>>From the Bellagio hotel in Las Vegas. It's the cube covering UI path forward for brought to you by UI path. >>Good morning. Welcome to the cubes coverage of UI path forward for day two. Live from the Bellagio in Las Vegas. I'm Lisa Martin with Dave Velante, Dave. We had a great action packed day yesterday. We're going to have another action packed day today. We've got the CEO coming on. We've got customers coming on, but there's been a lot in the news last 24 hours. Facebook, what are your thoughts? >>Yeah, so wall street journal today, headline Facebook hearing fuels call for rain in on big tech. All right, everybody's going after big tech. Uh, for those of you who missed it, 60 minutes had a, uh, an interview with the whistleblower. Her name is, uh, Francis Haugen. She's very credible, just a little background. I'll give you my take. I mean, she was hired to help set Facebook straight and protect privacy of individuals, of children. And I really feel like, again, she, she didn't come across as, as bitter or antagonistic, but, but I feel as though she feels betrayed, right, I think she was hired to do a job. They lured her in to say, Hey, this is again, just my take to say, Hey, we want your help in earnest to protect the privacy of our users, our citizens, et cetera. And I think she feels betrayed because she's now saying, listen, this is not cool. >>You hired us to do a job. We in earnest, went in and tried to solve this problem. And you guys kind of ignored it and you put profit ahead of safety. And I think that is the fundamental crux of this. Now she made a number of really good points in her hearing yesterday and I'll, and we'll try to summarize, I mean, there's a lot of putting advertising revenue ahead of children's safety and, and, and others. The examples they're using are during the 2020 election, they shut down any sort of negative conversations. They would be really proactive about that, but after the election, they turned it back on and you know, we all know what happened on January 6th. So there's sort of, you know, the senators are trying that night. Um, the second thing is she talked about Facebook as a wall garden, and she made the point yesterday at the congressional hearings that Google actually, you can data scientists, anybody can go download all the data that Google has on you. >>You and I can do that. Right? There's that website that we've gone to and you look at all the data Google has and you kind of freak out. Yeah, you can't do that with Facebook, right? It's all hidden. So it's kind of this big black box. I will say this it's interesting. The calls for breaking up big tech, Bernie Sanders tweeted something out yesterday said that, uh, mark Zuckerberg was worth, I don't know. I think 9 billion in 2007 or eight or nine, whatever it was. And he's worth 122 billion today, which of course is mostly tied up in Facebook stock, but still he's got incredible wealth. And then Bernie went on his red it's time to break up big tech. It's time to get people to pay their fair share, et cetera. I'm intrigued that the senators don't have as much vigilance around other industries, whether it's big pharma, food companies addicting children to sugar and the like, but that doesn't let Facebook. >>No, it doesn't, but, but you ha you bring up a good point. You and I were chatting about this yesterday. What the whistleblower is identifying is scary. It's dangerous. And the vast majority, I think of its users, don't understand it. They're not aware of it. Um, and why is big tech being maybe singled out and use as an example here, when, to your point, you know, the addiction to sugar and other things are, uh, have very serious implications. Why is big tech being singled out here as the poster child for what's going wrong? >>Well, and they're comparing it to big tobacco, which is the last thing you want to be compared to as big tobacco. But the, but the, but the comparison is, is valid in that her claim, the whistleblower's claim was that Facebook had data and research that it knew, it knows it's hurting, you know, you know, young people. And so what did it do? It created, you know, Instagram for kids, uh, or it had 600,000. She had another really interesting comment or maybe one of the senators did. Facebook said, look, we scan our records and you know, kids lie. And we, uh, we kicked 600,000 kids off the network recently who were underaged. And the point was made if you have 600,000 people on your network that are underage, you have to go kill. That's a problem. Right? So now the flip side of this, again, trying to be balanced is Facebook shut down Donald Trump and his nonsense, uh, and basically took him off the platform. >>They kind of thwarted all the hunter Biden stuff, right. So, you know, they did do some, they did. It's not like they didn't take any actions. Uh, and now they're up, you know, in front of the senators getting hammered. But I think the Zuckerberg brings a lot of this on himself because he put out an Instagram he's on his yacht, he's drinking, he's having fun. It's like he doesn't care. And he, you know, who knows, he probably doesn't. She also made the point that he owns an inordinate percentage and controls an inordinate percentage of the stock, I think 52% or 53%. So he can kind of do what he wants. And I guess, you know, coming back to public policy, there's a lot of narrative of, I get the billionaires and I get that, you know, the Mo I'm all for billionaires paying more taxes. >>But if you look at the tax policies that's coming out of the house of representatives, it really doesn't hit the billionaires the way billionaires can. We kind of know the way that they protect their wealth is they don't sell and they take out low interest loans that aren't taxed. And so if you look at the tax policies that are coming out, they're really not going after the billionaires. It's a lot of rhetoric. I like to deal in facts. And so I think, I think there's, there's a lot of disingenuous discourse going on right now at the same time, you know, Facebook, they gotta, they gotta figure it out. They have to really do a better job and become more transparent, or they are going to get broken up. And I think that's a big risk to the, to their franchise and maybe Zuckerberg doesn't care. Maybe he just wants to give it a, give it to the government, say, Hey, are you guys are on? It >>Happens. What do you think would happen with Amazon, Google, apple, some of the other big giants. >>That's a really good question. And I think if you look at the history of the us government, in terms of ant anti monopolistic practices, it spent decade plus going after IBM, you know, at the end of the day and at the same thing with Microsoft at the end of the day, and those are pretty big, you know, high profiles. And then you look at, at T and T the breakup of at T and T if you take IBM, IBM and Microsoft, they were slowed down by the U S government. No question I've in particular had his hands shackled, but it was ultimately their own mistakes that caused their problems. IBM misunderstood. The PC market. It gave its monopoly to Intel and Microsoft, Microsoft for its part. You know, it was hugging windows. They tried to do the windows phone to try to jam windows into everything. >>And then, you know, open source came and, you know, the world woke up and said, oh, there's this internet that's built on Linux. You know, that kind of moderated by at T and T was broken up. And then they were the baby bells, and then they all got absorbed. And now you have, you know, all this big, giant telcos and cable companies. So the history of the U S government in terms of adjudicating monopolistic behavior has not been great at the same time. You know, if companies are breaking the law, they have to be held accountable. I think in the case of Amazon and Google and apple, they, a lot of lawyers and they'll fight it. You look at what China's doing. They just cut right to the chase and they say, don't go to the, they don't litigate. They just say, this is what we're doing. >>Big tech, you can't do a, B and C. We're going to fund a bunch of small startups to go compete. So that's an interesting model. I was talking to John Chambers about this and he said, you know, he was flat out that the Western way is the right way. And I believe in, you know, democracy and so forth. But I think if, to answer your question, I think they'll, they'll slow it down in courts. And I think at some point somebody's going to figure out a way to disrupt these big companies. They always do, you know, >>You're right. They always do >>Right. I mean, you know, the other thing John Chambers points out is that he used to be at 1 28, working for Wang. There is no guarantee that the past is prologue that because you succeeded in the past, you're going to succeed in the future. So, so that's kind of the Facebook break up big tech. I'd like to see a little bit more discussion around, you know, things like food companies and the, like >>You bring up a great point about that, that they're equally harmful in different ways. And yet they're not getting the visibility that a Facebook is getting. And maybe that's because of the number of users that it has worldwide and how many people depend on it for communication, especially in the last 18 months when it was one of the few channels we had to connect and engage >>Well. And, and the whistleblower's point, Facebook puts out this marketing narrative that, Hey, look at all this good we're doing in reality. They're all about the, the, the advertising profits. But you know, I'm not sure what laws they're breaking. They're a public company. They're, they're, they have a responsibility to shareholders. So that's, you know, to be continued. The other big news is, and the headline is banks challenge, apple pay over fees for transactions, right? In 2014, when apple came up with apple pay, all the banks lined up, oh, they had FOMO. They didn't want to miss out on this. So they signed up. Now. They don't like the fact that they have to pay apple fees. They don't like the fact that apple introduced its own credit card. They don't like the fact that they have to pay fees on monthly recurring charges on your, you know, your iTunes. >>And so we talked about this and we talk about it a lot on the cube is that, that in, in, in, in his book, seeing digital David, Michelle, or the author talked about Silicon valley broadly defined. So he's including Seattle, Microsoft, but more so Amazon, et cetera, has a dual disruption agenda. They're not only trying to disrupt horizontally the technology industry, but they're also disrupting industry. We talked about this yesterday, apple and finances. The example here, Amazon, who was a bookseller got into cloud and is in grocery and is doing content. And you're seeing these a large companies, traverse industry value chains, which have historically been very insulated right from that type of competition. And it's all because of digital and data. So it's a very, pretty fascinating trends going on. >>Well, from a financial services perspective, we've been seeing the unbundling of the banks for a while. You know, the big guys with B of A's, those folks are clearly concerned about the smaller, well, I'll say the smaller FinTech disruptors for one, but, but the non FinTech folks, the apples of the world, for example, who aren't in that industry who are now to your point, disrupting horizontally and now going after individual specific industries, ultimately I think as consumers we want, whatever is going to make our lives easier. Um, do you ever, ever, I always kind of scratch my nose when somebody doesn't take apple pay, I'm like, you don't take apple pay so easy. It's so easy to make this easy for me. >>Yeah. Yeah. So it's, it's going to be really interesting to see how this plays out. I, I do think, um, you know, it begs the question when will banks or Willbanks lose control of the payment systems. They seem to be doing that already with, with alternative forms of payment, uh, whether it's PayPal or Stripe or apple pay. And then crypto is, uh, with, with, with decentralized finance is a whole nother topic of disruption and innovation, >>Right? Well, these big legacy institutions, these organizations, and we've spoke with some of them yesterday, we're going to be speaking with some of them today. They need to be able to be agile, to transform. They have to have the right culture in order to do that. That's the big one. They have to be willing. I think an open to partner with the broader ecosystem to unlock more opportunities. If they want to be competitive and retain the trust of the clients that they've had for so long. >>I think every industry has a digital disruption scenario. We used to always use the, don't get Uber prized example Uber's coming on today, right? And, and there isn't an industry, whether it's manufacturing or retail or healthcare or, or government that isn't going to get disrupted by digital. And I think the unique piece of this is it's it's data, data, putting data at the core. That's what the big internet giants have done. That's what we're hearing. All these incumbents try to do is to put data. We heard this from Coca-Cola yesterday, we're putting data at the core of our company and what we're enabling through automation and other activities, uh, digital, you know, a company. And so, you know, can these, can these giants, these hundred plus year old giants compete? I think they can because they don't have to invent AI. They can work with companies like UI path and embed AI into their business and focused on, on what they do best. Now, of course, Google and Amazon and Facebook and Microsoft there may be going to have the best AI in the world. But I think ultimately all these companies are on a giant collision course, but the market is so huge that I think there's a lot of, >>There's a tremendous amount of opportunity. I think one of the things that was exciting about talking to one, the female CIO of Coca-Cola yesterday, a hundred plus old organization, and she came in with a very transformative, very different mindset. So when you see these, I always appreciate when I say legacy institutions like Coca-Cola or Merck who was on yesterday, blue cross blue shield who's on today, embracing change, cultural change going. We can't do things the way we used to do, because there are competitors in that review mirror who are smaller, they're more nimble, they're faster. They're going to be, they're going to take our customers away from us. We have to deliver this exceptional customer and employee experience. And Coca-Cola is a great example of one that really came in with CA brought in a disruptor in order to align digital with the CEO's thoughts and processes and organization. These are >>Highly capable companies. We heard from the head of finance at, at applied materials today. He was also coming on. I was quite, I mean, this is a applied materials is really strong company. They're talking about a 20 plus billion dollar company with $120 billion market cap. They supply semiconductor equipment and they're a critical component of the semiconductor supply chain. And we all know what's going on in semiconductors today with a huge shortage. So they're a really important company, but I was impressed with, uh, their finance leaders vision on how they're transforming the company. And it was not like, you know, 10 years out, these were not like aspirational goals. This is like 20, 19, 20, 22. Right. And, and really taking costs out of the business, driving new innovation. And, and it's, it was it's, it's refreshing to me Lisa, to see CFOs, you know, typically just bottom line finance focused on these industry transformations. Now, of course, at the end of the day, it's all about the bottom line, but they see technology as a way to get there. In fact, he put technology right in the middle of his stack. I want to ask him about that too. I actually want to challenge him a little bit on it because he had that big Hadoop elephant in the middle and this as an elephant in the room. And that picture, >>The strategy though, that applied materials had, it was very well thought out, but it was also to your point designed to create outcomes year upon year upon year. And I was looking at some of the notes. I took that in year one, alone, 274 automations in production. That's a lot, 150,000 in annual work hours automated 124 use cases they tackled in one year. >>So I want to, I want to poke at that a little bit too. And I, and I did yesterday with some guests. I feel like, well, let's see. So, um, I believe it was, uh, I forget what guests it was, but she said we don't put anything forward that doesn't hit the income statement. Do you remember that? Yes, it was Chevron because that was pushing her. I'm like, well, you're not firing people. Right. And we saw from IDC data today, only 13% of organizations are saying, or, or, or the organizations at 13% of the value was from reduction in force. And a lot of that was probably in plan anyway, and they just maybe accelerated it. So they're not getting rid of headcount, but they're counting hours saved. So that says to me, there's gotta be an normally or often CFOs say, well, it's that soft dollars because we're redeploying folks. But she said, no, it hits the income statement. So I don't, I want to push a little bit and see how they connect the dots, because if you're going to save hours, you're going to apply people to new work. And so either they're generating revenue or cutting costs somewhere. So, so there's another layer that I want to appeal to understand how that hits the income state. >>Let's talk about some of that IDC data. They announced a new white paper this morning sponsored by UI path. And I want to get your perspectives on some of the stats that they talked about. They were painting a positive picture, an optimistic picture. You know, we can't talk about automation without talking about the fear of job loss. They've been in a very optimistic picture for the actual gains over a few year period. What are your thoughts about that? Especially when we saw that stat 41% slowed hiring. >>Yeah. So, well, first of all, it's a sponsored study. So, you know, and of course the conferences, so it's going to be, be positive, but I will say this about IDC. IDC is a company I would put, you know, forest they're similar. They do sponsored research and they're credible. They don't, they, they have the answer to their audience, so they can't just out garbage. And so it has to be defensible. So I give them credit there that they won't just take whatever the vendor wants them to write and then write it. I've used to work there. And I, and I know the culture and there's a great deal of pride in being able to defend what you do. And if the answer doesn't come out, right, sorry, this is the answer. You know, you could pay a kill fee or I dunno how they handle it today. >>But, but, so my point is I think, and I know the people who did that study, many of them, and I think they're pretty credible. I, I thought by the way, you, to your 41% point. So the, the stat was 13% are gonna reduce head count, right? And then there were two in the middle and then 41% are gonna reduce or defer hiring in the future. And this to me, ties into the Erik Brynjolfsson and, and, and, uh, and, and McAfee work. Andy McAfee work from MIT who said, look, initially actually made back up. They said, look at machines, have always replaced humans. Historically this was in their book, the second machine age and what they said was, but for the first time in history, machines are replacing humans with cognitive functions. And this is sort of, we've never seen this before. It's okay. That's cool. >>And their, their research suggests that near term, this is going to be a negative economic impact, sorry, negative impact on jobs and salaries. And we've, we've generally seen this, the average salary, uh, up until recently has been flat in the United States for years and somewhere in the mid fifties. But longterm, their research shows that, and this is consistent. I think with IDC that it's going to help hiring, right? There's going to be a boost buddy, a net job creator. And there's a, there's a, there's a chasm you've got across, which is education training and skill skillsets, which Brynjolfsson and McAfee focused on things that humans can do that machines can't. And you have this long list and they revisited every year. Like they used to be robots. Couldn't walk upstairs. Well, you see robots upstairs all the time now, but it's empathy, it's creativity. It's things like that. >>Contact that humans are, are much better at than machines, uh, even, even negotiations. And, and so, so that's, those are skills. I don't know where you get those skills. Do you teach those and, you know, MBA class or, you know, there's these. So their point is there needs to be a new thought process around education, public policy, and the like, and, and look at it. You can't protect the past from the future, right? This is inevitable. And we've seen this in terms of economic activity around the world countries that try to protect, you know, a hundred percent employment and don't let competition, they tend to fall behind competitively. You know, the U S is, is not of that category. It's an open market. So I think this is inevitable. >>So a lot about upskilling yesterday, and the number of we talked with PWC about, for example, about what they're doing and a big focus on upscaling. And that was part of the IDC data that was shared this morning. For example, I'll share a stat. This was a survey of 518 people. 