcome back to the Cuban Peterborough's chief research officer of Silicon Angle and general manager of Wicked Bond. We're as part of our continuing coverage of the arse a show. We have a great guest Z scaler amid sin. Ha! Welcome to the Cube. >> Thank you for having me here. It's a pleasure to be here. >> So, um, it what exactly does Z scaler? D'oh >> Z's killer is in the business of providing the entire security stack as a service for large enterprises. We sit in between enterprise users and the Internet and various destinations they want to goto, and we want to make sure that they have a fast, nimble Internet experience without compromising any security. >> So if I can interpret what that means, that means that as Maur companies are trying to serve their employees that Air Mobile or customers who aren't part of their corporate network they're moving more. That communication in the Cloud Z scale is making it possible for them to get the same quality of security on that communication in the cloud is he would get on premise. >> Absolutely. If you look at some of the big business transformations that are happening, work lords for enterprises are moving to the cloud. For example, enterprises are adopting Office 3 65 instead, off traditional exchange based email and on your desktop applications. They might be adopting sales force for CR M Net suite for finance box for storage. So as these workloads are moving to the cloud and employees are becoming more and more mobile, you know they might be at a coffee shop. They might be on an iPad. Um, and they might be anywhere in the world. That begs the basic security question. Where should that enterprise DMC the security stack be sitting back in the day? Enterprises had a hub and spokes model, right? They might have 50 branch offices across the world. A few mobile workers, all of them, came back over private networks to a central hub, and that hub was where racks and racks of security appliances were deployed. Maybe they started off with a firewall. Later on, they added a proxy. You are l filtering some d e l P er down the road. People realized that you need to inspect us to sell. So they added some SSL offload devices. Someone said, Hey, we need to do some sand boxing for behavioral analysis. People started adding sandboxes. And so, over time the D. M. Z got cluttered and complicated and fast forward to Today. Users have become mobile. Workloads have moved to the cloud. So if I'm sitting in a San Francisco office on my laptop trying to do my regular work, my email is in the cloud. My my court applications are sitting in the cloud. Why should I have to vpn back to my headquarters in Cincinnati over a private network, you know, incurring all the Leighton see and the delays just so that I can get inspected by some legacy appliances that are sitting in that DMC, right? So we looked at that network transformation on We started this journey at Ze scale or eight years ago, and we said, Look, if users are going to be mobile and workloads are going to be in the cloud, the entire security stack should be as close as possible to where the users are. In that example, I described, I'm sitting here. I'm going to Salesforce. We're probably going to the same data center in San Francisco. Shouldn't my entire security stag be available right where I am, um, and my administrators should have full visibility, full control from a single pane of glass. I get a fast, nimble user experience. The enterprise doesn't have to compromise in any security, and that's sort of the vision that we have executing towards. >> But it's not just for some of the newer applications or some of the newer were close. We're also seeing businesses acknowledge that the least secure member of their community has an impact on overall security. So the whole concept of even the legacy has to become increasingly a part of this broad story. So if anybody accesses anything from anywhere through the cloud that those other workloads increasing, they're gonna have to come under the scrutiny of a cloud based security option. >> Absolutely. I mean, that's a brilliant point, Peter. >> I >> think of >> it this way. Despite all those security appliances that have been deployed over time, they're still security breach is happening. And why is that? That is because users are the weakest link, right? If I'm a mobile work user, I'm sitting in a branch office. It's just painful for me to go back to those headquarter facilities just for additional scanning so two things happen either I have a painful user experience. What? I bypassed security, right? Um, and more and more of the attacks that we see leverage the user as the weakest link. I send you a phishing email. It looks like it came from HR. It has a excel sheet attached to it to update some information. But, you know, inside is lurking a macro, right? You open it. It is from a squatter domain that looks very similar to the company you work for. You click on it and your machine is infected. And then that leads to further malware being downloaded, data being expatriated out. So the Z scaler solution is very, very simple. Conceptually, we want to sit between users and the destinations they goto all across the world. And we built this network of 100 data