 Okay, we are here live at Stratoconference. End of the day, Ben, it's a hot day. You know it's going great when it's 5.30 and you're cranking along and it's just so much content. You're going to burn the midnight oil. I'm here with a special presentation, a special guest, Ken Kukie, who wrote a killer book called Big Data. Okay, Big Data, a revolution that will transform how we work and think. Okay, this is really an awesome book. Get it, the testimonies on the back are all from the top. Elite thought leaders in the tech business. We've seen multiple cycles of innovation. Clay Scherke, I'm a big fan of Clay. Mark Benioff, obviously Maverick, Jojito, Larry Lessig, John Silly Brown, Jeff Jonas, obviously been on theCUBE, CUBE alumni. So really, really the thinkers of our generation are endorsing this book, which means that it's important. I haven't read it yet, so I can't comment personally, but I know that in talking to Ken yesterday, it's an important topic and some of the content he has in here will shape how people think about the world view going forward. Ken, welcome to theCUBE. Thank you very much. So, first I want to just get out there, quickly describe a little bit about the book and the motivation and what is kind of the core thesis inside the book? Well, the book is written with Victor Meyer Schoenberger, who is a professor at the Oxford Internet Institute in Britain. I'm the data editor of The Economist. Together, we came together to look at what's happening with Big Data and why it matters. And specifically, we identify a few trends that change things. It's not business as usual. The first is that instead of using just some data, we're able to use more and in fact, in some instances, all the data. By doing that, we can get into granularities that we never could before and learn new things. We could also use messy data, not just the clean data, the pristine data and the highly curated data of the past. On top of that, we're able to go for correlations, things where we don't have to answer why, but just what? Because it's good enough and it's fast enough. So together, what it means is that there's a whole new approach to business by taking data, not for its primary use, but for its secondary uses, re-usage it and extracting new forms of value from this information. So as the economist editor of the data, edit management, what's editor of data? Section of the economist, which means you look at data points for all the things, right? I mean, take us through that job and then we're going to, I want to bring that into the book because I think economists got a great reputation. They know, everyone knows that credibility, but you guys are sourcing all kinds of stories there. What specifically ends your role over there? Sure, well, it's actually two separate things. So my day job is thinking about data and using data, looking at all of the issues that we cover and how there's a data dimension to it. So if we're looking at, for example, the conflict in Syria, there's an absolute data dimension. You might want to look at the combatants, both in terms of the military and the paramilitary and the deaths there and the injuries there. And then look at the civilians and you get a whole new approach by looking at the civilian casualties and look at the refugees, rather than just the casualties, just the mortality of the soldiers themselves. So that's a way with which we use data to inform our coverage. Now that's sort of secondary, if you will, that's data visualization and that's how we tell stories in the media, but it's a little bit different than the way that business is implementing data, which is, if you will, not my day job, is the data editor of the economist, but thinking about big data and what it means, what's to play for, for new companies, whether they're startups that are doing analytics, or whether it's old companies like General Electric or Nestle that have huge pools of data that all they need to do is unlock it with the right mojo. Well, let's talk about that. So one of the things we've been saying on siliconangle.com and here in theCUBE, certainly over and over again, at least the past two years, but Strada seems to kind of elevate the conversation. Maybe a little bit at a Duke summit. It was mostly a geek crowd, but here you got a little confluence of business, business mind, business visionaries, and also tech alpha geeks, and also now data scientists like yourself. So, and we say for the first time in business history, ever in the evolution of the human race, you can actually instrument the business model of a company end to end, everything. I mean, everything's measurable now. Hiring, firing, suppliers, customers, support. The Zappos kind of support on one hand too, using big data. So a massive re-engineering is coming. That's right. Right down the tracks. Exactly. And it's coming fast. That's right. And so you agree with that, obviously. 100%. In fact, what we're finding is that data is becoming the new form of corporate literacy. We have seen this before. We have seen when companies needed to retrofit themselves for a data deluge when the information was financial information. This is the turn of the 19th century. We had disclosure requirements. We had a whole generation of bookkeepers and clerks that became accountants and auditors. We had to create a whole new professional class to deal with information, and the information was accountancy. We didn't have that as a practice before. We didn't have the standards, didn't have the norms, didn't have the professional institutes. We didn't have the training. We are now doing the exact same thing for a new form of literacy and numeracy called big data, called analytics, it's business intelligence. And here we need to create a new generation of business leaders. We need to create new professional bodies. We need to create new schools. And this is going to be our generation's great infrastructure project, like we saw with the modern techie greatest generation of