68% of upscaled workers had higher salaries than before. They also shared 57% of upskilled workers had higher roles and their enterprises then before. So some, again, two point it's a sponsored study, so it's going to be positive, but there, there was a lot of discussion of upskilling yesterday and the importance on that education, because to your point, we can't have one without the other. You can't give these people access to these tools and not educate them on how to use it and help them help themselves become more relevant to the organization. Get rid of the mundane tasks and be able to start focusing on more strategic business outcome, impacting processes. >>We talked yesterday about, um, I use the example of, of SAP. You, you couldn't have predicted SAP would have won the ERP wars in the early to mid 1990s, but if you could have figured out who was going to apply ERP to their businesses, you know what, you know, manufacturing companies and these global firms, you could have made a lot of money in the stock market by, by identifying those that were going to do that. And we used to say the same thing about big data, and the reason I'm bringing all this up is, you know, the conversations with PWC, Deloitte and others. This is a huge automation, a huge services opportunity. Now, I think the difference between this and the big data era, which is really driven by Hadoop is it was big data was so complicated and you had a lack of data scientists. >>So you had to hire these services firms to come in and fill those gaps. I think this is an enormous services opportunity with automation, but it's not because the software is hard to get to work. It's all around the organizational processes, rethinking those as people process technology, it's about the people in the process, whereas Hadoop and the big data era, it was all about the tech and they would celebrate, Hey, this stuff works great. There are very few companies really made it through that knothole to dominate as we've seen with the big internet giants. So you're seeing all these big services companies playing in this market because as I often say, they like to eat at the trough. I know it's kind of a pejorative, but it's true. So it's huge, huge market, but I'm more optimistic about the outcomes for a broader audience with automation than I was with, you know, big data slash Hadoop, because I think the software as much, as much more adoptable, easier to use, and you've got the cloud and it's just a whole different ball game. >>That's certainly what we heard yesterday from Chevron about the ease of use and that you should be able to see results and returns very quickly. And that's something too that UI path talks about. And a lot of their marketing materials, they have a 96, 90 7% retention rate. They've done a great job building their existing customers land and expand as we talked about yesterday, a great use case for that, but they've done so by making things easy, but hearing that articulated through the voice of their customers, fantastic validation. >>So, you know, the cube is like a little, it's like a interesting tip of the spirits, like a probe. And I will tell you when I, when we first started doing the cube and the early part of the last decade, there were three companies that stood out. It was Splunk service now and Tableau. And the reason they stood out is because they were able to get customers to talk about how great they were. And the light bulb went off for us. We were like, wow, these are three companies to watch. You know, I would tell all my wall street friends, Hey, watch these companies. Yeah. And now you see, you know, with Frank Slootman at snowflake, the war, the cat's out of the bag, everybody knows it's there. And they're expecting, you know, great things. The stock is so priced to perfection. You could argue, it's overpriced. >>The reason I'm bringing this up is in terms of customer loyalty and affinity and customer love. You're getting it here. Absolutely this ecosystem. And the reason I bring that up is because there's a lot of questions in the, in the event last night, it was walking around. I saw a couple of wall street guys who came up to me and said, Hey, I read your stuff. It was good. Let's, let's chat. And there's a lot of skepticism on, on wall street right now about this company. Right? And to me, that's, that's good news for you. Investors who want to do some research, because the words may be not out. You know, they, they, they gotta prove themselves here. And to me, the proof is in the customer and the lifetime value of that customer. So, you know, again, we don't give stock advice. We, we kind of give fundamental observations, but this stock, I think it's trading just about 50. >>Now. I don't think it's going to go to 30, unless the market just tanks. It could have some, you know, if that happens, okay, everything will go down. But I actually think, even though this is a richly priced stock, I think the future of this company is very bright. Obviously, if they continue to execute and we're going to hear from the CEO, right? People don't know Daniel, Denise, right? They're like, who is this guy? You know, he started this company and he's from Eastern Europe. And we know he's never have run a public company before, so they're not diving all in, you know? And so that to me is something that really pay attention to, >>And we can unpack that with him later today. And we've got some great customers on the program. You mentioned Uber's here. Spotify is here, applied materials. I feel like I'm announcing something on Saturday night. Live Uber's here. Spotify is here. All right, Dave, looking forward to a great action packed today. We're going to dig more into this and let's get going. Shall we let's do it. All right. For David Dante, I'm Lisa Martin. This is the cube live in Las Vegas. At the Bellagio. We are coming to you presenting UI path forward for come back right away. Our first guest comes up in just a second.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Worldwide Public sector Welcome to the cubes Coverage of AWS 2020. This is specialized programming for the worldwide public sector. I'm Lisa Martin, and I'm joined by Mick Boccaccio, the security advisor at Splunk Met. Welcome to the Q Virtual Oh, >>thank you for having me. It's great to be here. >>So you have a really interesting background that I wanted to share with our audience. You were the first see so in the history of U. S presidential campaigns with Mayor Pete, you were also branch shape of Threat intelligence at the executive office of the President. Tell us something about about your background is so interesting. >>Uh, yeah, those and I'm a gonna Def con and I teach lock picking for funds. Ease working for Mayor Pete A. C. So the campaign was really, really unique opportunity and I'm glad I did it. I'm hoping that, you know, on both sides of the aisle, no matter what your political preference, people realize that security and campaigns can only be married together. That was an incredible experience and worked with Mayor P. And I learned so much about how campaigns work and just the overall political process. And then previous to that being at the White House and a threat intelligence, role of branch chief they're working over the last election, the 2016 election. I think I learned probably more than any one person wants Thio about elections over that time. So, you know, I'm just a security nerd. That kind of fell into those things. And and and here I am and really, really, really just fortunate to have had those experiences. >>Your phone and your email must have been blowing up the last couple of weeks in the wake of the US presidential election, where the word fraud has brought up many times everyday. But election security. When I saw that you were the first, see so for Pete Buddha Judge, that was so recent, I thought, Really, Why? Why are they just now getting folks like yourself? And you are a self described a cybersecurity nerd? Why are they Why were they just recently starting to catch on to this? >>I think it's, uh like security on the campaign and security anywhere else on credit to the Buddha Judge campaign. There is no federal or mandate or anything like that that says your campaign has toe have a security person at the head of it or any standards to implement those security. So you know that the Buddha Judge campaign kind of leaned into it. We wanna be secure. We saw everything that happened in 2016. We don't want that to be us. And I think Mawr campaigns are getting on that plane. Definitely. You know, you saw recently, uh, Trump's campaign, Biden's campaign. They all had a lot of security folks in, and I think it's the normal. Now people realize how important security is. Uh, not only a political campaign, but I guess the political process overall, >>absolutely. We've seen the rise of cyber attacks and threats and threat vectors this year alone, Ransomware occurring. Everyone attack every 11 seconds or so I was reading recently. So give me an other view of what the biggest threats are right now. >>Two elections and I think the election process in general. You know, like I said, I'm just a security nerd. I've just got a weird background and done some really unique things. Eso I always attack the problems like I'm a security nerd and it comes down to, you know that that triumvirate, the people process and technology people need had to have faith in the process. Faith in the technology. You need to have a a clear source to get their information from the process. To me, I think this year, more than previous elections highlighted the lack of a federal uniforms standard for federal elections. State the state. We have different, different standards, and that kind of leads to confusion with people because, hey, my friend in Washington did it this way. But I'm in Texas and we do it this way. And I think that that standard would help a lot in the faith in the system. And then the last part of that. The technology, uh, you know, voting machines campaigns like I mentioned about campaigns. There's nothing that says a campaign has toe have a security person or a security program, and I think those are the kind of standards for, you know, just voting machines. Um, that needs to be a standard across the board. That's uniforms, so people will will have more faith because It's not different from state to state, and it's a uniformed process. >>E think whole country could have benefited from or uniformed processes in 2020. But one of the things that I like I did my first male and fellow this year always loved going and having that in person voting experience and putting on my sticker. And this year I thought in California we got all of our But there was this massive rise in mainland ballots. I mean, think about that and security in terms of getting the public's confidence. What are some of the things that you saw that you think needs to be uniforms going forward >>again? I think it goes back to when When you look at, you know, you voted by mail and I voted absentee and your ballot was due by this date. Um, you know where I live? Voting absentee. It's Dubai. This state needs we received by the state. Andi, I think this year really highlighted the differences between the states, and I'm hoping that election security and again everyone has done a super fantastic job. Um, sister has done incredible. If you're all their efforts for the working with election officials, secretaries of states on both sides of the aisle. It's an incredible work, and I hope it continues. I think the big problem election security is you know, the election is over, so we don't care again until 2022 or 2024. And I think putting something like a federalized standard, whether it be technology or process putting that in place now so that we're not talking about this in two or four years. I'm hoping that moment, um, continues, >>what would your recommendation be from building security programs to culture and awareness? How would you advise that they start? >>So, uh, one of the things that when I was on the Buddha Judge campaign, you know, like I said, we was the first person to do security for a campaign. And a lot of the staffers didn't quite have the background of professional background of work with security person. No, you know why? What I was doing there Eso my hallmark was You know, I'm trying to build a culture heavy on the cult. Um, you got to get people to buy in. I think this year when you look at what What Krebs and siesta and where the team over there have done is really find a way to tell us. Security story and every facet of the election, whether it be the machines themselves, the transporting the votes, counting the votes, how that information gets out to people websites I started like rumor control, which were were amazing amazing efforts. The public private partnerships that were there I had a chance to work with, uh, MJ and Tanya from from AWS some election project. I think everyone has skin in the game. Everyone wants to make it better. And I hope that moment, um, continues. But I think, you know, embracing that there needs to be a centralized, uniformed place, uh, for every state. And I think that would get rid of a lot of confusion >>when you talk about culture and you mentioned specifically called Do you think that people and agencies and politicians are ready to embrace the culture? Is there enough data to support that? This is really serious. We need to embrace this. We need to buy in a You said, um >>I hope right. I don't know what it could take. I'm hoping so after seeing everything you know, being at the White House from that aperture in 2016. Seeing all of that, I would, you know, think right away. Oh, my gosh. 2018, The midterms, We're gonna be on the ball. And that really didn't happen like we thought it would. 2020. We saw a different kind of technical or I guess, not as technical, uh, security problem. And I think I'm kind of shifting from that to the future. People realize. And I think, uh, both sides of the aisle are working towards security programs and security posture. I think there's a lot of people that have bought into the idea. Um, but I think it kind of starts from the top, and I'm hoping it becomes a standard, so there's not really an option. You will do this just for the security and safety of the campaigns and the electoral process. But I do see a lot more people leaning into it, and a lot more resource is available for those people that are >>talk to me about kind of the status of awareness of security. Needing to combat these issues, be able to remediate them, be able to defend against them where our folks in that awareness cycle, >>I think it ebbs and flows like any other process. Any other you know, incident, event. That happens. And from my experience in the info SEC world, normally there's a compromise. There's an incident, a bunch of money gets thrown at it and then we forget about it a year or two later. Um, I think that culture, that awareness comes in when you have folks that would sustain that effort. And again, you know, on the campaign, um, even at the White House, we try to make everyone apart of security. Security is and all the time thing that everyone has a stake in. Um, you know, I can lock down your email at work. I can make sure this system is super super secure, but it's your personal threat model. You know, your personal email account, your personal social media, putting more security on those and being aware of those, I think that's that awareness is growing. And I Seymour folks in the security community just kind of preaching that awareness more and more and something I'm really, really excited about. >>Yeah, the biggest thing I always think when we talk about security is people that were the biggest threat vector and what happened 89 months ago when so many businesses, um, in any, you know, public sector and private went from on site almost maybe 100% on site to 100% remote people suddenly going, I've got to get connected through my home network. Maybe I'm on my own personal device and didn't really have the time of so many distractions to recognize a phishing email just could come in and propagate. So it's that the people challenge e always seems to me like that might be the biggest challenge. Besides, the technology in the process is what do you think >>I again it goes back. I think it's all part of it. I think. People, um, I've >>looked at it >>slightly. Ah, friend of mine made a really good point. Once he was like, Hey, people gonna click on the link in the email. It's just I think 30% of people dio it's just it's just the nature of people after 20 some odd years and info sec, 20 some odd years and security. I think we should have maybe done a better job of making that link safer, to click on, to click on to make it not militias. But again it goes back, Thio being aware, being vigilant and to your point. Since earlier this year, we've seen a tax increase exponentially specifically on remote desktop protocols from Cove. It related themes and scams and, you know, ransomware targeting healthcare systems. I think it's just the world's getting smaller and we're getting more connected digitally. That vigilance is something you kind of have to building your threat model and build into the ecosystem. When we're doing everything, it's just something you know. I quit a lot, too. You've got junk email, your open your mailbox. You got some junk mail in there. You just throw it out. Your email inbox is no different, and just kind of being aware of that a little more than we are now might go a long way. But again, I think security folks want to do a better job of kind of making these things safer because malicious actors aren't going away. >>No, they're definitely not going away that we're seeing the threat surfaces expanding. I think it was Facebook and TIC Tac and Instagram that were hacked in September. And I think it was unsecured cloud database that was the vehicle. But talking about communication because we talk about culture and awareness communication from the top down Thio every level is imperative. How how do we embrace that and actually make it a standard as possible? >>Uh, in my experience, you know, from an analyst to a C So being able to communicate and communicate effectively, it's gonna save your butt, right? It's if you're a security person, you're You're that cyber guy in the back end, something just got hacked or something just got compromised. I need to be able to communicate that effectively to my leadership, who is gonna be non technical people, and then that leadership has to communicate it out to all the folks that need to hear it. I do think this year just going back to our elections, you saw ah lot of rapid communication, whether it was from DHS, whether it was from, you know, public partners, whether was from the team over Facebook or Twitter, you know, it was ah, lot of activity that they detected and put out as soon as they found it on it was communicated clearly, and I thought the messaging was done beautifully. When you look at all the work that you know Microsoft did on the block post that came out, that information is put out as widely as possible on. But I think it just goes back to making sure that the people have access to it whenever they need it, and they know where to get it from. Um, I think a lot of times you have compromised and that information is slow to get out. And you know that DeLay just creates a confusion, so it clearly concisely and find a place for people, could get it >>absolutely. And how do you see some of these challenges spilling over into your role as the security advisor for Splunk? What are some of the things that you're talking with customers about about right now that are really pressing issues? >>I think my Rolex Plunkett's super super weird, because I started earlier in the year, I actually started in February of this year and a month later, like, Hey, I'm hanging out at home, Um, but I do get a chance to talk to ah, lot of organizations about her security posture about what they're doing. Onda about what they're seeing and you know everything. Everybody has their own. Everybody's a special snowflakes so much more special than others. Um, credit to Billy, but people are kind of seeing the same thing. You know, everybody's at home. You're seeing an increase in the attack surface through remote desktop. You're seeing a lot more fishing. You're singing just a lot. People just under computer all the time. Um, Zoom WebEx I've got like, I don't know, a dozen different chat clients on my computer to talk to people. And you're seeing a lot of exploits kind of coming through that because of that, people are more vigilant. People are adopting new technologies and new processes and kind of finding a way to move into a new working model. I see zero trust architecture becoming a big thing because we're all at home. We're not gonna go anywhere. And we're online more than we're not. I think my circadian rhythm went out the window back in July, so all I do is sit on my computer more often than not. And that caused authentication, just, you know, make sure those assets are secure that we're accessing from our our work resource is I think that gets worse and worse or it doesn't. Not worse, rather. But that doesn't go away, no matter what. Your model is >>right. And I agree with you on that circadian rhythm challenge. Uh, last question for you. As we look at one thing, we know this uncertainty that we're living in is going to continue for some time. And there's gonna be some elements of this that air gonna be permanent. We here execs in many industries saying that maybe we're going to keep 30 to 50% of our folks remote forever. And tech companies that air saying Okay, maybe 50% come back in July 2021. As we look at moving into what we all hope will be a glorious 2021 how can businesses prepare now, knowing some amount of this is going to remain permanent? >>It's a really interesting question, and I'll beyond, I think e no, the team here. It's Plunkett's constantly discussions that start having are constantly evaluating, constantly changing. Um, you know, friends in the industry, it's I think businesses and those executives have to be ready to embrace change as it changes. The same thing that the plans we would have made in July are different than the plans we would have made in November and so on. Andi, I think, is having a rough outline of how we want to go. The most important thing, I think, is being realistic with yourself. And, um, what, you need to be effective as an organization. I think, you know, 50% folks going back to the office works in your model. It doesn't, But we might not be able to do that. And I think that constant ability Thio, adjust. Ah, lot of company has kind of been thrown into the fire. I know my backgrounds mostly public sector and the federal. The federal Space has done a tremendous shift like I never well, rarely got to work, uh, vert remotely in my federal career because I did secret squirrel stuff, but like now, the federal space just leaning into it just they don't have an option. And I think once you have that, I don't I don't think you put Pandora back in that box. I think it's just we work. We work remote now. and it's just a new. It's just a way of working. >>Yep. And then that couldn't be more important to embrace, change and and change over and over again. Make. It's been great chatting with you. I'd love to get dig into some of that secret squirrel stuff. I know you probably have to shoot me, so we will go into that. But it's been great having you on the Cube. Thank you for sharing your thoughts on election security. People processes technology, communication. We appreciate it. >>All right. Thanks so much for having me again. >>My pleasure for McClatchy. Oh, I'm Lisa Martin. You're watching the Cube virtual.