centers. Why? Because you cannot travel faster than the speed of light. So if you're in San Francisco, you better go through our San Francisco facility. All your policies will show up here. All the latest and greatest security protections will be available. We serve 5000 large enterprises. So if we discover a new security threat because of an employee from, let's say, a General Electric. Then someone from United Airlines automatically gets protection simply because the cloud is live all the time. You're not waiting for your security boxes to get, you know, the weekly patch updates for new malware indicators and so on. Right, So, um, you get your stack right where you are. It's always up to date. User experience is not compromised. Your security administrators get a global view off things. And one >> of the >> things that that I that we haven't talked about here it is the dramatic cost savings that this sort of network transformation brings for enterprises. To put that in perspective, let's say you're a Fortune 100 organization with 100,000 employees worldwide in that, huh? Been spoke model. You are forcing all those workloads to come toe a few choke points, right? That is coming over. Very expensive. NPLs circuits private circuits from service providers. You're double trombone in traffic, back and forth. You know, you and I are in a branch. We might be on. Ah, Skype session. Ah, Google Hangout session. All our traffic goes to H Q. Goes to the cloud comeback comes back to h. Q comes back to you, there's this is too much back and forth, and you're paying for those expensive circuits and getting a poor user experience. Wouldn't it be great if you and I could go straight to the Internet? And that can only be enabled if we can provide that pervasive security stack wherever you are? And for that, we built this network of 100 data centers worldwide. Always live, always up to date you. You get routed to the closest the scaler facility. All your policy show up. They're automatically and you get the latest and greatest protection. >> So it seems as though you end up with three basic benefits. One is you get the cost benefit of being able to, uh, have being able to leverage a broader network of talent, skills and resources You reduce. Your risk is not the least of which is that the cost and the challenges configuring a whole bunch of appliances has not gotten any easier over the last. No, it hasn't cheaters. And so not only do you have user error, but you also Administrator Erin, absolutely benign, but nonetheless it's there, and then finally and this is what I want to talk about. Increasingly, the clot is acknowledged as the way that companies are going to improve their portfolio through digital assets. Absolutely. Which means new opportunities, new competition, new ways of improving customer experience. But security has become the function of no within a lot of organizations. Absolutely. So How does how does AE scaler facilitate the introduction of new business capabilities that can attack these opportunities in a much more timely way by reducing doesn't reduce some of those some of those traditional security constraints. >> Absolutely right, and we call it the Department of No right. We've talked to most people in the industry. They view their I t folks there, security forces, the department of Know Why? Because there's this big push from users to adopt newer, nimble, faster cloud based ah solutions that that improved productivity. But often I t comes in the way. No, If you look at what Izzy's killer is doing, it's trying to transform the adoption of these Cloud service. Is that do improve business productivity? In fact, there is no debate now because there are many, many industries that ever doubt adopted a cloud first strategy. Well, that means is, as they think of the network and their security, they want to make sure that cloud is front and center. Words E scaler does is it enables that cloud for a strategy without any security compromise. I'll give you some specific examples. Eight out of 10 c I ose that we talk to our thinking about office 3 65 or they have already deployed it right. One of the first challenge is that happens when you try to adopt office. 3 65 is that your legacy network and security infrastructure starts to come crumble. Very simple things happen. You have your laptop. Suddenly, that laptop has many, many persistent SSL connections to the clothes. Because exchange is moved to the cloudy directory, service is are moving to the cloud. If you have a small branch office with 2000 users, each of them having 30 40 persistent connections to the cloud will your edge firewall chokes. Why? Because it cannot maintain so many active ports at the same time, we talked about the double trombone ing of traffic back and forth. If you try to not go direct to the Internet but force everyone to go through a couple of hubs. So you pay for all the excessive band with your traditional network infrastructure, and your security infrastructure might need a forklift upgrades. So a cloud transformation project quickly becomes a network in a security transformation project. And this is where you nosy scaler helps tremendously because we were born and bred in the cloud. Many of these traditional