>> (upbeat music) >> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards Brought to you by Amazon Web Services. >> Hi, and welcome back. I'm Stu Miniman and this is theCUBE's coverage of Amazon Web Services, Public Sector Awards for their partners. Really interesting, we get to talk to people around the globe, we talked to the vendors, the award winners as well as their customers who have some interesting projects. So happy to welcome to the program coming to us from Argentina. I have Leo Bracco. He is the Latin American Executive Director for CloudHesive and joining him, his customer Carolina Tchintian. She is the Director of the Political Institution Program at CIPPEC. Thank you so much for joining us. >> Thank you. >> Thank you for having us. >> All right, so Leo, first of all, let's start with you if we could. So CloudHesive first of all, congratulations, you were the Nonprofit Sector award winner for cybersecurity solutions. Of course, anybody that knows public sector, there's the government agencies, there's nonprofits there's education. The cybersecurity of course, went from the top priority to the top, top priority here in 2020. So if you could just give us a snapshot of CloudHesive for our customer. >> Well, CloudHesive is a US based company, started six years ago in 2014. And we decide a couple of years ago to move to Latin America and to start working with Latin America customers. Our offices are in Argentina right now. And one of the focus that we have in the solutions that we give to our customers is security. We work on services to help companies to reduce the cost, increase productivity, and what should the security posture? So we've been working a long time ago to many NPOs, and seeing how they can leverage the solutions and how they can give secure, how to be secure in the world. In the internet. >> All right, Carolina, if you could tell us a little bit about the CIPPEC and maybe then key us up as the project that you're working on. >> Okay, thank you. So CIPPEC is a nonprofit think tank, nonprofit organization, independent organization that aims to deliver better public policies in different areas. In economic development, in social protection and state and government. My particular program, the political institutions program goal is, or the mission is basically to promote evidence based decisions to improve democratic processes and to guarantee civil and political rights across all the countries. So we on issues such as improving election administrations, legislative work, representation, and that's our area of work. >> Wonderful. Sounds like a phenomenal project. Leo, if you could help us understand where did CloudHesive get involved in this project? Was there an existing relationship already, or was it for a specific rollout? that tell us about, obviously the security angles are a big piece? >> No, we didn't have a previous engagement with them. They come to us with a very short time to elections and they need a secure solution. So we first have to analyze the actual solution, how it works, acknowledging well the current infra that they have. Then we have to understand the challenge that they're facing. They have a very public site, they need to go public and they need to be very secure. And the last, we have to develop a fast migration strategy. We knew that AWS was the perfect fit for the need. So we just had to align a good strategy with the customer need. And all these it has been done in less than 72 hours. That was our deadline to elections. >> Wow, talk about fast. Okay, Carolina, help us understand a little bit. Had your organization, had you been using a Cloud before? Seventy-two hours is definitely an aggressive timeline. So help us understand a little bit as to what went into making your decision and obviously, 72 hours super short timeframe. >> Super, super short. Yeah, that was a big challenge. So let me tell you more about what we do and the context. So Argentina holds elections, national elections every two years. In each election year CIPPEC tries to generate and systematize analysis of provincial and national elections with the goal of informing key actors in the electoral processes. This is and decision makers, political parties, media, and general population. So as our first experience in 2017, with informed voter project, we had this collaboration with the National Electoral Authorities in which we created a landing page in our website where you could find as the voter, all of the information you need to go and cast your vote throughout the entire election process. Meaning from the campaign stage, election administration details, polling places, electoral offer, participation et cetera. So that was a landing page hosted in our website. And in 2017, we managed to have a button in every eligible voter in Argentina Facebook feed. So you could go click there and go to our website, right. And have all of the information summarize in a very simple way, straightforward way. So what happened in the 2017 election day is that the button was so successful that the landing page made our server to collapse in the first hours of the election day. So we learned a huge lesson there, which was that we had to be prepared in 2019, if we wanted to repeat this experience. And that is how we get to CloudHesive. >> Wonderful, Leo, if you could, help us understand a little bit architecturally what's going on there, what was CoHesive doing, what AWS services were leveraged? >> Perfect. Well we need great reliability, performance, scalability of course and the main thing security. We have no doubt about the Cloud and all the differentials of AWS. Our main question was about how do we align the right services to give the best solution to the customer? So we did kind of strategy with S3, CloudFront, and we, at the same time being monitorizing everything with CloudTrail and securing the public's access to all of these information. That give us a perfect fit for the solution, a very easy solution and very of course scalable, but more than anything, we could improve the customer experience in very small amount of time. So this is a very simple solution, that fits perfect for the customer. >> Wonderful. Carolina, if you could, tell us how did things go? What lessons have you learned? Anything along the way that you would give feedback to your peers or other organizations that were looking to do something similar? >> Yeah, well, the 2017 experience was a very tough experience for us because we've been preparing for election day during the 2016 and 2017. And the infrastructure was the limit we had in that point. So we couldn't afford ... We have a commitment with informing voters and informing key actors on election process. And these key actors are expecting that information on election day, before, and after. The lesson there is, we cannot be limited by the infrastructure. Assuming that in 2019, that the landing page would receive a similar amount or a huge amount of traffic volume visits on the election day, basically, we knew that traditional hosting service couldn't fulfill those needs so we had to go beyond traditional and the partner was critical to help us to the migration, to the Cloud. >> Yeah, Leo, maybe you could speak a little bit to that, the scalability, and of course, nonprofit's very sensitive to costs involved in these solutions. Help us understand that those underpinnings of leveraging, AWS specifically in CloudHesive. How this meets their needs and still is financially, makes sense. >> Perfect. When you have this kind of solutions, of course, your first concern is, okay, how do I make a scalable solution that fits on the, just on this moment that they need the behavior for so many infrastructure involved. And then at the other day, they need no infra at all, but you have another two big things that you have to focus on. One, is the security, you need to monitor all the behaviors of the content and pay attention to any external menace. You have one 24-hour day, so you need to be very responsibility and high sensitive information that the customer has on the set of data there. It's good to say that we have no security incidents, and no security breach during the most public stage of the operation, so that there was very good for us. The next thing is from the delivery perspective. You have a potential pick of people over the side to usually manage the content delivery network to answer all the requirements. You must be able to share the content in CloudFront, and so you have, and you can achieve your goals, right? And what I can say, it's about numbers, we achieve more than 99.5 efficiency hit rate you over the CDN, that's over CloudFront. And we kept server CPU such below 10% all the time. So this was a major success for us. Like we have no trouble, we use things at the most. And most of anything, the customer has the security, everything look from our perspective. (mumbles) >> Leo, what follow up if I could, if you look at 2020 being able to scale and respond to the changes in workload and be able to stay secure when bad actors, many people are working at home, but doesn't mean the bad actors aren't out there. We've actually seen an increase in security attacks. So just, do you have any commentary overall about what's happening more recently in what you see in your space? >> Yeah, well, we're very focused right now and while security is being each time bigger, right? One of the biggest menace in security is our own team, because we have to keep our teams auto align to the process and understanding the security as a first step doing things from the network perspective. Then we have a very good experience over this last two years, with all the security tools that AWS is seeking to the market. So we now have CloudTrail. We can do many things with WAF we're working towers of new good security solutions. And so I think this will be the future. We have to focus ourself in these two pillars. The first pillar is, okay, what we can do on our own network and the other pillar's, all the tools that AWS is giving us so we can manage security from a new perspective. >> Carolina, last question that I have for you is, look forward a little bit, if you will, are there things that you'll be looking to do in future election cycles or anything else from this project that you could expect going forward? >> Yeah, definitely. We're going to repeat this experience in 2021. Trying to think of the success was the 2019 election cycle. And in this particular informed voter project, we might want to keep doing this for the next election cycles, not only 2023 now, but for the future. >> All right then, Leo, last piece for you, first of all, congratulations, again, winning Best Cyber Security Solution for Nonprofit. Just talk a little bit if you would, about your partnership with AWS and specifically, the requirements and what you see in the nonprofit segment. >> Well, we see that the nonprofit are growing large too, they will need very good scalable solutions. We see that all the focus that we have in on security is the next need because we have been working on these towers to the future. The solutions kept growing each time. The networks are growing each time. And the traffic is growing. The focus on the security will be one of the appendix of our work in the future. And I think that's the biggest issue that we are going to have. Having good engineers, good hard work and manage the challenge and consolidate all the solution as a need. Right now, we're working on many projects with different NGO's and we're working towers that they have the solution that fits them. And of course, we try to keep, in all the public sector, we try to keep the cost at a range level that we can afford that our customers can afford. That's I think, a big problem that we're having. >> Well, Carolina, congratulations on the progress with your project. Thank you so much for joining us. And Leo, thank you again for joining us and congratulations to you and the CloudHesive team for winning the award. >> Thanks. >> Thank you very much. >> All right, stay tuned for more coverage, theCUBE, at the AWS Public Sector Partner Awards. I'm Stu Miniman. Thanks for watching. (upbeat music)

(upbeat music) >> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hi, and welcome back. I'm Stu Miniman and this is theCUBE's coverage of Amazon Web Services, Public Sector Awards for their partners. Really interesting, we get to talk to people around the globe, we talked to the vendors, the award winners as well as their customers who have some interesting projects. So happy to welcome to the program coming to us from Argentina. I have Leo Bracco. He is the Latin American Executive Director for CloudHesive and joining him, his customer Carolina Tchintian. She is the Director of the Political Institution Program at CIPPEC. Thank you so much for joining us. >> Thank you. >> Thank you for having us. >> All right, so Leo, first of all, let's start with you if we could. So CloudHesive first of all, congratulations, you were the Nonprofit Sector award winner for cybersecurity solutions. Of course, anybody that knows public sector, there's the government agencies, there's nonprofits there's education. The cybersecurity of course, went from the top priority to the top, top priority here in 2020. So if you could just give us a snapshot of CloudHesive for our customer. >> Well, CloudHesive is a US based company, started six years ago in 2014. And we decide a couple of years ago to move to Latin America and to start working with Latin America customers. Our offices are in Argentina right now. And one of the focus that we have in the solutions that we give to our customers is security. We work on services to help companies to reduce the cost, increase productivity, and what should the security posture? So we've been working a long time ago to many NPOs, and seeing how they can leverage the solutions and how they can give secure, how to be secure in the world. In the internet. >> All right, Carolina, if you could tell us a little bit about the CIPPEC and maybe then key us up as the project that you're working on. >> Okay, thank you. So CIPPEC is a nonprofit think tank, nonprofit organization, independent organization that aims to deliver better public policies in different areas. In economic development, in social protection and state and government. My particular program, the political institutions program goal is, or the mission is basically to promote evidence based decisions to improve democratic processes and to guarantee civil and political rights across all the countries. So we on issues such as improving election administrations, legislative work, representation, and that's our area of work. >> Wonderful. Sounds like a phenomenal project. Leo, if you could help us understand where did CloudHesive get involved in this project? Was there an existing relationship already, or was it for a specific rollout? that tell us about, obviously the security angles are a big piece? >> No, we didn't have a previous engagement with them. They come to us with a very short time to elections and they need a secure solution. So we first have to analyze the actual solution, how it works, acknowledging well the current infra that they have. Then we have to understand the challenge that they're facing. They have a very public site, they need to go public and they need to be very secure. And the last, we have to develop a fast migration strategy. We knew that AWS was the perfect fit for the need. So we just had to align a good strategy with the customer need. And all these it has been done in less than 72 hours. That was our deadline to elections. >> Wow, talk about fast. Okay, Carolina, help us understand a little bit. Had your organization, had you been using a Cloud before? Seventy-two hours is definitely an aggressive timeline. So help us understand a little bit as to what went into making your decision and obviously, 72 hours super short timeframe. >> Super, super short. Yeah, that was a big challenge. So let me tell you more about what we do and the context. So Argentina holds elections, national elections every two years. In each election year CIPPEC tries to generate and systematize analysis of provincial and national elections with the goal of informing key actors in the electoral processes. This is and decision makers, political parties, media, and general population. So as our first experience in 2017, with informed voter project, we had this collaboration with the National Electoral Authorities in which we created a landing page in our website where you could find as the voter, all of the information you need to go and cast your vote throughout the entire election process. Meaning from the campaign stage, election administration details, polling places, electoral offer, participation et cetera. So that was a landing page hosted in our website. And in 2017, we managed to have a button in every eligible voter in Argentina Facebook feed. So you could go click there and go to our website, right. And have all of the information summarize in a very simple way, straightforward way. So what happened in the 2017 election day is that the button was so successful that the landing page made our server to collapse in the first hours of the election day. So we learned a huge lesson there, which was that we had to be prepared in 2019, if we wanted to repeat this experience. And that is how we get to CloudHesive. >> Wonderful, Leo, if you could, help us understand a little bit architecturally what's going on there, what was CoHesive doing, what AWS services were leveraged? >> Perfect. Well we need great reliability, performance, scalability of course and the main thing security. We have no doubt about the Cloud and all the differentials of AWS. Our main question was about how do we align the right services to give the best solution to the customer? So we did kind of strategy with S3, CloudFront, and we, at the same time being monitorizing everything with CloudTrail and securing the public's access to all of these information. That give us a perfect fit for the solution, a very easy solution and very of course scalable, but more than anything, we could improve the customer experience in very small amount of time. So this is a very simple solution, that fits perfect for the customer. >> Wonderful. Carolina, if you could, tell us how did things go? What lessons have you learned? Anything along the way that you would give feedback to your peers or other organizations that were looking to do something similar? >> Yeah, well, the 2017 experience was a very tough experience for us because we've been preparing for election day during the 2016 and 2017. And the infrastructure was the limit we had in that point. So we couldn't afford ... We have a commitment with informing voters and informing key actors on election process. And these key actors are expecting that information on election day, before, and after. The lesson there is, we cannot be limited by the infrastructure. Assuming that in 2019, that the landing page would receive a similar amount or a huge amount of traffic volume visits on the election day, basically, we knew that traditional hosting service couldn't fulfill those needs so we had to go beyond traditional and the partner was critical to help us to the migration, to the Cloud. >> Yeah, Leo, maybe you could speak a little bit to that, the scalability, and of course, nonprofit's very sensitive to costs involved in these solutions. Help us understand that those underpinnings of leveraging, AWS specifically in CloudHesive. How this meets their needs and still is financially, makes sense. >> Perfect. When you have this kind of solutions, of course, your first concern is, okay, how do I make a scalable solution that fits on the, just on this moment that they need the behavior for so many infrastructure involved. And then at the other day, they need no infra at all, but you have another two big things that you have to focus on. One, is the security, you need to monitor all the behaviors of the content and pay attention to any external menace. You have one 24-hour day, so you need to be very responsibility and high sensitive information that the customer has on the set of data there. It's good to say that we have no security incidents, and no security breach during the most public stage of the operation, so that there was very good for us. The next thing is from the delivery perspective. You have a potential pick of people over the side to usually manage the content delivery network to answer all the requirements. You must be able to share the content in CloudFront, and so you have, and you can achieve your goals, right? And what I can say, it's about numbers, we achieve more than 99.5 efficiency hit rate you over the CDN, that's over CloudFront. And we kept server CPU such below 10% all the time. So this was a major success for us. Like we have no trouble, we use things at the most. And most of anything, the customer has the security, everything look from our perspective. (mumbles) >> Leo, what follow up if I could, if you look at 2020 being able to scale and respond to the changes in workload and be able to stay secure when bad actors, many people are working at home, but doesn't mean the bad actors aren't out there. We've actually seen an increase in security attacks. So just, do you have any commentary overall about what's happening more recently in what you see in your space? >> Yeah, well, we're very focused right now and while security is being each time bigger, right? One of the biggest menace in security is our own team, because we have to keep our teams auto align to the process and understanding the security as a first step doing things from the network perspective. Then we have a very good experience over this last two years, with all the security tools that AWS is seeking to the market. So we now have CloudTrail. We can do many things with WAF we're working towers of new good security solutions. And so I think this will be the future. We have to focus ourself in these two pillars. The first pillar is, okay, what we can do on our own network and the other pillar's, all the tools that AWS is giving us so we can manage security from a new perspective. >> Carolina, last question that I have for you is, look forward a little bit, if you will, are there things that you'll be looking to do in future election cycles or anything else from this project that you could expect going forward? >> Yeah, definitely. We're going to repeat this experience in 2021. Trying to think of the success was the 2019 election cycle. And in this particular informed voter project, we might want to keep doing this for the next election cycles, not only 2023 now, but for the future. >> All right then, Leo, last piece for you, first of all, congratulations, again, winning Best Cyber Security Solution for Nonprofit. Just talk a little bit if you would, about your partnership with AWS and specifically, the requirements and what you see in the nonprofit segment. >> Well, we see that the nonprofit are growing large too, they will need very good scalable solutions. We see that all the focus that we have in on security is the next need because we have been working on these towers to the future. The solutions kept growing each time. The networks are growing each time. And the traffic is growing. The focus on the security will be one of the appendix of our work in the future. And I think that's the biggest issue that we are going to have. Having good engineers, good hard work and manage the challenge and consolidate all the solution as a need. Right now, we're working on many projects with different NGO's and we're working towers that they have the solution that fits them. And of course, we try to keep, in all the public sector, we try to keep the cost at a range level that we can afford that our customers can afford. That's I think, a big problem that we're having. >> Well, Carolina, congratulations on the progress with your project. Thank you so much for joining us. And Leo, thank you again for joining us and congratulations to you and the CloudHesive team for winning the award. >> Thanks. >> Thank you very much. >> All right, stay tuned for more coverage, theCUBE, at the AWS Public Sector Partner Awards. I'm Stu Miniman. Thanks for watching. (upbeat music)

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's the cube. Now, here's your host, Dave Vellante. >> Hello everyone and welcome to this week's Wikibon cube insights powered by ETR. In this breaking analysis ahead of the RSA conference, we want to update you on the cyber security sector. This year's event is underlined by coronavirus fears, IBM has pulled out of the event and cited the epidemic as the reason and it's also brings to the front the sale of RSA by Dell to STG partners and private equity firm. Now in our last security drill down, we cited several mega trends in the security sector. These included the ever escalating sophistication of the attacker, the increased risk from the data economy, the expanded attack surface with the huge number of IP addresses that are that are exploding out there, and the lack of skills and the number of cyber tools that are coming to the market. Now, as you know, in these segments, we'd like to share insights from the cube. And I want you to listen to two American statesman and what they said, on The Cube. Here's general Keith Alexander, who's the former director of the NSA, along with Dr. Robert Gates, who's the former director of the CIA and former Secretary of Defense, play the clip. >> When you think about threats, you think about nation states, so you can go to Iran, Russia, China, North Korea, and then you think about criminal threats, and all the things like ransomware. Some of the nation state actors are also criminals at night, so they can use nation state tools and my concern about all the evolution of cyber threats is that the attacks are getting more destructive. >> I think cyber and the risks associated with cyber, and IT need to be a regular part of every board's agenda. >> So you hear General Alexander really underscore the danger, as well, Dr. Gates is articulating what we've said many times on the cube that cyber security is a board level agenda item. Now, the comments from both of these individuals represent what I would consider tailwinds for cyber technology companies. Now we're going to drill into some of those today. But it's not all frictionless. There are headwinds to in this market space, cloud migration, the shift from north south south to East West network traffic, its pressure traditional appliance based perimeter security solutions, increase complexity and lack of skills and other macro factors, including questions on ROI. CFO saying, hey, we spend all this cash, why aren't we more secure? Now, I want you to hear from two chief information security officers officers on both the challenges that they face and how they're dealing with them. Roll the clip. >> Lack of talent, I mean, we're starving for talent. Cybersecurity is the only field in the world with negative unemployment. We just don't have the actual bodies to actually fill the gaps that we have and in that lack of talent Cecil's are starving. >> I think that the public cloud offers us a really interesting opportunity to reinvent security right. So if you think about all of the technologies and processes and many of which are manual over the years, I think we have an opportunity to leverage automation to make our work easier in some ways. >> Now I featured Brian Lozada and Katie Jenkins before and breaking analysis segments, and you can hear it from the cyber leaders, we lack the talent, and cloud computing and automation are areas we're pursuing. So this challenges security companies to respond. But at the end of the day, companies have no no choice. In other words, organizations buying security solutions, the sophistication of the attacker is very high and the answer to my CFO and ROI is fear based. If you don't do this, you might lose billions in market cap. Now, I want you to take a listen to these cubilam talking about the attacker of sophistication and the importance of communication skills in order to fund cyber initiatives, really to keep up with the bad guys, please play the clip. >> The adversary is talented and they're patient, they're well funded okay, that's that's where it starts. And so, you know why why bring an interpreter to a host when there's already one there right? Why write all this complicated software distribution when I can just use yours. And so that's that's where the play the game starts. And and the most advanced threats aren't leaving footprints because the footprints already there, you know, they'll get on a machine and behaviorally they'll check the cash to see what's hot. And what's hot in the cash means that behaviorally, it's a fast they can go they're not cutting a new trail most of the time, right? So living off the land is not only the tools that they're using the automation, your automation they're using against you, but it's also behavioral. >> That's why the most the most important talent or skill that a security professional needs is communication skills. If you can't articulate technical risk into a business risk to fund your program, it's, you know, it's very hard for you to actually be successful in security. >> Now, the really insidious thing about what TK Keanini just said is the attackers are living off the land, meaning they're using your tools and your behaviors to sneak around your data unnoticed. And so as Brian Lozada said, as a security Pro, you need to be a great communicator in order to get the funding that you need to compete with the bad guys. Which brings me to the RSA conference. This is why you as a security practitioner attend, you want to learn more, you want to obtain new skills, you want to bring back ideas to the organization. Now one of the things I did to prepare for this segment is to read the RSA conference content agenda, which was co authored by Britta Glade and I read numerous blogs and articles about what to expect at the event and from all that I put together this word cloud, which conveys some of the key themes that I would expect you're going to hear at the shows. Look at skills jump right out, just like Brian was saying, the human element is going to be a big deal this year. IoT and the IT OT schism, everyone's talking about the Olympics, and seeing that as a watershed event for cyber, how to apply machine learning and AI is a big theme, as is cloud with containers and server less. phishing, zero trust and frameworks, framework for privacy, frameworks for governance and compliance, the 2020 election and weaponizing social media with deep fakes, and expect to hear a lot about the challenges of securing 5G networks, open source risks, supply chain risks, and of course, the need for automation. And it's no surprise there's going to be a lot of talk about cyber technology, the products and of course, the companies that sell them. So let's get into the market and unpack some of the ETR spending data and drill into some of these companies. The first chart I want to show you is spending on cyber relative to other initiatives. What this chart shows is the spending on cyber security highlighted in the green in relation to other sectors in the ETR taxonomy. Notice the blue dot. It shows the change in spending expected in 2020 versus 2019. Now, two points here. First, is that despite the top of my narrative that we always hear, the reality is that other initiatives compete for budget and you just can't keep throwing cash at the security problem. As I've said before, we spend like .014% percent of our global GDP on cyber, so we barely scratched the surface. The second point is there's there's there's a solid year on year growth quite high at 12% for a sector that's estimated at 100 to 150 billion dollars worldwide, according to many sources. Now let's take a look at some of the players in this space, who are going to be presenting at the RSA conference. You might remember to my 2020 predictions in that breaking analysis I focused on two ETR metrics, Net Score, which is a measure of spending velocity and Market Share, which measures pervasiveness in the data set. And I anointed nine security players as four star players. These were Microsoft, Cisco, Palo Alto Networks, Splunk, Proofpoint, Fortinet, Oka, Cyber Ark and CrowdStrike. What we're showing here is an update of that data with the January survey data. My four star companies were defined as those in the cyber security sector that demonstrate in both net scores or spending momentum, that's the left hand chart and market share or pervasiveness on the right hand chart. Within the top 22 companies, why did I pick 22? Well, seemed like a solid number and it fit nicely in the screen and allowed more folks. So a few takeaways here. One is that there are a lot of cyber security companies in the green from the standpoint of net score. Number two is that Fortinet and Cisco fell off the four star list because of their net scores. While still holding reasonably well, they dropped somewhat. Also, some other companies like Verona's and Vera code and Carbon Black jumped up on the net score rankings, but Cisco and Fortinet are still showing some strength in the market overall, I'ma talk about that. Cisco security businesses up 9% in the quarter, and Fortinet is breaking away from Palo Alto Networks from a valuation perspective, which I'm going to drill into a bit. So we're going to give Cisco and Fortinet two stars this survey period. But look at Zscaler. They made the cut this time their net score or spending momentum jumped from 38% last quarter to nearly 45% in the January survey, with a sizable shared in at 123. So we've added Zscaler to the four star list, they have momentum, and we're going to continue to watch that quarterly horse race. Now, I'd be remiss if I didn't point out that Microsoft continues to get stronger and stronger in many sectors including cyber. So that's something to really pay attention to. Okay, I want to talk about the valuations a bit. Valuations of cyber security space are really interesting and for reasons we've discussed before the market's hot right now, some people think it's overvalued, but I think the space is going to continue to perform quite well, relative to other areas and tech. Why do I say that? Because cyber continues to be a big priority for organizations, the software and annual recurring revenue contribution ARR continues to grow, M&A is going to continue to be robust in my view, which is going to fuel valuations. So Let's look at some of the public companies within cyber. What I've compiled in this chart is eight public companies that were cited as four star or two star firms, as I defined earlier, now ranked this by market value. In the columns, we show the market cap and trailing 12 month revenue in billions, the revenue multiple and the annual revenue growth. And I've highlighted Palo Alto Networks and Fortinet because I want to drill into those two firms, as there's a valuation divergence going on between those two names, and I'll come back to that in just a minute. But first, I want to make a few points about this data. Number one is there's definitely a proportional relationship between the growth rate and the revenue multiple or premium being paid for these companies. Generally growth ranges between one and a half to three times the revenue multiple being paid. CrowdStrike for example has a 39 x revenue multiple and is growing at 110%, so they're at the high end of that range with a growth at 2.8 times their revenue multiple today. Second, and related, as you can see a wide range of revenue multiples based on these growth rates with CrowdStrike, Okta and now Zscaler as the standouts in this regard. And I have to call at Splunk as well. They're both large, and they have high growth, although they are moving beyond, you know, security, they're going into adjacencies and big data analytics, but you you have to love the performance of Splunk. The third point is this is a lucrative market. You have several companies with valuations in the double digit billions, and many with multi billion dollar market values. Cyber chaos means cash for many of these companies, and, of course for their investors. Now, Palo Alto throw some of these ratios out of whack, ie, why the lower revenue multiple with that type of growth, and it's because they've had some execution issues lately. And this annual growth rate is really not the best reflection of the stock price today. That's really being driven by quarterly growth rates and less robust management guidance. So why don't we look into that a bit. What this chart shows is the one year relative stock prices of Palo Alto Networks in the blue and compared to Fortinet in the red. Look at the divergence in the two stocks, look at they traded in a range and then you saw the split when Palo Alto missed its quarter last year. So let me share what I think is happening. First, Palo Alto has been a very solid performance since an IPO in 2012. It's delivered more than four Rex returns to shareholders over that period. Now, what they're trying to do is cloud proof their business. They're trying to transition more to an AR model, and rely less on appliance centric firewalls, and firewalls are core part of the business and that has underperformed expectations lately. And you just take Legacy Tech and Cloud Wash and Cloud native competitors like Zscaler are taking advantage of this and setting the narrative there. Now Palo Alto Network has also had some very tough compares in 2019 relative to 2018, that should somewhat abate this year. Also, Palo Alto has said some execution issues during this transition, especially related to sales and sales incentives and aligning that with this new world of cloud. And finally, Palo Alto was in the process of digesting some acquisitions like Twistlock, PureSec and some others over the past year, and that could be a distraction. Fortinet on the other hand, is benefiting from a large portfolio refresh is capitalizing on the momentum that that's bringing, in fact, all the companies I listed you know, they may be undervalued despite, of all the company sorry that I listed Fortinet may be undervalued despite the drop off from the four star list that I mentioned earlier. Fortinet is one of those companies with a large solution set that can cover a lot of market space. And where Fortinet faces similar headwinds as Palo Alto, it seems to be executing better on the cloud transition. Now the last thing I want to share on this topic is some data from the ETR regression testing. What ETR does is their data scientists run regression models and fit a linear equation to determine whether Wall Street earnings consensus estimates are consistent with the ETR spending data, they started trying to line those up and see what the divergence is. What this chart shows is the results of that regression analysis for both Fortinet and Palo Alto. And you can see the ETR spending data suggests that both companies could outperform somewhat expectations. Now, I wouldn't run and buy the stock based on this data as there's a lot more to the story, but let's watch the earnings and see how this plays out. All right, I want to make a few comments about the sale of the RSA asset. EMC bought RSA for around the same number, roughly $2 billion that SDG is paying Dell. So I'm obviously not impressed with the return that RSA has delivered since 2006. The interesting takeaway is that Dell is choosing liquidity over the RSA cyber security asset. So it says to me that their ability to pay down debt is much more important to Dell and their go forward plan. Remember, for every $5 billion that Dell pays down in gross debt, it dropped 25 cents to EPS. This is important for Dell to get back to investment grade debt, which will further lower its cost. It's a lever that Dell can turn. Now and also in thinking about this, it's interesting that VMware, which the member is acquiring security assets like crazy and most recently purchased carbon black, and they're building out a Security Division, they obviously didn't paw on the table fighting to roll RSA into that division. You know maybe they did in the financial value of the cash to Dell was greater than the value of the RSA customers, the RSA product portfolio and of course, the RSA conference. But my guess is Gelsinger and VMware didn't want the legacy tech. Gelsinger said many times that security is broken, it's his mission to fix it or die trying. So I would bet that he and VMware didn't see RSA as a path to fixing security, it's more likely that they saw it as a non strategic shrinking asset that they didn't want any part of. Now for the record, and I'm even won't bother showing you the the data but RSA and the ETR data set is an unimpressive player in cyber security, their market share or pervasiveness is middle of the pack, so it's okay but their net score spending velocities in the red, and it's in the bottom 20th percentile of the data set. But it is a known brand, certainly within cyber. It's got a great conference and it's been it's probably better that a PE company owns them than being a misfit toy inside of Dell. All right, it's time to summarize, as we've been stressing in our breaking analysis segments and on the cube, the adversaries are very capable. And we should expect continued escalation. Venture capital is going to keep pouring into startups and that's going to lead to more fragmentation. But the market is going to remain right for M&A With valuations on the rise. The battle continues for best of breed tools from upstarts like CrowdStrike and Okta and Zscaler versus sweets from big players like Cisco, Palo Alto Networks and Fortinet. Growth is going to continue to drive valuations. And so let's keep our eyes on the cloud, remains disruptive and for some provides momentum for others provides friction. Security practitioners will continue to be well paid because there's a skill shortage and that's not going away despite the push toward automation. Got in talk about machine intelligence but AI and ML those tools, there are two edged sword as bad actors are leveraging installed infrastructure, both tools and behaviors to so called live off the land, upping the stakes in the arms race. Okay, this is Dave Vellante for Wikibon's CUBE Insights powered by ETR. Thanks for watching this breaking analysis. Remember, these episodes are all available as podcasted Spotfire or wherever you listen. Connect with me at david.vellante at siliconangle.com, or comment on my LinkedIn. I'm @dvellante on Twitter. Thanks for watching everybody. We'll see you next time. (upbeat music).