limitations that you have with appliance based security or networking, you know, in the traditional sense don't exist for the scaler, right? We can enable your branch officers to go directly to the cloud. In fact, we've started doing some very clever things. For example, we peer with Microsoft in about 20 sites worldwide. So what that means is, when you come to the scaler for security, there's a very high likelihood that Microsoft has a presence in the same data center. We might be one or two or three millisecond hops away because we're in the same equinox facility in New York or San Jose. And so not only are you getting your full security stack where you are, you're getting the superfast peered connections to the end Cloud service is that you want to goto. You don't have to work. Worry about you know your edge Firewalls not keeping up. You don't have to worry about a massive 30 40% increase in back hole costs because you were now shipping all this extra traffic to those couple of hubs. And more importantly, you know, you've adopted these transformative technologies on your users don't have to complain about how slow they are because you know, most of the millennials hitting the workforce. I used to a very fast, nimble experience on their mobile phones with consumer APS. And then they come into the enterprise and they quickly realize that, well, this is all cumbersome and old and legacy stuff >> in me s. So let's talk a little bit about Let's talk a bit about this notion of security being everywhere and increasingly is removed to a digital business or digital orientation. With digital assets being the basis for the value proposition, which is certainly happening on a broad scale right now, it means it's security going back to the idea of security being department. No security has to move from an orientation of limiting access to appropriately sharing. Security becomes the basis for defining the digital brand. So talk to us a little bit about how the how you look out, how you see the world, that you think security's gonna be playing in ultimately defining this notion of digital brand digital perimeters from a not a iittie standpoint. But from a business value standpoint, >> absolutely. I would love to talk about that. So Izzy's killer Our cloud today sees about 30,000,000,000 transactions a day from about 5000 enterprises. So we have a very, very good pulse on what is happening in large enterprises, from from a cloud at perspective or just what users are doing on the Internet. So here are some of the things that we see. Number one. We see that about 50 60% of the threats are coming inside SSL, so it's very important to inspect SSL. The second thing that we observe is without visibility. It is very different, very difficult for your security guys to come up with a Chris policy, right? If you cannot see what is happening inside an SSL connection, how are you going to have a date? A leakage policy, right? Maybe your policy is no P I information should leak out. No source code should leak out. How can you make sure that an engineer is not dropping something in this folder, which is sinking to Google Drive or drop box in an in an SSL tano, Right. How do you prioritize mission Critical business applications like office 3 65 over streaming media, Right. So for step two, crafting good policy is 100% real time visibility. And that's what happens when you adopt the Siskel a network. You can see what any user is doing anywhere in the world within seconds. And once you have that kind of visibility, you can start formulating policies, both security and otherwise that strike a good balance between business productivity that you want to achieve without compromising security. >> That's the policy's been 10 more net. You can also end that decisions. >> Yes, right. So, for example, you can you can have a more relaxed social media policy, right? You can say Well, you know, everyone is allowed access, but they can. Maybe streaming media is restricted to one hour a day. You know, after hours, or you can say, I want to adopt um, storage applications in the clothes here are some sanctioned APS These other raps were not going to allow right. You can do policies by users, by locations by departments, right? And once you have the visibility, you can. You can be very, very precise and say, Well, boxes, my sanction story, Jap other APS are not allowed right and hear other things that a particular group of users can do on box. Or they cannot do because we were seeing every transaction between the user on going to the destination and as a result, begin, you know, we can enable the enterprise administrator to come up with very, very specific policies that are tailored for that. >> You said something really interesting. I'm gonna ask you one more question, but I'm gonna make a common here. And that common is that the power of digital technology is that it can be configured and copied and changed, and it's very mutable. It's very plastic, but at the end of the day it has to be precise, and I've never heard anybody talk about the idea of precise and security, and I think it's a very, very powerful concept. But what are what's What's the scale are talking about in our say this year. >> Well, we're going to talk about