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello, everyone, and welcome to this week's Cube Insights, powered by ETR. Today is November 8, 2019 and I'd like to address one of the most important topics in the minds of a lot of executives. I'm talking about CEOs, CIOs, Chief Information Security Officers, Boards of Directors, governments and virtually every business around the world. And that's the topic of cyber security. The state of cyber security has changed really dramatically over the last 10 years. I mean, as a cyber security observer I've always been obsessed with Stuxnet, which the broader community discovered the same year that theCUBE started in 2010. It was that milestone that opened my eyes. Think about this. It's estimated that Stuxnet cost a million dollars to create. That's it. Compare that to an F-35 fighter jet. It costs about $85-$100 million to build one. And that's on top of many billions of dollars in R&D. So Stuxnet, I mean, it hit me like a ton of bricks. That the future of war was all about cyber, not about tanks. And the barriers to entry were very, very low. Here's my point. We've gone from an era where thwarting hacktivists was our biggest cyber challenge to one where we're now fighting nation states and highly skilled organized criminals. And of course, cyber crime and monetary theft is the number one objective behind most of these security breaches that we see in the press everyday. It's estimated that by 2021 cyber crime is going to cost society $6 trillion in theft, lost productivity, recovery costs. I mean, that's just a staggeringly large number. It's even hard to fathom. Now, the other C-change is how organizations have had to respond to the bad guys. It used to be pretty simple. I got a castle and the queen is inside. We need to protect her, so what do we do? We built a mote, put it around the perimeter. Now, think of the queen as data. Well, what's happened? The queen has cloned herself a zillion times. She's left the castle. She's gone up to the sky with the clouds. She's gone to the edge of the kingdom and beyond. She's also making visits to machines and the factories and hanging out with the commoners. She's totally exposed. Listen, by 2020, there's going to be hundreds of billions of IP addresses. These are going to be endpoints and phones, TVs, cameras, tablets, automobiles, factory machines, and all these represent opportunities for the bad guys to infiltrate. This explosion of endpoints that I'm talking about is created massive exposures, and we're seeing it manifest itself in the form of phishing, malware, and of course the weaponization of social media. You know, if you think that 2016 was nuts, wait 'til you see how the 2020 presidential election plays out. And of course, there's always the threat of ransomware. It's on everybody's minds these days. So I want to try to put some of this in context and share with you some insights that we've learned from the experts on theCUBE. And then let's drill into some of the ETR data and assess the state of security, the spending patterns. We're going to try to identify some of those companies with momentum and maybe some of those that are a little bit exposed. Let me start with the macro and the challenged faced by organization and that's complexity. Here's Robert Herjavec on theCUBE. Now, you know him from the Shark Tank, but he's also a security industry executive. Herjavec told me in 2017 at the Splunk.com Conference that he thought the industry was overly complex. Let's take a look and listen. >> I think that the industry continues to be extremely complicated. There's a lot of vendors. There's a lot of products. The average Fortune 500 company has 72 security products. There's a stat that RSA this year, that there's 1500 new security start-ups every year. Every single year. How are they going to survive? And which ones do you have to buy because they're critical and provide valuable insights? And which ones are going to be around for a year or two and you're never going to hear about again? So it's a extremely challenging complex environment. >> So it's that complexity that had led people like Pat Gelsinger to say security is a do-over, and that cyber security is broken. He told me this years ago on theCUBE. And this past VM World we talked to Pat Gelsinger and remember, VMware bought Carbon Black, which is an endpoint security specialist, for $2.1 billion. And he said that he's basically creating a cloud security division to be run by Patrick Morley, who is the Carbon Black CEO. Now, many have sort of questioned and been skeptical about VMware's entrance into the space. But here's a clip that Pat Gelsinger shared with us on theCUBE this past VM World. Let's listen and we'll come back and talk about it. >> And this move in security, I am just passionate about this, and as I've said to my team, if this is the last I do in my career is I want to change security. We just not are satisfying our customers. They shouldn't put more stuff on our platforms. >> National defense issues, huge problems. >> It's just terrible. And I said, if it kills me, right, I'm going to get this done. And they says, "It might kill you, Pat." >> So this brings forth an interesting dynamic in the industry today. Specifically, Steven Smith, the CISO of AWS, at this year's Reinforce, which is their security conference, Amazon's big cloud security conference, said that this narrative that security is broken, it's just not true, he said. It's destructive and it's counterproductive. His and AWS's perspective is that the state of cloud security is actually strong. Kind of reminded me of a heavily messaged State of the Union address by the President of the United States. At the same time, in many ways, AWS is doing security over. It's coming at it from the standpoint of a clean slate called cloud and infrastructure as a surface. Here's my take. The state of security in this union is not good. Every year we spend more, we lose more, and we feel less safe. So why does AWS, the security czar, see if differently? Well, Amazon uses this notion of a shared responsibility security model. In other words, they secure the S3 buckets, maybe the EC2 infrastructure, not maybe, the EC2 infrastructure. But it's up to the customer to make sure that she is enforcing the policies and configuring systems that adhere to the EDIX of the corporation. So I think the shared security model is a bit misunderstood by a lot of people. What do I mean by that? I think sometimes people feel like well, my data's in the cloud, and AWS has better security than I do. Here I go, I'm good. Well, AWS probably does have better security than you do. Here's the problem with that. You still have all these endpoints and databases and file servers that you're managing, and that you have to make sure comply with your security policies. Even if you're all on the cloud, ultimately, you are responsible for securing your data. Let's take a listen to Katie Jenkins, the CISO of Liberty Mutual, on this topic and we'll come back. >> Yeah, so the shared responsibility model is, I think that's an important speaking point to this whole ecosystem. At the end of the day, Liberty Mutual, our duty is to protect policyholder data. It doesn't matter if it's in the cloud, if it's in our data centers, we have that duty to protect. >> It's on you. >> All right, so there you have it from a leading security practitioner. The cloud is not a silver bullet. Bad user behavior is going to trump good security every time. So unfortunately the battle goes on. And here's where it gets tricky. Security practitioners are drowning in a sea of incidents. They have to prioritize and respond to, and as you heard Robert Herjavec say, the average large company has 75 security products installed. Now, we recently talked to another CISO, Brian Lozada, and asked him what's the number one challenge for security pros. Here's what he said. >> Lack of talent. I mean, we're starving for talent. Cyber security's the only field in the world with negative unemployment. We just don't have the actual bodies to actually fill the gaps that we have. And in that lack of talent CISOs are starving. We're looking for the right things or tools to actually patch these holes and we just don't have it. Again, we have to force the industry to patch all of those resource gaps with innovation and automation. I think CISOs really need to start asking for more automation and innovation within their programs. >> So bottom line is we can't keep throwing humans at the problem. Can't keep throwing tools at the problem. Automation is the only way in which we're going to be able to keep up. All right, so let's pivot and dig in to some of the ETR data. First, I want to share with you what ETR is saying overall, what their narrative looks like around spending. So in the overall security space, it's pretty interesting what ETR says, and it dovetails into some of the macro trends that I've just shared with you. Let's talk about CIOs and CISOs. ETR is right on when they tell me that these executives no longer have a blank check to spend on security. They realize they can't keep throwing tools and people at the problem. They don't have the bodies, and as we heard from Brian Lozada. And so what you're seeing is a slowdown in the growth, somewhat of a slowdown, in security spending. It's still a priority. But there's less redundancy. In other words, less experimentation with new vendors and less running systems in parallel with legacy products. So there's a slowdown adoption of new tools and more replacement of legacy stuff is what we're seeing. As a result, ETR has identified this bifurcation between those vendors that are very well positioned and those that are losing wallet share. Let me just mention a few that have the momentum, and we're going to dig into this data in more detail. Palo Alto Networks, CrowdStrike, Okta, which does identity management, Cisco, who's coming at the problem from its networking strength. Microsoft, which recently announced Sentinel for Azure. These are the players, and some of them that are best positioned, I'll mention some others, from the standpoint spending momentum in the ETR dataset. Now, here's a few of those that are losing momentum. Checkpoint, SonicWall, ArcSight, Dell EMC, which is RSA, is kind of mixed. We'll talk about that a little bit. IBM, Symantec, even FireEye is seeing somewhat higher citations of decreased spending in the ETR surveys and dataset. So there's a little bit of a cause for concern. Now, let's remember the methodology here. Every quarter ETR asks are you green, meaning adopting this vendor as new or spending more? Are you neutral, which is gray, are you spending the same? Or are you red, meaning that you're spending less or retiring? You subtract the red from the green and you get what's called a net score. The higher the net score, the better. So here's a chart that shows a ranking of security players and their net scores. The bars show survey data from October '18, July '19, and October '19. In here, you see strength from CrowdStrike, Okta, Twistlock, which was acquired by Palo Alto Networks. You see Elastic, Microsoft, Illumio, the core, Palo Alto Classic, Splunk looking strong, Cisco, Fortinet, Zscaler is starting to show somewhat slowing net score momentum. Look at Carbon Black. Carbon Black is showing a meaningful drop in net score. So VMware has some work to do. But generally, the companies to the left are showing spending momentum in the ETR dataset. And I'll show another view on net score in a moment. But I want to show a chart here that shows replacement spending and decreased spending citations. Notice the yellow. That's the ETR October '19 survey of spending intentions. And the bigger the yellow bar, the more negative. So Sagar, the director of research at ETR, pointed this out to me, that, look at this. There are about a dozen companies where 20%, a fifth of the customer base is decreasing spend or ripping them out heading into the year end. So you can see SonicWall, CA, ArcSight, Symantec, Carbon Black, again, a big negative jump. IBM, same thing. Dell EMC, which is RSA, slight uptick. That's a bit of a concern. So you can see this bifurcation that ETR has been talking about for awhile. Now, here's a really interesting kind of net score. What I'm showing here is the ETR data sorted by net score, again, higher is better, and shared N, which is the number of shared accounts in the survey, essentially the number of mentions in that October survey with 1,336 IT buyers responded. So how many of that 1,300 identified these companies? So essentially it's a proxy for the size of the install base. So showing up on both charts is really good. So look, CrowdStrike has a 62% net score with a 133 shared account. So a fairly sizable install base and a very high net score. Okta, similar. Palo Alto Networks and Splunk, both large, continue to show strength. They got net scores of 44% and 313 shared N. Fortinet shows up in both. Proofpoint. Look at Microsoft and Cisco. With 521 and 385 respectively on the right hand side. So big install bases with very solid net scores. Now look at the flip side. Go down to the bottom right to IBM. 132 shared accounts with a 14.4% net score. That's very low. Check Point similarly. Same with Symantec. Again, bifurcation that ETR has been citing. Really stark in this chart. All right, so I want to wrap. In some respects from a practitioner perspective, the sky erectus is falling. You got increased attack surface. You've got exploding number of IP addresses. You got data distributed all over the place, tool creep. You got sloppy user behavior, overwork security op staff, and a scarcity of skills. And oh, by the way, we're all turning into a digital business, which is all about data. So it's a very, very dangerous time for companies. And it's somewhat chaotic. Now, chaos, of course, can mean cash for cyber security companies and investors. This is still a very vibrant space. So just by the way of comparison and looking at some of the ETR data, check this out. What I'm showing is companies in two sectors, security and storage, which I've said in previous episodes of breaking analysis, storage, and especially traditional storage disk arrays are on the back burner spending wise for many, many shops. This chart shows the number of companies in the ETR dataset with a net score greater than a specific target. So look, security has seven companies with a 49% net score or higher. Storage has one. Security has 18 above 39%. Storage has five. Security has 31 companies in the ETR dataset with a net score higher than 30%. Storage only has nine. And I like to think of 30% as kind of that the point at which you want to be above that 30%. So as you can see, relatively speaking, security is an extremely vibrant space. But in many ways it is broken. Pat Gelsinger called it a do-over and is affecting a strategy to fix it. Personally, I don't think one company can solve this problem. Certainly not VMware, or even AWS, or even Microsoft. It's too complicated, it's moving too fast. It's so lucrative for the bad guys with very low barriers to entry, as I mentioned, and as the saying goes, the good guys have to win every single day. The bad guys, they only have to win once. And those are just impossible odds. So in my view, Brian Lozada, the CISO that we interviewed, nailed it. The focus really has to be on automation. You know, we can't just keep using brute force and throwing tools at the problem. Machine intelligence and analytics are definitely going to be part of the answer. But the reality is AI is still really complicated too. How do you operationalize AI? Talk to companies trying to do that. It's very, very tricky. Talk about lack of skills, that's one area that is a real challenge. So I predict the more things change the more you're going to see this industry remain a game of perpetual whack a mole. There's certainly going to be continued consolidation, and unquestionably M&A is going to be robust in this space. So I would expect to see continued storage in the trade press of breaches. And you're going to hear scare tactics by the vendor community that want to take advantage of the train wrecks. Now, I wish I had better news for practitioners. But frankly, this is great news for investors if they can follow the trends and find the right opportunities. This is Dave Vellante for Cube Insights powered by ETR. Connect with me at David.Vellante@siliconangle.com, or @dvellante on Twitter, or please comment on what you're seeing in the marketplace in my LinkedIn post. Thanks for watching. Thank you for watching this breaking analysis. We'll see you next time. (energetic music)

>>live from Miami, Florida It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, everybody. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>Welcome back to Las Vegas. Everybody. You're watching the cube, the leader in live tech coverage. This is day two of UI path forward UI pass, third North America event and we're excited to be here. This is our second year here. Daniel DNAs is here. He's the CEO of the rocket ship known as UI path. Welcome back to the cube. Great to see you again. >>Thank you. Thank you for inviting me here. >>Oh, so it's our, it's our pleasure and it's been great to be able to document this and we've been saying all week that we see the ecosystem developing the customer base, that UI path very reminiscent of some of the very successful companies that we've seen. But we've never seen a company sort of growing this fast. I have to start with you. Our big idea person kind of go big or go home mentality. But did you really see it getting here so fast? >>Well we, we kind of see it a year ago going here. I can not say that. I've seen it five years, five years ago, I couldn't see, I couldn't see me even in front of a hundred people speaking not to talk about 3000 like can close today, yesterday. >>Well, it's gotta make you very happy. You set it up on stages. When you see your saw the software that you developed, your, you're a developer, you're a coder affecting people's lives. The way some of the examples that you gave, it was a little tear in your eye maybe out of saying, but how to tug at your heart a little bit. That's got to be as a developer and of course now CEO, that's gotta be very gratifying to see your technology have an impact on people's lives. >>Okay, well I can tell you it is a really gratifying, in the end it's, um, we, we, we've built technology, you know, to, first of all, we are proud as engineers to build the best technology that we can, but it's, uh, it makes us a lot more, it's a lot more touching seeing that you can help humans to become better, to become healthier, to even save lives, to help refugees. It's a, it's an amazing feeling. It's when >>I talk to people about robotic process automation, most people don't, don't really are connected and they'll say things to me like, really is there that much room for automation? We've been in the computer industry for 50 years, we've been automating everything back office, front office. How much more room is there? And you put forth the premise last night in your keynote essentially said technology is actually created inefficiencies that >>despite all the automation that we've had now we have all these processes that can be improved. So necessarily the first time I had heard that put forward. I guess my question is, so technology got us into this problem, can technology get us out? >> Yeah. Um, first of all, I'm a software engineer, so I didn't believe there are so many inefficiencies in within the business world. I fought the law. Gender prizes should have been automated completely. Everything should run as move, as ineffectually. But in reality, the alert is far away from this. And as I said yesterday, email and a pro plus activity tools, especially spreadsheets and line of business application has changed completely. How we perform or in front office and back office. But uh, it's, it's a lot of skit work because it's, it's work created when people build business processes, they work with different systems and they always touch the system by looking at the user interface of the, by looking at human readable interfaces of these systems and uh, and when you go and automate them it's kind of difficult to translate into a BIS. >>So where are the at the on the field. So our approach is just through replicates Q months using the same tools, the same thing. Knowledge is that thought for media to business people building and the it's the only way that can work at scale. Of course you can take one particular process, build an it project flow developers with them be successful but you cannot do it. The large scale that an enterprise has, it's the only technology that can work at the large scale. Like I believe in the transportation industry, self-loving cars are the only solution to the industry or not. It's not feasible to say I will build much larger freeways. No, you put self driving cars or self driving trucks driving in the night on the freeway and this is how you will free daily, you know, everything else for the norm of agriculture. Same sort of concept. >>Like there's nothing, I can't make more land. Right? But as you grow your company, um, you guys growing so fast, are you able to use automations to support that growth? I'm sure there are some inefficiencies in it because there's a pace of growth. Helped me understand that this is, this is our story. So way we've built initial finance processes, finance, HR, procurement processes in the very manual slides using people and then scaling up when each a point where we've become a big consumers of all our own technology. It's not, it's not about what we use the most modern systems in the world. It's not a vote that they are not integrated. It's about all the, all the words build by this business people, all the reports that we are creating or all this stuff required a lot of work. We have automated more than a hundred thousand manual hours within you iPod today. >>A mother company built on the best technology stick, all that. Do you feel, feel like that's part of the reason why you've been able to grow so fast? Maybe faster than other historical examples of software companies? Systems are one thing way we weren't able to grow as fast by couple of reasons. First of all, we went global from day one. We were not the typical Silicon Valley company that says I will win in North America and then I will replicate this model across the world because they lose about three to five years in Muslim America just Lang to perfect the machine at least at least then we just went when globally they want an hour. It helps because we can make a business case easy so we can, we can go into a lot of gentle price, show them how it works and it does not require such a huge investment. >>That list to get started and second is it's evolved our culture. We put the big emphasize in keeping our culture customer focus and we put humility as the core. Then that evolved culture and I, I know it might sounds a bit pretentious to see, we put humility, but it's a humility that gives you a, a great, great framework of how you operate. You can, it makes you listen to people, it makes you able to change your mind. It makes you actually accelerate because people that change their mind or they look to find foster better solution that people that are stuck and they need a lot of data until they make, because they are afraid of losing face in making decision. So it's something that works. So it's uh, it's this, those two things combined gave us this cake. It's very interesting you say that because there's a lot of ways to skin a cat. >>Um, many companies have succeeded with extremely dogmatic approach. I mean, I would argue Microsoft, much of its success was it was built around personal productivity, you know, or bust. Um, yet your philosophy is be more open minded. You're humble. Listen to the customers change fast if necessary. Kind of a different philosophy maybe than some others have used in the past. I believe that our philosophy is, is helping us, I don't know, maybe a Microsoft has change. Yeah, exactly that. Satya. So, so it's uh, it's not, so I think this is, this is built in the fabric of how humans operate. We talk to other humans, we learned their needs and then we address their needs. I think it's arrogance to say, I know your boss, I will do this is what you have to do. Like many more traditional software companies are doing, we were very fortunate to build these products by listening to customers. >>That's, that's luck. You don't have to find product market fit. Listen to customers. Market is big. Bring what they want. Well the funny thing is, you know, we talking about the analysts meeting and I do remember you, you're there the other day. You said that you made a bunch of mistakes early on that you got ahead a build it and they will come a mentality. You've kind of built it and then you had to go out, listen to people and figure out how to apply it. Right. Actually I've been using a lot of parallels to service now. It's kind of right. Fred Luddy did, he built, he built a platform and then the VC said, well what do we use it for? He goes, anything good? He had to go and talk to people into the route. Okay, how do I apply it? But you said, well kind of made some mistakes early on, but you recovered from those mistakes by listening. >>It sounds like the definitely in the bill. Coming from a software engineer background, I, uh, I have, uh, at least I had the tendency to don't give enough credit to sales, to marketing even to the customers it was, we clearly understand the customers in the, so we build technology for the sake of technology. So we were really fortunate to have some MALDI customers. We didn't understand how because I fought that custom was, should all to themselves to test and find the best technology out there and just go there. I was really kind of, I had a lot of blind sports, so on how this world operates, but after I've stopped it to visit customers and understand their pain points and their requests actually realize they are smarter than us in using our own technology because they use it in the real world. So then message that that completely transformed my thinking. So I went back to my engineering team, sunlight, unlike the one guys on this day, I don't want to ever hear, we don't fix bugs and we do features and we do this. When the customers say, you do this, you say, thank you, thank you for showing me the light. I will do this. That's, that makes me create a better broad your feet >>back as a gift. The feedback is a gift. So I want to ask you about the statement you made yesterday in your keynote about we are cloud first and you announced a SAS capability today. I said I signed up, took me seconds, and now I've got to do some work to invite some other people and start doing some automations. But when you were in your apartment in Bucharest or wherever you started the company, why not cloud then? >>Most of our customers are still on prem. So way we have to be where customers are with the clouds first four years ago we wouldn't be here today. Oh. So we started close to the customers way and learn a lot from really large customers that thought a bit more reluctant to go into cloud and now as I think all in all in life is about timing. I think it's the right time timing to benefit the other segments of the market and allow for automation on demand was the infrastructure. Bryce, that people that are still on prem pay are huge. Compare some in some companies only to provision a server would be like 200 K period one time on. Then you have people to maintain them. Offering a many surveys by us in our own cloud looking at the best, you know, we create the best infrastructure, most efficient. We have the best people understanding our technology. We're seeing it. I think it's a great business proposition, but now we were ready to do it. >>Well, plus it sets up potentially new pricing models, you know, consumption based pricing models. You hear a lot of, a lot of row, a lot of bots, uh, are, are sitting idle as a customer. Help just charge me when I'm using violet, thinking of, you know, the serverless and functions. But this is possible only with economy of scale. So the cloud is, you're going to your cloud, you're not going to build it on Azure or AWS or you guys may use, we'll use Bob Lee Clow shows, which is infrastructure. You just have this look Chelios. Yeah. Okay. Um, I'm going ask you about, uh, IPO. Um, what, can you share with us your thoughts? You know, the window seems to be closing a little bit different, right? You know, Uber's and now Slack, you know, not such a successful. And what are your thoughts on IPO? Well, I think that the enterprise software >>companies were actually pretty successful in IPO and this year. And they have one of the, you know, a lot of just multiples that we have. We're seeing. So you cannot compare marketplace companies like who you are or Lyft to enterprise software. So I think for a good enterprise software company, they will always be a place to land a good IPO regardless of timing. Timing is, doesn't work for us. We are still, we are still a young company in many ways. We are 40 years old company. So it will be one of the yellow most earliest IPO. Very, very, very early. We need the bit, we need more at least one year. Like we want do an IPO in 2020, but we've been here the 2021 would be a good year for that. Depends on the climate, but we have met on the client, you have them, you're very well capitalized. Right? It's not like you need to do upsell Kevin the motivation and we still have five would bribe private Gabby. The markets are very frothy so you can still raise a lot of money and very good volume. >>Right. So the motivation for IPO is, is what awareness maybe for the employee. >>Yeah. Exited for the employees. And, uh, you'll just get to a size where you cannot be prideful. And most of our customers are public and they are much more comfortable dealing with the public. >>Yeah, for sure. It's part of your transparency edict, but I mean, well a lot of companies that have raised a ton of dough at the Cloudera for example, waited and waited and waited and then, you know, they go public. It's like, then the public doesn't get to participate in the upside. So I'm sure you're having those conversations thinking about it though. You know, the little guy wants to invest too and you're like, yeah, why not, right? Yeah. So let's go this. It's very exciting times and as you say, it depends on the time and we'll see what happens with the 2020 election who can, who can predict those things. But, so I want to ask you about the Capitol because software is a very capital efficient marketplace, but, but we see companies, you know, you included raising hundreds of millions, sometimes a billion plus dollars. Why such large raises? Where do you see that going? You mentioned engineering, you'd have plenty of money to do engineering. Is it really promotion? We tried to get to escape philosophy. We >>build a big market and we have invested in a mode in order to, if you go fast, well let's take cold car. Okay, the fosters or car go, the more guess it consumes. Right, so you need, if you want to comprise the time, it's costly, but that helps you extend much faster when when large markets and build a large bill, really a large company. In the short time, we could have been much more efficient if we, instead of four years, we would have built this company in 10 years. Many companies, if they would reach our size in 10 years, I will still be happy, but we've done it in four instead of 10 and then it was if you have unit capital to grow fast, >>I think it's the right approach because I do think there's going to be consolidation in this market and I think the company that achieves escape velocity and you are the favorite to do that now, we'll do very, very well. I think the market's much larger than the market forecast suggests. I think the Tam is way, way, way under, and again, we call this on service now as well. We saw this early on at the core. People tell how the core is really not that big, but, but the, but the adjacencies and the potential market is, it's, it's, it's way more than 16 billion or whatever that number is you showed. I think it's, it's, it's, it's 30 40 you know, perhaps even even bigger. >>I think as people realize that this is the really, the only way you can achieve automation on this, a smaller type of processes, but large volume, I think they will. They will go more and more. >>Well then, I know you're super busy and you've got to go. Thanks so much for coming again. Thank you guys for watching. Keep it right there. We'll be right back. Right after this short break. You're watching the cube from UI path forward three right back.

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

>> Announcer: Live from Las Vegas, it's The Cube, covering Interconnect 2017, brought to you by IBM. >> Okay, welcome back everyone. Here, live in Las Vegas for IBM Interconnect 2017, this is SiliconANGLE's The Cube's coverage. Three days, a lot of great interviews, more in day two here. I'm John Furrier, my co-host Dave Vellante, our next guest is Tanya Seajay, founder and CEO of Orenda Software Solutions. Welcome to The Cube. >> Thank you so much. >> So, your company does a lot of cool things with data. One of the things, obviously, in the news, you can't read a story these days without hearing something about Trump, Uber, bad behavior. >> Dave: Fake news. >> Fake news, there's always scandal. It's the internet, for crying out loud. Everything's going on, but reputation now is measurable and data is out there and companies now as they go on to digital as a medium end to end for marketing and engaging customers, they got to be careful. What's your take? What's going on in this marketplace? >> There's a couple of things that are happening simultaneously. One is, we talked about this just briefly, the Edelman Trust Barometer. It's a global survey that's done every year, and it started I believe in 2010. In 2017, the findings were that we are in a trust crisis globally, and you would have heard that from Marc with Salesforce today. That's what he was referencing. At the same time, PricewaterhouseCooper came out with another survey across North America, and it was that we are in the midst of a trust economy and trust is growing. So, at one point, we used to make our buying decisions on whether or not a product was convenient or a good sale price, those kinds of things. Now, we want to know whether or not we trust the brand, whether or not we trust the CEO, and whether or not the companies have purpose. So, our buying decisions are changing, so not only are we in a trust crisis but we are also a trust economy. So, measuring trust is exceptionally important and a value to all brands globally. >> This purpose thing is interesting. We've been seeing the same thing, and we just had South by Southwest, Intel. We were headlining the Intel AI Lounge, and they had this program, AI for Social Good, which has got a great program. It's on our YouTube channel, youtube.com/siliconangle, folks that are watching, but there's a counterculture going on right now, we're seeing in this world. The younger audience is coming in, the new generation, the digital natives. They're living in a digital world 100%, so there seems to be a counterculture of anti-what it was, pre-now, internet, what it was before, trolling, all this stuff's been around for a while. But you're starting to see people really focus in on good and mission purpose. There's an element where there's a new generation saying, we want to apply tech for good, and you're seeing it with equality, they mentioned a lot of things on the stage today. But beyond that, it's kind of this post-9/11 generation where, like, hey what are you, all you old people bickering about? Just do social good. I mean, do you seeing that too? We're seeing a lot of it across the board. Can you share any stories in this area? >> Yeah, social good is really important in terms of giving back to your community, and in the communities where you do business, you want to have that connection. So, when we were creating Orenda, the software that measures trust, it also measures a few other things. We went back into about 30 years of research in social science and selected, there are six key factors to a healthy relationship, and what we were calling corporate social responsibility is now just more or less social good. So, you want to do things that are good to the communities that you do business in, and there's also the exchange of benefits. I do something for you, you do something for me, which brings in the more collaborative systems and partnership ecosystems that exist. >> It's a community model, too. With open source growing, connected internet, everyone's connected to each other. That's a community framework. >> That's right. >> And that's kind of the, seems to be the trend. >> It is a trend, and at one point, companies used to market to their customers. Now, you see something quite different. Customers are empowered and they're engaging through content so the exchange is continuous. One of the examples we have is with Apple. So, every time your heart beats, someone is talking about Apple. It is so huge. >> The velocity, you mean the velocity. >> Yeah, just the velocity. There's so much information coming out. We were following 25 different companies in December, and we pulled in five million data points. So, that's the amount of information that is coming at us and at brands at any particular time. What we need to do was turn that into insights in real time. If not, it's useless. >> It's interesting you mention Apple. So, we have a data science group within SiliconANGLE, The Cube. We call it our cognitive beta program. We haven't released it yet, but we're looking at all the Twitter data and we can actually see all the tweets. And then, we can extrapolate the users and obviously get all the data, which phone they're using, tweeting from. And that came out, you saw Trump was on an Android, an iPhone. And here at this show, based on the data that we have, 76% of this audience, here and online, is iPhone over Android. So, you say, okay, big deal, ho-hum. Actually, demographically, it matters. Now, some shows, the more geeky shows, you'll see Android over iPhone, so it's a small little data point. But you can almost, like that movie Contact, where you open up one door, you can get all those different insights. So, a small data point like that could add to other data. >> It could, and it's unlocking it, like you said, that is the most important part. You can get all this data. You can get it continuously. But unlocking it and telling everybody what it means to them, and it can mean something different depending on what kind of solution or problem that you're trying to overcome. But yeah. >> Yeah, and the other concepts we follow a lot in the big data world is data at rest and data in motion. And Dave and I were just at breakfast this morning, talking about content and motion brands and motion. So, your company really is measuring the brand in motion, right? >> That's right. >> So, this is kind of a cool new cutting edge coolness. >> It's really cool. >> Explain what's going on there. What's the cutting edge tech? What are some stories? Good, bad, and the ugly? >> One of the interesting things that we just did is we were following five of the biggest banks in Canada, and at the same time, CBC, which is the national broadcasting company, did this go public article and it was extremely negative. And we were tracking them, so we were able to show in real time the trust levels dropping. And in correlation to that, we looked at the stock prices of those companies, and they were also dropping. So, to be able to demonstrate that the brand itself, the reputation, particularly trust, was what the issue was, and that makes a lot of sense. It's money, it's banks, it's trust. That's what's going to be impacted the most. But being able to correlate that, it's a piece of information that we haven't been able to use before. >> So, that's insight. So now, the actionable insight is, wow we should send someone in there digitally, parachute into the virtual news cycle, and provide content or perspective. I'm saying, they can get in, stop that bleeding. >> Get in and stop the bleeding. And the other thing is that they were five national banks, but only one of them was taking the hit for it. They were the actual face of the issue. So, to be able to say, we're all being hit by this particular news story, yes, but you're being hit the most. >> It's a classic public relations problem. If you don't react, then it gets settled in, it becomes a matter of fact. >> Yeah, so you need to be able to deal with that escalation in real time. >> So, what do you guys do that's different than, a lot of sentiment analysis and it's kind of an overcrowded space. >> Tanya: It's a busy space, yeah. >> What's unique in what you guys do? >> What's unique is the actual social science on top of that. So, there is positive, negative, which gives you a little bit of information. What we did is just put on a whole other filter, and we use social science to do that. So, in order to show the brand momentum that needs to exist for a more resilient company, we said we need to know whether or not trust is increasing or decreasing, commitment with the brand or loyalty to the brand is increasing or decreasing. This is really important information. Positive, negative just doesn't tell you enough. So, when you are doing your messaging from a public relations point, you know to talk about integrity if there's a trust issue that you're dealing with. If it's satisfaction, then it's something that you want to do better in terms of a particular product. So, you get to focus on what the actual problem is, so that's how we're absolutely unique, is that we're able to measure emotion in a very different way, through social science and key factors that need to exist for a healthy brand. >> And the secret sauce behind the tech is what? Is it some cognitive, it's data science? >> We do a couple of things. So, one of the reasons why we partnered with IBM and are using Watson, the APIs, is that we built our own algorithms and we have it interact with a huge dictionary of words that we use. And we had to be able to customize that because the way we use language is always different. The way we talk about oil gas is different than we would talk about Coca-Cola, say. So, we had to be able to customize the dictionary so that if we use the word recall with a car manufacturer, that's extremely negative. But recall within the healthcare system is probably neutral. So, we had to be able to make those differences. So then, we also use AI. We use the Personality Insights tool within Watson, so we can take a whole customer buying group, look at them as an individual's huge amounts of data, millions and millions of data points, and say this is what this particular customer group or stakeholder group, this is what they need as a group, this is what they value, these are their key personalities. So again, you just get that deeper insight into who's buying your product. >> And the data sources? Talk about where the data comes from. >> The data comes from social media, and why that's really important is because within public relations and communications, there's always been focus groups, right, where you try to pull out insights into our brand from focus groups or surveys. >> Weeks and weeks and weeks of research. >> Right? Weeks and weeks of research. And you still have just a certain amount of data that you get to deal with. This, we treat social media as a huge focus group with tremendous amounts of data, tremendous amounts of insights, and we can pull it out in real time. So, if there's an issue that is escalating, we can say this is what your customer base is saying about you, this is how the impact is. We don't have to go through months of research to deal with an issue we need to deal with within 10 minutes, usually. >> So, Twitter's obviously a huge source of data, is that correct? >> Twitter's huge. >> 'Cause it's so real time and there's so much of it. What other sources? Is that the primary or a primary source? >> Facebook is interesting. You can get public information, but you can't private. Instagram is another. Blogs are a great source of information as well. Almost any online information where there's engagement, so there's a conversation that's taking place. If it's static, it doesn't, it doesn't really have an impact on you, right? >> Is there third party data sources that you use that other people use as well? Is it Twitter Firehose? Is it RSS feeds? Is there like a syndicate of data sources? >> We use GNIP, so that's owned by Twitter. Yeah, that's what we use. >> But for blogs, how do they get the blogs? >> You scrape them. >> So you scrape them. So RSS feeds and. >> Yeah, and I really enjoy the fact that a lot of governments are going into open source data, so the more we get, the better it is. We have a couple of relationships, partnerships with national media sources as well, so we're able to use that and go back into time, thankfully, from their end. >> Tanya, what's the coolest or weirdest discovery you've made with the data? Because as you get all this gesture data, I'm sure there's some things that just, whoa. >> One of the funnest for me, I'm a bit of a political nerd, and so I really, really enjoy politics. And when we were building out Orenda, we used the federal election in Canada, and yes we did do some with the US election too, but it was so much data, it was. (John and Dave laugh) >> John: Big tsunami. >> Yeah, thanks a lot, John. >> It's not stopping by the way either. It's continuing to go on. >> But yeah, the funniest moment, that one, just as an aside, was the whole, would you rather have Trump or a mozza stick as president, which was, really gained popularity. But for the federal election, what we did was follow the four federal candidates, and we were able to show when we stopped as a nation talking about Thomas Mulcair as the next leader and when we started talking about Justin Trudeau. And we were able to predict that Justin Trudeau's brand was building momentum, weeks before the polls came out and said that the machine changed. >> This year's contender. Alright. Well, Tanya, thanks so much for coming on The Cube. Really appreciate it. I love what you guys do. I think that's, you're on the cutting edge of really compelling social science, and as the culture deals with autonomous driving cars and smart cities, I think this is going to be an ongoing field of study of understanding the relationship between data and humans with respect to societal changes. So, again, this is I think one small use case of really an exploding area. So, thanks for sharing. It's The Cube here live in Las Vegas. For more Interconnect coverage, after this short break, I'm John Furrier with Dave Vellante. We'll be right back. Stay with us.