a bunch of very interesting things. First, we'll talk about the scale of private access. This is a new offering on the scale of platform. We believe that VP ends have become irrelevant because of all the discussions we just had, um, Enterprises are treating their Internet as though it was the Internet, right? You know, sort of a zero trust model. They're moving the crown jewel applications to either private cloud offerings are, you know, sort of restricting that in a very micro segmented way. And the question is, how do you access those applications? Right? And the sea skill immortal is very straightforward. You have a pervasive cloud users authenticate to the cloud and based on policies, we can allow them to go to the Internet to sites that have been sanctioned and allowed. We make sure nothing good is leaking out. Nothing bad is coming in, and that same cloud model can be leveraged for private access to crown jewel applications that traditionally would have required a full blown vpn right. And the difference between a VPN and the skill of private access is VP ends basically give you full network access keys to the kingdom, right? Whether it's a contractor with, it's an employee just so that you could access, you know, Internet application. You allow full network access, and we're just gonna getting rid of that whole notion. That's one thing we're gonna stroke ISS lots of cloud white analytics, As I mentioned, you know, we process 30,000,000,000 transactions a day. To put that in perspective, Salesforce reports about four and 1 30,000,000,000 4 1/2 to 5,000,000,000 transactions. They're about three and 1/2 1,000,000,000 Google searches done daily, right? So it is truly a tin Internet scale. We're blocking over 100,000,000 threats every day for, ah, for all our enterprise user. So we have a very good pulse on you know what's what's an average enterprise user doing? And you're going to see some interesting cloud? Wait, Analytics. Just where we talk about a one of the top prevalent Claude APs, what are the top threats? You know, by vertical buy by geography, ese? And then, you know, we as a platform has emerged. We started off as a as a sort of a proxy in the cloud, and we've added sand boxing capabilities. Firewall capabilities, you know, in our overall vision, as I said, is to be that entire security stack that sits in your inbound and outbound gateway in that DMC as a pure service. So everything from firewall at layer three to a proxy at Layer seven, everything from inline navy scanning right to full sand. Boxing everything from DLP to cloud application control. Right? And all of that is possible because, you know, we have this very scalable architecture that allows you to to do sort of single scan multiple action right in that appliance model that I describe. What ends up happening is that you have many bumps in the wire. One of the examples we use is if you wanted to build a utility company, you don't start off with small portable generators and stack them in a warehouse, right? That's inefficient. It requires individual maintenance. It doesn't scale properly. Imagine if you build a turbine and ah, and then started your utility company. You can scale better. You can do things that traditional appliance vendors cannot think about. So we build this scalable, elastic security platform, and on that platform it's very easy for us to add. You know, here's a firewall. Here's a sandbox. And what does it mean for end users? You know, you don't need to deploy new boxes. You just go and say, I want to add sand boxing capabilities or I want to add private access or I want to add DLP. And it is as simple as enabling askew, which is what a cloud service offering should be. >> Right. So we're >> hardly know software. >> So we're talking about we're talking about lower cost, less likelihood of human error, which improves the quality, security, greater plasticity and ultimately, better experience, especially for your non employees. Absolutely. All right, so we are closing up this particular moment I want Thank you very much for coming down to our Pallotta studio is part of our coverage on Peter Boris. And we've been talking to the scanner amidst, huh? Thank you very much. And back to Dio Cube.

(electronic dance music) >> Host: Live from San Francisco. It's theCUBE. Covering Oracle OpenWorld 2016. Brought to you by Oracle. Now, here's your hosts, John Furrier and Peter Burris. >> Hey, welcome back everyone. We are here live in San Francisco for Oracle OpenWorld 2016. This is theCUBE. SiliconANGLE Media's flaghship program. We got out to the events and extract the signal from the noise. I'm John Furrier the co-CEO of SiliconANGLE Media with Peter Burris, my co-host, who's the head of research for SiliconANGLE Media as well as the general manager of Wikibon Research. Our next guest is Dave Donatelli, Executive Vice President of Cloud and Converged Systems and Infrastructure at Oracle. Cube alumni always coming on. Great to see you. Thanks for spending really valuable time to come and share your insights with us. >> Great to see you guys again. It's always a pleasure. >> So you did the keynote today, obviously the forces in the industry around Cloud, Oracle's got the whole story now. They got the