>> Commentator: Live from Las Vegas. It's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Hello everyone. Welcome to theCUBE special broadcast here at the Mandalay Bay in Las Vegas for IBM InterConnect 2017. This is IBM's big Cloud show. I'm John Furrier. My co-host, David Vellante for the next three days will be wall-to-wall coverage of IBM's Cloud Watson. All the goodness from IBM. The keynote server finishing up now but this morning was the kickoff of what seems to be IBM's Cloud strategy here with Dave Vellante. Dave, you're listed in the keynote, we are hearing the presentation. We had the General Manager/Vice President of Data from Twitter on there, Chris Moody, talkin' about everything from the Trump presidential election being the avid tweeter that he is and got a lot of laughs on that. To the SVP of Cloud talking about DevOps and this is really IBM is investing 10 million dollars plus into more developer stuff in the field. This is IBM just continuing to pound the ball down the field on cloud. Your take? >> Well IBM's fundamental business premise is that cognitive, which includes analytics, John plus cloud plus specific industry solutions are the best way to solve business problems and IBM's trying to differentiate from the other cloud guys who David Kenny was on stage today saying, you know, they started with a retail business or the other guys started with search, we started with business problems, we started with data. And that's fundamental to what IBM is doing. The other point, I think is-- the other premise that IBM is putting forth is that the AI debate is over. The Artificial Intelligence, you know, wave of excitement in the 70s and 80s and then, you know, nothing is now back in full swing. An AI on the Cloud is a key differentiator from IBM. In typical IBM fashion for the last several Big Shows, IBM brought out not an IBMer but a customer or and or a partner. And today it brought out Chris Moody from Twitter talking about their relationship with IBM but more specifically the fact that Twitter's 11 years old. Some of the things you're doing with Twitter obviously connected into March Madness and then Arvind Krishna who has taken over for Robert LeBlanc as the head of the Cloud group, talked about IBM, AI, IBM's Cloud, blocked chain, trusted transactions, IoT, DevOPs, all the buzz words merged into IBM's Cloud Strategy. And of course, we reported several years ago at this event about Bluemix as the underpinning of IBM's developer strategy. And as well it showcased several partners. Indiegogo was a crowdfunding site and others. Some of those guys are going to be in theCUBE. So. You know as they say, this AI debate is over. It's real and IBM's intent is to the platform for business. >> Dave, the thing I want to get your thoughts on is IBM's on a 19 consecutive quarters of revenue problems with the business on general but they've been on a steady course and they kind of haven't wavered. So it's as if they know they got to shrink to grow approach but we just came off the heels of Google Next which is their Cloud Show. How the Amazon is on re-invent as the large public cloud but the number one question on the table that's going to power IoT, that's going to power AI, is the collision between cloud computing and IoT, cloud computing in big data I should say is colliding with IoT at the center which is going to fuel AI and so, it brings up the question of enterprise readiness. Okay? So this is the number one conversation in the hallways here at Las Vegas and every single Cloud Show in the enterprise is, can I move to the cloud? Obviously it's a hybrid world, multi-cloud world. IBM's cloud play. They had a Cloud. They're in the top four as we put them in there. Has to be enterprise ready but yet it as to spawn the development side. So again, your take on enterprise readiness and then really fueling the IoT because IoT is a real conversation at an architectural level that is shifting the-- tipping the scales if you will for where the action will be. >> Well John, you and I have talked in theCUBE for years now. Going on probably five years that IBM had to shrink to grow. They've got the shrink part down. They've divested some of its business like the x86 business and the microelectronics business. They have not solved the grow problem. Let's just say 19 straight quarters of declining revenue. But here's the question. Is IBM stronger today than it was a year ago? And I would argue yes and why is that? One is its focus. Its got a much clearer focus on its strategy around cognitive, around data and marrying that to Cloud. I think the other is as an 80 billion dollar company even though it's shrinking, its free cash flow is still 11.6 billion. So it's throwing off a lot of cash. Now of course, IBM made those numbers, made its earnings numbers by with through expense control, its got lower tax right. Some of the new ones of the financial engineering. Its got some good IP revenue. But nonetheless, I would still argue that IBM is stronger this year than it was a year ago. Having said that, IBM's service as business is still 60% of the company. The software business is still only about 30% into it but 10% is hardware. So IBM-- people say IBM has exited the hardware business. It hasn't exited completely the hardware business but it's only focusing on those high value areas like mainframe and they're trying to sort of retool power. Its got a new leader with Bob Picciano but it's still 60% of the company's business is still services and it's shifting to a (mumbles) model. An (mumbles) model. And that is sometimes painful financially. But again John, I would argue that it is stronger. It is better positioned. And now its got some growth potential in place with AI and with, as you say, IoT. We're going to have Harriet Green on. We're going to have Deon Newman on. Focusing on the IoT opportunity. The weather company acquisition as a foundation for IoT. So the key for IBM is that it's strategic imperatives are now over 40% of its business. IBM promised that it would be a 40 billion dollar business by 2018 and it's on track to do that. I think the question John is, is that business as profitable as its old business? And can it begin growing to offset the decline in things like storage, which has been seeing double digit declines and its traditional hardware business. >> So Dave, this is to my take on IBM. IBM has been retooling for multiple years. At least a five year journey that they have to do because let's just go down the enterprise cloud readiness matrix that I'm putting together and let's just go through the components and then think about what was old IBM and what's new. Global infrastructure. Compute networking, storage and content delivery, databases, developer tools, security and identity, management tools, analytics, artificial intelligence, Internet of Things, mobile services, enterprise applications, support, hybrid integration, migration, governance and security. Not necessarily in that order. That is IBM, right? So this is a company that has essentially (mumbles) together core competencies across the company and to me, this is the story that no one's talking about at IBM is that it's really hard to take those components and decouple them in a fashion that's cloud enabled. This is where, I think, you're going to start to see the bloom on the rose come out of IBM and this is what I'm looking at because IBM had a little bit here, they had a little bit here, then a little stove pipe over here. Now bringing that together and make it scalable, it's elastic infrastructure. It's going to be really the key to success. >> Well, I think, if again if you breakdown those businesses into growth businesses, the analytics business is almost 20 billion. The cloud business is about 14 billion. Now what IBM does is that they talk about as a service runway of you know, 78 billion so they give you a little dimensions on you know, their financials but that cloud business is growing at 35% a year. The as a service component, let's call it true cloud, is growing over 60% a year. Mobile growing, 35%. Security, 14%. Social, surprisingly is down actually year on year. You would thought that would be a growth theory for them but nonetheless, this strategic initiatives, this goal of being 40 billion by 2018 is fundamental to IBM's future. >> Yeah and the thing too about the enterprise rate is in the numbers, it speaks to them where the action is. So right now the hottest conversations in IT are SLA's. I need SLA's. I have a database strategy that has to be multi-database. So (mumbles) too. Database is a service. This is going to be very very important. They're going to have to come in and support multiple databases and identity and role-based stuff has to happen because now apps, if you go DevOps and you go Watson Data Analytics, you're going to have native data within the stack. So to me, I think, one of the things that IBM can bring to the table is around the enterprise knowledge. The SLA's are actually more important than price and we heard that at Google Next where Google tried it out on their technologies and so, look at all the technology, buy us 'cause we're Google. Not really. It's not so much the price. It's the SLA and where Google is lacking as an example is their SLA's. Amazon has really been suring up the SLA's on the enterprise side but IBM's been here. This is their business. So to me, I think that's going to be something I'm going to look for. As well as the customer testimonials, looking at who's got the hybrid and where the developer actually is. 'Cause I think IoT is the tell sign in the cloud game and I think a lot of people are talking about infrastructures of service but the actual B-platform as a service and the developer action. And to me, that's where I'm looking. >> Well comparing and contrasting, you know, those two companies. Google and Amazon with IBM, I think completely different animals. As you say, you know, Google kind of geeky doesn't really have the enterprise readiness yet although they're trying to talk that game. Diane Green hiring a lot of new people. AWS arguebly has, you know, a bigger lead on the enterprise readiness. Not necessarily relative to IBM but relative to where they were five years ago. But AWS doesn't have the software business that IBM has yet. We'll see. Okay so that's IBM's ace in the hole is the software business. Now having said that, David Kenny got on stage today. So he came out and he's doing his best Jeremy Burton impression. Came out in sort of a James Bond, you know, motif and guys with sunglasses and he announced the IBM Cloud Object Storage Flex. And he said, yes we have a marketing department and they came up with that name. You know, this to me is their clever safe objects tour to compete with S3, you know several years late. After Amazon has announced S3. So they're still showing up some of that core infrastructure but IBM's-- the (mumbles) of IBM strategy is the ability to layer cognitive and their SAS Portfolio on top of Cloud and superglue those things together. Along, of course, with its analytics packages. That's where IBM gets the margin. Not in volume infrastructure as a service. >> I want to get your take on squinting through the marketing messages of IBM and get down to the meat and the bone which is where is the hybrid cloud? Because if you look at what's going on in the cloud, we hear the new terms, lift and shift. Which to me is rip and replace. That's one strategy that Google has to take is if you run (mumbles) and Google, you're kind of cloud native. But IBM is dealing a lot at pre-existing enterprise legacy stuff. Data center and whatnot so the lift and shift is an interesting strategy so the question is, for you is, what does it take for them to be successful? With the data platform, with Watson, with IoT, as enterprise extend from the data center with hybrid. >> Well I think that, you know, again IBM's (mumbles) is the data and the cognitive platform. And what IBM is messaging to your question is that you own your data. We are not going to basically take your data and form our models and then resell your IP. That's what IBM's telling people. Now why don't we dig into that a little bit? 'Cause I don't understand sort of how you separate the data from the models but David Kenny on stage today was explicit. That the other guys, he didn't mention Google and Amazon, but that's who he was talkin' about, are essentially going to be taking your data into their cloud and then informing their models and then essentially training those models and seeping your IP out to your competitors. Now he didn't say that as explicitly as I just did but that's something as a customer that you have to be really careful of. Yes, it's your data. But if data trains the models, who owns the model? You own the data but who owns the model? And how do you protect your IP and keep it out of the hands of the competitors? And IBM is messaging that they are going to help you with the compliance and the governance and the (mumbles) of your organization to protect your IP. That's a big differentiator if in fact there's meat in the bone there. >> Well you mentioned data, that's a key thing. I think whether doing it really quickly is getting the hybrid equation nailed so I think that's going to like just pedal as fast as you can. Get that going. But data first enterprise is really speaks to the IoT opportunity and also the new application developers. So to me, I think, for IBM to be successful, they have to continue to nail this data as value concept. If they can do that, they're going to drive (mumbles) and I think that's their differentiation. You look at, you know, Oracle, Azure, Microsoft Azure and IBM, they're all playing their cards to highlight their differentiation. So. Table stakes infrastructures of service, get some platform as a service, cloud native, open source, all the goodness involved in all the microservices, the containers, Cooper Netties, You're seeing that marker just develop as it's developing. But for IBM to get out front, they have to have a data layer, they have to have a data first strategy and if they do that well, that's going to be consistent with what I think (mumbles). And so, you know, to me I'm going to be poking at that. I'm going to be asking all the guests. What do you think of the data strategy? That's going to be powering the AI, you're seeing artificial intelligence, and things like autonomous vehicles. You're seeing sensors, wearables. Edge of the network is being redefined so I'm going to ask the quests really kind of how that plays out in hybrid? What's your analysis going to be for the guests this week? >> Well, I think the other thing too is the degree to-- to me, a key for IBM success and their ability to grow and dominate in this new world is the degree to which they can take their deep industry expertise in health care, in financial services and certain government sectors and utilities, et cetera. Which comes from their business process, you know, the BPO organization and they're consulting and the PWC acquisition years ago. The extent to which they can take that codifier, put it in the software, marry it with their data analytics and cognitive platforms and then grow that at scale. That would be a huge differentiator for IBM and give them a really massive advantage from a business model standpoint but as I said, 60% of the IBM's business remains services so we got a ways to go. >> Alright. We're going to be drilling into it again. There's a collision between cloud and big data markets coming together that's forming the IoT. You can see machine learning. You can see artificial intelligence. And I'm really a forcing function in cloud acceleration with data analytics being the key thing. This is theCUBE. We'll be getting the data for you for the next three days. I'm John Furrier. With Dave Vellante. We'll be back with more coverage. Kicking off day one of IBM InterConnect 2017 after the short break.

>> Announcer: Live from Las Vegas, Nevada it's theCube. Covering, Accelerate 2017. Brought to you by Fortinet. Now, here are your hosts, Lisa Martin, and Peter Buress. (tech music) >> Lisa: Hi, welcome back to theCube. We are Silicon Angle's Flagship Program, where we go out to the events and extract the signal to the noise, bringing it directly to you. Today, we are in beautiful Las Vegas with Fortinet. It's their Accelerate 2017 Event. I'm your host, Lisa Martin, joined by my cohost, Peter Buress. And we're very excited to be joined by a Technology Alliance Partner, Nozomi Networks, Edgard Capdevielle. You are the CEO? >> Yes, that's right. >> And, welcome to theCube. >> Thank you, happy to be here. >> So, a couple of great things that Nozomi announced, just a couple of months ago, one was, they just secured fantastic $7.5 million in Series A Funding. And the second thing they announced was you, as the new CEO, so congratulations on your new post. >> Thank you very much, thank you. >> So, Nozomi is focused on the Industrial Control Systems Industry. What was it about this particular opportunity, that attracted you to want to lead Nozomi? >> Yeah, great question. Two things mainly. One, is the team. The two founders are truly rock stars, they have a great background in Cyber Security, and how do we apply Artificial Intelligence to Industrial Cyber Security. And two was, I had been working with the founders for a little bit, and I saw, with my own eyes, how the customers adopted the technology, how easy it was to deploy in an industrial setting, which tends to have a lot of friction. Not a lot of equipment gets into those networks. And the ease of proof of concepts, I saw it with my own eyes. And the frictionless interactions, made me join. >> So Nozomi was started in 2013, you're already monitoring over 50,000 industrial installations. >> That's right. >> Some of the themes that we've talked about, at the event today, so far, with Fortinet's senior leaders, is the evolution of security, where they're positioning, really at this third generation of that. As we're seeing that, and we're seeing that in order for businesses to digitalize successfully, they have to have trust in that data. What is Nozomi seeing, in terms of your industrial customers? What are some of the biggest concerns that they have, regarding security? And how are you working with Fortinet, to help mitigate or limit damage from cyber attacks? >> A lot of our customers in our space, are going through what's called IT/OT Conversions. OT networks, have traditionally been serial, point to point, run over two step para copper and they've recently adopted ethernet. When you adopt ethernet, you have a gravitational force, which is to connect. So these OT networks used to be air gaps, segregated, and now they're being converged with IT technology, under sometimes, IT operation. And therefore, they start suffering the traditional IT attacks. Those traditional IT attacks, are particularly harmful when it comes to industrial, critical infrastructure. And they require special technology that understands those protocols, to be able to detect anomalies, and white list or black list, certain activities. >> Give some example, of an IOT network. So, what is, you say critical infrastructure, gives us some examples, what are we talking about? >> IOT's a very broad term. We focus very specifically on industrial IOT. >> Or, industrial IOT. >> Industrial IOT, could be a network that controls a refining, so the refining process in a refinery. It could be electrical distribution, any form of electrical generation, oil and gas, upstream or downstream. Manufacturing, everything that moves in manufacturing, is controlled by an industrial control networks. Pharma, in the same subsegment, if you will. Some transportation, we're based in San Francisco, so our barge system is controlled with industrial control systems. >> So, we're talking about, as you say critical infrastructure, we're talking about things that, where getting control of some element of that critical infrastructure, >> Correct. >> Especially in the process manufacturing businesses, can have enormously harmful effects? >> Correct. >> On not only business, but an entire community? >> The disruption that it can cause is tremendous. From lights out in a city, to harm to people, in a transportation case, oil and gas case. Environmental damage, leakage. The damage can be tremendous. And that's basically, one of the huge differences between IT and OT. In IT, if your network blinks, your email may be two seconds late, my print job may need to be resent. In OT, you may not be able to turn off that valve, or stop this process from happening, or receive an alarm in time. >> Right, so like, I live in Palo Alto. Not too from me is, some of the big refineries up in Richmond, California. And not too long ago, they had an OT outage, and it led to nearly a billion dollars worth of damage, to that plant, and to the local environment. >> Correct. >> So this is real serious stuff. >> So with a product like Nozomi, you can detect anomalies. Anomalies come in three flavors. One could be equipment damage, malfunction. The other one could be human error, which is very very common. And the other one could be cyber. Any one of those could be an anomaly, and if it tries to throw the process into a critical state, we would detect that, and that's where ... >> Talking about cyber, from a cyber attack perspective, what is it about industrial control systems that makes them such a target? >> Yeah. It is that they had been used to be isolated networks, just like I said. When IT and OT converges, are taking networks that used to be serial security was not really a concern, in industrial control networks, you don't really have identity, you don't have authentication. You're just starting to have encryption. Basically, if you drop a command in the network, that command will get executed. So, it's about the vulnerability of those. >> Vulnerability, maybe it's an easy target? And then from a proliferation perspective, we mentioned the evolution of security. But, the evolution of cyber attacks, the threat surface is increasing. What is the potential, give us some examples, some real world examples, of the proliferation that a cyber attack, >> That is a great question. >> And an industrial control system, can have on a retailer or a bank, energy company? >> The industry was put in the map in 2010, with Stuxnet. Stuxnet was the first attack, everybody talked about Stuxnet for a while. And it was very hard to create a market out of that, because it was done really by a nation's state, and it was done like once. Since then, 2010, 2013 and now 'til today, attacks have increased in frequency dramatically, and in use cases. Not only are nation states attacking each other, like in the case now of the Ukraine, but now you have traditional security use cases, your malicious insider, you're compromised insiders, doing industrial cyber attacks. In 2015, the Department of Homeland Security reported 295, industrial cyber attacks, in our nation's critical infrastructure. And those are not mandated, they don't have a reporting mandate, so those are voluntary reports. >> Wow. >> So that number, could be two or three times as big. If you think about it, from 2010, we've gone from once a year, to 2015 once per day. So, it's happening. It's happening all the time. And it's increasing not only in frequency, but in sophistication. >> So, it's 295 reported. But there's a bunch of unreported, >> Correct. >> That we know about, and then there's a bunch that we don't know about? >> Correct. >> So, you're talking about potentially thousands of efforts? And you're trying with Fortinet and others, to bring technology, as well as, a set of best practices and thought leadership, for how to mitigate those problems? >> That's right. With Fortinet, we have a very comprehensive solution. We basically combine Fortinet's sophistication or robustness from a cyber security platform, with Nozomi's industrial knowledge. Really, we provide anomaly detection, we detect, like I said, any sort of anomaly, when it comes to error, cyber, or malfunction. And we feed it to Fortinet. Fortinet can be our enforcement arm if you will, to stop, quarantine, block, cyber attacks. >> So, Nozomi's building models, based on your expertise of how industrial IOT works, >> That's right. >> And you're deploying those models with clients, but integrating the back into the Fortinet sandbox, and other types of places. So, when problems are identified, it immediately gets published, communicated to Fortinet, and then all Fortinet customers get visibility into some of those problems? >> We connect with Fortinet in two ways. One, is we have 40 SIM, so we alert everybody. We become part of the information, security information environment. But we also used Nozomi Fortigates, to block, to become active in the network. Our product is 100% passive. We have to be passive to be friendly deployed in industrial networks. But, for the level of attack or the level or risk is very high, you can actually configure Fortinet to receive a command from Fortinet, and from Nozomi, and actually block or quarantine a particular contaminated node, or something like that. Does that make sense? >> Oh, totally. Makes 100%, because as you said, so you let Fortinet do the active work, of actually saying yes or no, something can or cannot happen, based on the output of your models? >> That's right. Yep. >> So, when you think about IOT, or industrial IOT, there's an enormous amount of investment being made of turning all these analog feeds, into digital signals, that then can be modeled. Tell us a little bit about how your customers are altering their perspective on, what analog information needs to be captured, so that your models can get smarter and smarter, and better and better at predicting and anticipating and stopping problems. >> When it comes to industrial models, you need to pretty much capture all the data. So, we size the deployment of our product based on the number of nodes or PLC's that exist in an industrial network. We have designed our product to scale, so the more information or the more number of nodes, the better our models are going to be, and our products will scale to build those models. But, capturing all the data is required. Not only capturing, but parsing all the data, and extracting the insides and the correlations between all the data, is a requirement for us to have the accuracy in anomaly detection that we have. >> What is the customer looking at in terms of going along that, that seems like an arduous task, a journey. What does, and you don't have to give us a customer name, but what does that journey look like, working together with Nozomi, and Fortinet, to facilitate that transformation, from analog to digital, if all the information is critical? >> That transformation is happening already. A lot of these industrial networks are already working on top of ethernet, a standard DCPIP. The way the journey works for us, is we provide, as soon as we show up, an immediate amount of visibility. These networks don't have the same tool sets from a visibility and asset management perspective that IT networks have. So, the first value add is visibility. We capture an incredible amount of information. And the first and best way to deploy it initially, is with, let me look at my network, understand how many PLC's do I have, how the