IaaS V2, they're calling it. And now you have up and down the stack PasS and Saas, and under the covers, under the hood is the power hardware. >> Dave: Of the infrastructure, yeah. >> Very disruptive and we chatted and we wrote a story at SiliconANGLE, also on Forbes, about the destruction of the existing incumbents. So with that in mind, how did the keynote go from your perspective? What was your key themes and how does that relate to some of the disruption in the landscape of the industry? >> Okay, well, as a self-writer, I'd say the keynote went very well, but what I really talked about was Oracle offers people three deployment models. And I gave 'em kind of five journey to take to the Cloud. The three models are public Cloud, broad-based public Cloud. Second thing is traditional enterprise, which business we've been in for so long. And then a new category is what we call Cloud a customer. Taking our public Cloud and making that available to customers. And then the second thing I did in the keynote is talk about five journeys people could take to our public Cloud and it's everything from optimizing what they currently have in their legacy environment, to running hybrid Cloud, to running this Cloud a customer, to running private Cloud, and the fifth one is just, end what you're doing in the current way and move all public Cloud. So in the five journeys, just to drill down on that. It's five different paths the customer could take. >> Dave: Correct, all from a customer's perspective. >> From their current position to a Cloud endgame, if you will. >> Dave: Yes. >> And which one you think is the most dominant right now in terms of your view because obviously we'll go though those, but of the ones, beyond on-prem, which ones has the most relevance today in terms of customers that you hear from. Why I'd say two things, what I see and what I've seen the last year is the acceleration of movement to the public Cloud in literally since the start of the year has been massive. And what's really changed about a lot of it's coming top down. So you see CEOs, board of directors, CFOs saying we're going to go to the Cloud, even some companies are giving their IT departments specific requirements. You'll have 40% of our applications in the Cloud by 2000. So big acceleration there and in saying that what most customers are doing is something in the middle. They have their legacy that they've always been running. we look at it app by app by app. What's the most likely to transform to the Cloud? Which ones are probably just going to go away? Which one should we just redesigned and build net new in the Cloud. And so that means to me that hybrid is really, you know, the one that we see most often. People are running on-premise, they're running in the Cloud. They'll have a mix for some time until the on-premise continues to go away. >> What's the concept we heard from Chuck Hollis yesterday around this notion of Cloud quotas. He's seeing customers being kind of mandated to get to the Cloud, almost like a quota. Hey, where are you with your with your Cloud migration? So there's pressure certainly coming in but you introduced Cloud insurance. Is that not actually insurance, but as a concept, just explain what you meant by that. >> Sure. So what we mean by that is this, is that, as I, as we just talked about most enterprises, if you look at most of data out there says only 5% of applications have moved to the Cloud, so far. So that means a lot are still running in their data centers. But now you're going to go to your boss and you're going to say, "Hey you know I need to buy some new infrastructure.". And if you're a regular company, that's going to take three to five years to depreciate. So you go to your boss and say, "Hey, give me $10 million, I got this great idea. I'm going to put this new infrastructure in.". Well, what if two years from now your boss comes in and says, "Guess what? We now we need to move to the public Cloud.". With traditional infrastructure or with infrastructure designed by companies who don't have a public Cloud, you now have a boat anchor, right? I run big businesses myself and the last thing you want is equipment on your books depreciating that has no technical value. What we mean by Cloud insurance, is that everything we sell customers on-premise also has a public Cloud equivalency. Think of Exadata. You can use Exadata on-premise. We have an Exadata Cloud service you can subscribe to in the Cloud. So if you buy an Exadata on-premise today and they say we want to start moving to Cloud. You can say, "Great, I'll do things like test EV in the Cloud with my equivalent Exadata service.". They're fully compatible. It's got the same management. It's one push button to move data from on-premise to the public Cloud. No one else can do that. >> Peter: So you're really selling them a Cloud option. Whatever you buy you are also buying a Cloud option. >> What I say is I'm giving them assurance and insurance. The assurance is you're buying something today that you know will have a useful life going forward in the go forward architecture. >> Peter: And if you want to exercise hat option today, you can do so, if you want exercising three years you can do so. >> Exactly. >> No financial