segmentation should be properly done. And then, during all this time, our model building is happening, we're learning about the physical process and about the network. After we've done with the learning our system, determines that now it's ready to enforce, or detect anomalies, and we become at that point, active in anomaly detection. At that point, the customer may connect us with Fortinet, and we may be able to enforce quarantine activities, or blocking activities, if the problem requires it. >> Is there any one particular, use case that sticks out in your mind, as a considerable attack, that Nozomi has helped to stop? >> We obviously can't name any one in particular, but when it comes to defending yourself against cyber criminals, we have defended companies against malicious insiders. Sometimes, an employee didn't like how something may have happened, with them or with somebody else, and that person leaves the company, but nobody removed their industrial credentials. And they decide to do something harmful, and it's very hard. Industrial malicious insider activity, is extremely hard to pinpoint, extremely hard to troubleshoot. Industrial issues in general, are very hard to troubleshoot. So, one of the things that Nozomi adds a lot of value is, is allowing troubleshooting from the keyboard, without eliminating trucks and excel sheets, you quickly can pinpoint a problem, and stop the bad things before they happen. >> One more quick question for you. With the announcements that Fortinet has made today, regarding, you mentioned some of the products, what are you looking forward to most in 2017, in terms of being able to take it to the next level with your customers? To help them, help themselves? >> Listen, the solution works amazingly well. We have to tell more people about it. I think the critical infrastructure has not had the attention in prior years, and I think this year's going to be a year where, ICS security is going to be, and Fortinet of course, is very aware of this, is going to be a lot more relevant for a lot more people. The number of attacks, and the you know, the attacks surface that will never be, it's all playing so that, this year's going to be a big year. >> Yeah, I think we were talking, before we started, that the U.S. Department of Homeland Security, has just identified the U.S. Election System, as a critical infrastructure. >> That's right. >> So maybe it's going to take more visible things, that have global implications, to really help move this forward. >> I think the one point I would make when it comes to government, government has been great, if you make an analogy, this is an analogy that I have on the top of my head, if you look at cars in the automotive industry, seat belts and airbags have saved a lot of lives. We don't have that in industrial cyber security. And we need the government to tell us, what are the seat belts? And what are the minimum set of requirements that are electrical, infrastructures should be able to sustain? And that way, it makes the job easier for a lot of us, because nobody can tell you today, how much security to invest, and what's the mix of security solutions that you should have. And therefore, in the places where you don't have a lot of investment, you don't have none. And you become very vulnerable. Today, if you want to ship a car, and you want your car to be driven on the road, it has to have airbags, and it has to have seat belts, and that makes it a minimum bar for proper operation, if you will. >> But the proper, the way it typically works, is government is going to turn to folks like yourself, to help advise and deliver visibility, into what should be the appropriate statements about regulation, and what needs to be in place. So, it's going to be interesting because you and companies like you, will in fact be able to generate much of the data, that will lead to hopefully, less ambiguous types of regulations. >> Yes, that's right. That's right. I agree 100%. >> Wow, it's an exciting prospect. Edgard Capdevielle, thank you so much. CEO of Nozomi Networks, it's been a pleasure to have you on the program today. >> Thank you. >> On behalf of my cohost Peter Buress, Peter, thank you. We thank you for watching theCube, but stick around, we've got some more up, so stay tuned. (tech music)

LeBron welcome to the silicon angle studios the cube here in Palo Alto I'm John furry your host we are in studio for a conversation with Aykroyd who's the CEO vision formerly with Oracle marketing cloud recently took the Jobs CEO decision congratulations thanks John great to see you thanks for coming in on the holidays kind of winding down the year what a year it's been Trump's meeting with tech leaders Oh having them kiss the ring get the trillion dollars offshore on site advertising is upside down date is the hottest thing on the planet you know you're in the center of the action certainly at Oracle we had multiple conversations but now you're leading in coupling so Kevin Aykroyd leaving Oracle marketing cloud or incision that's that's way down the track that has change right no big deal well we're as you know we're always out front of the trends but the marketing concepts have been around our businesses since in the centuries since business was around but now is data as we talked us changing so the biggest trend that we see happening is that marking isn't just a marketing thing it's a company-wide data opportunity so it's certainly changing a lot of the game and I know we've talked about that so you know what's the what's the change why did you decide to take the CEO opportunity of decision was the company did it what attracted you to these yes thanks for asking and good to be here by the way i I've been here with you fair amount this is the first time I'm not wearing my Oracle marketing cloud uniform so good to be seen in a second uniform right how does the how does the blue and orange decision uniform look John I look I've been working hard all right yeah yeah taking these good well you got to grow you know that's executive everything stops with yeah well well and just to be really clear because I know that my name with you guys especially has been synonymous with Oracle marketing cloud I I started it I did all the acquisitions I grew it you know is kind of my baby I didn't leave because there was anything wrong I think Oracle marketing cloud is going to continue to just absolutely kick ass and take names think they've built the right mousetrap you know as you've heard me they didn't they didn't start from CRM and go backward they didn't start from the website and go out they started with data right data objects crosswise add this the first big DoD MP and data marketplace I think they're data-driven you know strategy is going to continue to see them just absolutely survive after me and I sure hope something cause I well they're set up to win I mean if the integrations are always a challenge and I think our last interview at the modern marketing experience great show yeah we talked about that specific thing where you want to be vertically specialized but yet horizontally integrated and you set that up and and I think I and day right have set that up so they're poised really well CC so I didn't leave Oracle because of any lack of faith in their ability to go conquer that very big opportunity or any personal dissatisfaction is probably the best job I've ever had my career this is one of those classic cases where I saw an opportunity that was so good I had to leave something that I that I loved so for everybody that's listening I'll just say that again Kevin didn't leave Oracle because there was anything wrong Kevin left Oracle because of what I'm about to riff on now it was this big opportunity and basically John we can we can go as deep as you'd like to in today's interview but at the highest level this big opportunity that I saw is you just look at the data driven and then you know data meets content meets applications meets media the channels come together right the life cycles you look at everything that's happened and it's easy to kind of now say well just go look at what Salesforce marketing cloud and adobe marketing cloud an Oracle marketing cloud right look at that billions and billions and billions and billions of acquisition look how fast and far that's come and basically look at the needs that drove that that massive convergence and it has fundamentally changed the industry it's fundamentally changed the chief media the chief marketing the chief commerce officers ability to go drive results that they couldn't have done without Salesforce Adobe and Oracle doing what we did right but all of that has been done at paid media right the advertising at commerce and it owned media right our websites or mobile applications none of that through with all the tech giants in the industry and of the 20 billion dollars in M&A capital op X and capex since then none of its touched the third leg of the stool which is earned media right earned media communications good old-fashioned PR the exact same need for that data technology and measurement transformation that sales and service and commerce and paid media you know and owned they've all been through that this mission critical part called communications or in media has not been through it as we were building this my private equity company GT CR is very quick quietly over the last two years put together six leading solution providers in this earned media communications world just like I put eloquent responses in blue Chi and Maximizer they've been doing the same thing over here aimed at this earned media opportunity and if anything I think that every CEO every CIO every CMO would tell you they understand there's very clear there's a lot of clarity that I can't advertise my way there and I just can't get there by sending 300 promotional email and SMS campaigns you know versus 200 last year I can't promote my way there I can't advertise my way there if I want to influence customer experience customer loyalty and relationship and ultimately customer purchasing behavior I got a not just advertise and promote to them I get to get at what's called influencers right consumers whether they're b2b consumers or b2c consumers I am more and more being influenced and driven on who I listen to who I respect and hold credible and ultimately who I buy from based on people I trust that's that's called an influencer whether that's a reporter an academic a social person a blogger a community leader brands know I got to get to the influencers if I want to get to my customers and that's all about earn so the opportunity to go repeat exactly what I did at Oracle marketing cloud for Paden owned but do it over here and earned was simply too big an opportunity to pass up well first of all I love that one and drill down on scission and specifically and when you your plans are there but let's stay on this mega trencher second because I think you're hitting the nail on the head here because I think this some that you know we actually when we started Silicon angle media seven years ago this was the premise of our business yes we saw that the connected network that's right of social is fueling this new earned area where earned is truly earned yet there's no real website no silver bullets right it's a distributed as tightly coupled Network and there's pockets of it so you know what influence is about the most followers it's about the relationship of the connected consumer yeah who's also a consumer and a producer of content yeah their opinion there and so this is all kind of a new behavioral thing yeah so you go back to you know he earned and I mean the honed and paid and searched and all that stuff did contextual and behavioral absolutely really that's two things that's right the behavior of the crowd you got you can't look further than the Trump election to say whoa who saw that coming that's an example of an earned dynamic I would say that caused people to go well what the heck yeah I should send him a letter for thanking him for making my point so so emphatically for me we're all going exactly right hey what's up her for that crying in there wine in California for sure a blue state but this brings up the dynamic right this is the mega trend that now this earned media component isn't just about ads it's software that's right it's about software and networks and with cloud computing there's an opportunity for people to participate in there so so how how do you guys a minute rephrase it this right how does customers what what's the current pain point I mean what's the top three yeah I'll see you advertising you know I don't want drive traffic to my site that's an old mentality right that's the only thing they can do right now yeah it is looks so again I think it is getting at that at the risk of being repetitive it is okay boy if that's all I do is rely on the big monolithic web infrastructure I've developed the campaign engine that just keeps getting cheaper and cheaper so I keep sending more and more and okay it's programmatic now so I guess I can throw more at Google and Facebook I I'm not saying those aren't important parts of the mix you of course need to continue but they're declining and efficacy there right so not only the decline in efficacy while they increase in spend the cus the consumer right again whether that's a b2b consumer Ibiza is becoming increased don't view him as credible don't view his trustworthy if they've got these big lofty goals in this new digital world we're right the fragmented influence is hard and hard to contain and they just flat-out need to they recognize that the thing that's probably going to be the most important going forward which is solving this puzzle is the thing they've D invested in the most right it's gone from the king of the hill 20 years ago to as a true second-class citizen while they got all drunk on paid advertising and you know more e-commerce the role of the buyers interesting is let me just get your thoughts I'm sure because one of the things that we've observed at silk'n angle and our business model is we do really really well with our I'd sing I don't call my advertise sponsors if you will because we're very community driven with the cube as you know is that we have buy-in from not just CMO yeah in some cases just the head of communications right so the role of PR public relations is a communications function so the thing about social is you have a dynamic of organic and everyone knows organic is the cool right yeah organic growth bottoms up but the interesting thing is communication pros have a top-down command and control mentality yeah so when you blend command and control with organic growth you can actually have both now you can't this seems to be the new power base that's right the comms person which was hey get the press release out there go talk to ten reporters is now a million people yep the CMO would go with agencies to spend a lot of dough on print ads and TV commercials they have to work together well and the chief communication officer is still one of the nice things you know seven out of ten times they're reporting directly to the CMO the other three times they actually appear to the CMO and they report directly the CEO so it's not Adi empowered function it shouldn't it shouldn't be right and then I think that the modern communication organization I'll talk about who they are and then I'll circle back on the pain point because there's some acute pain there that we're trying to address they don't look at it as just PR now to be really clear and I would like this on record to the traditional journalist reporter media never been more important right it's not like they've lacked but even then right who that reporter is on that publication website versus the print versus the broadcast versus their blog versus their Twitter handle versus their Facebook page versus their Instagram account right even that traditional reporter is nine different influences at nine different audiences in nine different media right so they haven't become less important to become far more fragmented yeah that's exactly right and nailing that is is no trivial thing that's got to get done they they they really are they're they're as digital and as modern and as social as everybody else but then you also got to realize boy right these communities are incredibly powerful these these mini bloggers have as much cloud as the New York Times does in this particular area right the social followings these academics these thought leaders the definition of a digital influencer has widened quite a bit above and beyond the core journalist trip but but don't forget that that person's really important so and then you got the consumer influencers and their user-generated content themselves right so that the customer is their own influencer which is really interesting and that's a b2b dynamic as well as a b2c dynamic so that's the world we all of a sudden you know find ourselves in but I think the modern the digital world that you're talking about isn't a b2b versus b2c it's digital it's digital period one yeah concept and it's no motton it's no longer digital communications or digital marketing it's just communications and marketing in the digital world right and that's a that sounds simple that's a pretty fundamental shift now let's go back into though the tools that they have so they're as savvy and is digital as their peers that are running commerce or paid advertising or the website they've really been bereft of toolkits I'm going to give you an example we work with an extremely large one of the four largest beauty products companies in the world and when they do a good new product launch right let's let's look at advertising they will harness data they will develop 30 different audiences right and they will go to discrete tonality creative offer you name it at 30 different you know so they'll do 30 different banner ads they'll do the same thing with social audience they'll do 40 different data-driven audiences that get discrete touch content an email to do 50 or 60 right 50 or 60 different data-driven segments and even in the website they'll say hey Jon's profile that's profile seven Kevin's profile is profile 12 you will see a completely different website than I will based on data driven right what are they doing Communications one press release and one infographic goes to all 12,000 communication outlets no data no versioning right no nothing so this concept of the right version of the content to the right audience at the right time I'm putting you know in advertising and in commerce on the website I'm talking to soccer moms vs. sexy grandmas versus Wall Street women very different for my beauty products in communications I'm talking to all of them the same which is kind of crazy because the emulators would be a labor driven market - that's right - call it arms and legs right which is what it yeah yeah and a head and arms and legs and a lot of people kind of reaching out but now the trend is to have a much more sass that's exactly right and and and I don't have the platform to actually go do that right so as far as some of the pain we're trying to provide now with our communication cloud just like with the other marketing clouds I don't have I can actually do data-driven intelligent messaging and content delivery to the audience to the influencers that get at the discrete audiences just like I do the data-driven direct communication to the end users themselves probably more importantly I'll stick with my example for a sec John that beauty company at fortune 500 Beauty company they get Rachel who is the head fashion reporter on the fashion section New York times.com right Rachel covered and Rachel embedded my press release on my infographic homerun pop the champagne right it's like okay but well there's two million people that went to that fashion section in New York Times comm today when she covered right how many of them actually read the content and picked it up don't know how many of them actually engaged in it read the infographic click the video click the links don't know who were they from a demographic psychographic sociographic right behavioral don't know and probably most importantly what did they do after they read it did they go to the desired shopping cart or the right community page or back to the website or unit was there any actual digital behavior driven from that bigger meeting full of discovery data the or it stops at I got picked up by the reporter yeah and I have no idea how many of the two million people were influenced covered engaged right etcetera and no idea about the behavior that I took so the link between the influencer comes and the end-user has never been closed that's the second part of the pain point that really fixes now we are fixing the gap between the influencer and and the end user and you're going to see us call that the influencer graph John you'll see a you'll see a press release a targeted one that's data driven and very rich media go out around the influencer graph because if we can start saying hey John's my end user customer now I know right quantitatively with data that I can optimize in real time which influencers matter which reporters which academics which bloggers in which channels in which media and which content as people have different on fluentd rankings in certain contexts you got it and all that's a black hole we know it we have no idea how to measure it make it data-driven make it contextual and optimize it in real time with a digital platform so that this command-and-control CCO who thinks this way now actually has his his or her system of record to actually go execute this way as Maslov Harkavy needs as that sounds because the commerce paid and owned guys have had this for a while this is a this is like discovering fire here for the chief communications officer because they've never had their data and tech enablement platform to do this the way the other guys have so that's that's number two and the number three and I think this is really important is we all know that communicate I want I need to measure and optimize the comms function the way I just talked about it we all know that if done right it amplifies the bejesus out of the owned and the paid - yeah you shouldn't be thinking about them in silos but there's no way to measure that if I did a really good job and earned look at the impact it have in the efficacy on that massive page budget now mutually exclusive and there's a relationship between them because in social and communities collaboration that's a four linchpin it is you cannot articulate just how important that is and until tech vendors put the apps the api's the data and then the right through the ID syncs together you can't measure it right and as fundamental as that sounds that's why what's happened over there in Adobe Oracle Salesforce land had to happen and it's why what we're doing here incision line has to happen so that not only can coms catch up but comms can communicate in that data and play an active role in that - an active role because no leaders happen is they're going to realize holy smokes the paid performed here without their and the paid performed here with the earn and quite frankly that earned outperformed the paid right so we're not going to be a participant role is going to be a I'm going to resume my rightful place at the head of that you're the head of that tribe on our second segment when it get more indecision and specific solution but in this segment on kind of wrapping up the big megatrend Housley social and the technology and network effect of social combined with the data combined with the fact that comms communications right is now an active leader and important role in the creative Nick that's right I've earned that's right and integrating in page I can have a cohesive but decoupled programs it's not silver bullet either well pleasure rising tide floats all but I've earned has been under developed under developed under invested in under tech enabled under date enabled and really that's what it gets to is the people in charge understand that they didn't quite have the data ten tools to do it the data the tech tools are now available and now the the industry just got to kind of get up the sophistication curve so final questions in this segment is where's the progress bar on this sector how early is it first inning bottom of the first second inning and to there's always in these early adopter markets that certainly that you saw I believe left the Oracle for it but this is an I agree by the way is a great great opportunity they're always the champions internally who can see it - yeah how where's the progress bar and what's the advice to the folks that are inside these companies who actually have the religion say this is the future and have to communicate it to the rest of the kink unfortunately the thinking the thought leadership bar is probably in the third inning to get it uh doing something about it and going from good thinking to good practitioner ship and execution is retraining first out the first out to the first pitch in the first inning you know of the first game of the season we're literally at ground one the good news is though is they're not going to try to go convince the CFO from a money or the CIO from a resource or the CEO from a strategy this whole I keep saying is this data tech and measurement transformation the corporation no matter what the corporation is invested it in sales look what happened they invested in the service look what happened they invested in it and paid look what happened they've invested in it known so the good news is is while they are at the very very very beginning of the ball game they're literally the last function inside the corporation to actually go do it and they don't have evangelism around the benefit of this type of transformation it's worked in every other area so while they're the very beginning they want to convince anybody it's a good idea everybody else that's down the hall and sits around the CEOs table has been through that transformation so there's not that evangelism it's just now his or her tits operationalize they do some results that's on the right table and and it and it's shown results in all these other lines of business so there's not this fundamental disbelief that it won't show results in the communications line of business there's actually quite the opposite there's heavy belief that it will because it has shown right it has shown results and all these other lines of business so yeah especially look is that's obvious - it's like okay we got to do this yeah that they should be able to move faster does this caterpillar should turn into a butterfly really fast because everybody's thinking about it the text in place and it's worked in other places but we are really really really at the very beginning it's exciting Kevin Ackroyd CEO of sisian year inside our studio talking about the landscape of really digital changing and how earned media blogs and folks like silk'n angle and others who actually producing original content an engaging audiences now an opportunity to convert over on this new market shift going on big mega trend we back with segments to talk about the company and their solution and technology we're interesting to get that perspective Kevin thanks for joining us here in the palace news thanks for watching thank you [Music]