penalty to you. >> Exactly. And what most competitors are saying is hey, by the way you always did it and guess what? You don't have that option. >> Peter: It's your asset. So one of the things, I love the idea of the five paths, but paths are going to be influenced by workloads. So as you think about the characteristics of workloads, not big companies, small company, regional, those are always going to be important. Sophistication, maturity of the shop. But, as you think about workloads, going back to John's question, what types of workloads do you see coming in first? So for example, we're seeing a lot of on-premise, big data happening, but not as fast as it might because of complexity. We're starting to see more of that move into options that are more simply packaged, easier to use like in the Cloud. What kind of workloads do you think are going to pull customers forward first? >> Dave: Sure. Well, first remember we play in Saas, PasS, and infrastructure. And what we've seen if you look at our financials, is huge growth in SaaS and that's where people are saying, I am taking, you know, with GE here, as an example, Ge is taking their ERP, big global company, they're putting that in the public Cloud. HSBC was here, same story, big financial institution. They're putting that in the Cloud first. And the reason why they're doing it, is they think it gives you more flexibility, makes them more efficient, saves them money. Then, which really changed, and what we've evolved to, is with our new infrastructure Cloud now we can do anything. This is to your question. Anything that runs on an x86 server or spark based server, whether it's an Oracle application or not, you can either migrate it and run it in our Cloud. You can, you know, reimagine it using using our PaaS to redesign it, move it to the Cloud, it's everything. And we're seeing increasing rates of people walking through by app by app in their environment and doing just what we've said. What stays, what moves, what do we transform in the process? >> You seen a lot of the the movie at EMC, certainly your history, your career at EMC and then HP. Lot of industry had changed while you're, you know, in those shops, now here at Oracle. So I got to ask you now with the Oracle advantage and you guys are pushing from the silicon to the app, however, I forget how they word it, but it's silicon to the app, the end-to-end kind of thing. What's different from a design standpoint, from a technical, as the product development teams build it, what's the unique thing that's changed? And how's that render itself to impacting the customer? >> Dave: Okay, that's a great question. So let me give you the customer benefit first and I'll tell you why it occurs. what I said today from stage is that to run our, I'll use an examples of our software. To run our software there's no better place on earth than our infrastructure, and compared to their most likely alternative which is their self build, them buying an x86 server, them buying their own networking, them buying storage. We give people better performance, better end-user experience, easier to manage and most importantly it costs them less money. >> John: So knocking down Oracle on Oracle, boom. That's a baseline. >> Less cost versus you going to buy a server online at Dell and trying to put it together yourself. >> I buy that. >> Dave: The way we do it, is the fact that we have insights which we have designed, all the way into our software as well as into our products. So depending which product you're talking about, for instance in Spark, we embedded a silicon itself. Accelerators for things like encryption, for deencryption, for the ability to compress, to decompress. All kinds of things that matter and speed. At the same time we make a lot of changes to our software itself to make that run better with our Hardware. It's RIP. It takes a lot of engineering to do that, but simply put if you don't have the software stack, you know if you're someone who just builds hardware, you can't see the software, you can't make those changes. >> John: Well, you have the advantage. Obviously, you have have software that Oracle writes, you have systems that are engineered for Oracle software. Clear advantage, so you're saying unequivocally-- >> Dave: From a technical-- >> You blow everyone away. >> Dave: From a pure technical perspective, it is an unfair fight. We will win every time. >> John: Okay, so i buy that, so that, you win those rounds. Curveball is multi-vendor. Now we're into a multi-vendor because a lot of people have that technical debt now on the books, if you will, I don't know if that's the right term, technical debt, but they have legacy. It might be Dell EMC, it might be HP and other stuff. How do how do those shops deal with this Oracle infrastructure Cloud and non Oracle software. >> Okay, so two ways. So if you look at an on-premise, we make products that run both Oracle software, engineered systems to run both Oracle software, non Oracle software in the same machine. So you get all the accrued benefits we talked about but you can also host your applications that might not necessarily be Oracle, with us. In the Cloud itself, i think you heard, you know I thought Larry gave an excellent presentation yesterday and very clearly walking through what we do that's different than alternatives. And as we said, >> John: He was very aggressive on Amazon. >> Dave: But I thought he was very, I thought he was very fair in how you did it, right. He walked through it just the facts. This is what they do, this is what we do, this is why it's technically different. He didn't just come out and say hey, we're better than amazon he gave specific reasons why. >> John: He did that and he did that, he did both. >> But if you look at it, so even just running a generic app, that's non-Oracle, on our infrastructure as a service, what we said very clearly is, we have an infrastructure by the way it is architected, that has less noise, meaning so you get less performance disruption, so it runs faster. It's built with the newer hardware and at the same time in doing so because of our architecture we can offer that to people at a lower price than they'd otherwise get. And again I think those are very straightforward, very well articulated points to show the value and you know that opens up the whole world to us. As you know the x86 market is almost a $40 billion market on-premise. What we're saying now at Oracle is, we can do a better job for you in the public Cloud running any of those workloads. >> That's right now. I think the other thing that came out, we've talked about it here, is that the stream of innovation that's going to unload itself on the industry over the next few years, someone still has to do the integration of all of these different piece parts. They're going to be improved upon and that integration cost is real, and so you can look at that from a CIOs perspective, they can look at and say do I want to put my time into the integration, do I want to put my time into the application that's going to have a differential effect on my business. So you guys seem to be coming pretty strongly on we've got the baseline we need to do the, we've got the stuff that we need to bring the innovation in an integrated way into our packaging. >> Dave: That's correct and I think very well said. I believe we are the easiest company to work with, in bringing people from, in essence, their old architecture to the new. And that is because we've already done that integration work. We offer those architectures on both sides of the equation, current on-premise into the public Cloud and give you one management software structure to manage both. Anybody else is only going to work with you on one extreme or another. It's either, hey only do Cloud or only do on-prem. How you work with the other one, you as a customer stuck with that burden to figure out. Dave, I know you got to go to another meeting, but I want to get the final question to you to elaborate on. What you're most proud of now in your tenure at Oracle. Some things that have worked for you in the organization product-wise, successes you've had. You want to highlight a few? And what's your priorities going forward? You're now running the Cloud group as well as Converged Infrastructure kind of coming together. What are you most proud of? what is, could be people not things, like ZDLRA, I know is doing really, Juan Loaiza was saying it's a smashing success and we're not hearing anything about that. We heard about it yesterday, but so what are you most proud of and then what's your priorities going forward? >> What I'm most proud of about being at Oracle is we're an organization investing for our customers' future. So we're spending $5.2 billion this year on R and D and it's all about bringing out these products that fit the future for our customers and protecting their investments along the way. I'm very proud to be part of a company, because as you know in these big transitions, companies don't make it. Think of Deck, right? They're a leader, didn't make it through to the new transition. And we're one of these companies that's leading the new transition even though we also participated in the prior architecture. I think from a product perspective, I would say ZDLRA is a great one you brought up. It stands for Zero Data Loss Recovery Appliance. It is designed by our database engineers to fully backup and recover, as it says, with zero data loss, our database. And we've had a number of customers here, we had customers of the keynote today, very major enterprises at the keynote today was General Electric, who talked about how it enables them now to sleep. They don't get woken up at three in the morning. It gives some certainty in terms of how they recover. And most importantly, it saves them money. >> And you're in the hardware business, but you're not in the box business. You're actually have the software, it's again software enabled. Congratulations, I know you're attracting a lot of good talent as well. They did a great job and it's been fun to watch your success at Oracle and we're proud to cover you guys. We have some points we would disagree with you. If we had more time we can go into little detail, but thanks for spending the time and sharing on theCUBE. >> All right, a pleasure. Always great to see you guys. Live in San Francisco for Oracle OpenWorld. This is theCUBE. I'm John Furrier, Peter Burris, we'll be back with more after this short break.