the academics that then created the internet, we are having a new generation of math whizzes and statistics people, machine learning and AI people, creating this generation's way to optimize the enterprise through big data. Well, it's a revolution, I totally agree with you, but think about this, right? What you just said, how profound that is, our generation, you talk about a once in a multiple generation inflection point of massive change. I mean, massive change. And if you look at open source, at the open source economy, right? What has, how that has evolved. If you look at the data world, the liberation of data, it's free, we talk about transparency. Now you have frictionless sharing, all kinds of rights going on, data rights. So open source drives around basically three major things, training, which is awareness, skills gaps, management, how do you get people skilled, and then implementation. That is what we're seeing. Now let's break that down, training. Okay, we're doing conferences, we're at the big dawn of this, right? It's beginning, skills gap. What can you share with the audience out there around skills gap? Yeah, you know, the high level, you know, multi-disciplinary, blah, blah, blah, but let's drill down there. What are you finding with skills gap? You know, there is a skills gap, it's going to get fixed, because if we had this conversation in 1960, and we were talking about the skills gap in programming, we would have said there wasn't enough software programmers. McKinsey. Punch carders. Yeah, exactly, punch carders. Well, by the 60s there was actually software, but what we would have found was that McKinsey would have come up with reports with hysterical numbers, saying that there's not enough professionals in this area. And by 1970s, we still would have a shortage, but we certainly have enough in by the 80s as well. Even today, we have a shortage of extremely talented programmers, but we don't have a skills gap when it comes to programming. Same exact same thing is going to happen with big data. We are going to remedy this. What seems to be- You don't think it's a problem, you think it's just natural evolution, things will fill the gap quickly. Here's what's happening. What seems to be abundant today is actually scarce, and what seems to be scarce today is actually will become abundant. So it seems to be scarce. Can you give an example? Yeah, what seems to be scarce is the talent, and the mindset, and the creativity. That's going to become commoditized, because everyone's going to get it. Nobody in corporate America doesn't get the internet today. Now it's just going to be an optimization play. Big data's going that way as well. So what seems abundant? It seems that the data is abundant. It seems like we have information everywhere. The fact is, the reality is that the data is going to be what's scarce. That's going to be the critical resource. And what we're having is a sort of gravitational effect that the largest companies with the data are collecting even more data, and the companies with a little bit less data on a proportional basis, on a relative basis, have less. So where now we have to focus is collecting the data. So that explains why we're seeing a huge land graph in terms of companies reaching out and getting the data, because that's where the value's going to lie. That's awesome. I totally agree with you. Now let's get back to the implementation now of work, home life, education, and society. Global namespace is called the world, Earth. It's one nation now. Global data is not while Germany would argue, right? So let's talk about the global impact of data, right? So that also supports your scarcity argument. LinkedIn doesn't share their data unless you owe off in. That's exactly right. And you go into foreign countries with all kinds of mandates around data policies and policies. That's right. That's going to create some bottlenecks. Well, that's exactly right. I mean, what we're seeing in Europe right now against Google, by the European Union, is essentially the first big data regulatory proceeding. And we're going to see more of these things, just as Microsoft was under the gun for its technical standards in the middleware of software and adjacent markets. We're seeing essentially the same debate now being played for data with Google, but it's not just going to be Google. It's going to affect startups. It's going to affect larger companies as well. This is going to be very important. We don't know how to measure market size in a world in data. We don't know what it means to have lock-in when we have this data. Should data be liberated? Should it belong to the person who created it? Me, my own data? Maybe, but maybe not. And here's the case for why not. Because it took the company, the cost, and the effort to actually collect it and analyze it. Just the fact that I'm giving off the data itself may not be the most remain factor here. This is simply to say that the regulators are behind. They actually don't understand big data and how to perform the upholding the public interest in this era. Now I'd like to say that the good news is that the book that Victor and I wrote, itself takes a few baby steps in that direction. We move the ball up the yard, up the field, thinking about how we can conceive of big data both in commercial terms, in terms of public policy, and in terms of the regulatory nature of it. So Ed Dunbill talks about the prosthetic brain called big data of our life, the neural network, if you will. And I've been loving the neural network analogy for 20 years now, but now we're seeing that kind of, and he says data's the blood. And I've made the comment nutrients and some data's good, data bad, data. But let's talk about the impact on people, right? So how we learn, how we communicate, how we interact in spaces now, because now there's multiple dimensions. You could have, today I'm Big Data Furrier on Twitter. It's in my same handle, but now I'm Persona. So because we're at the Big Data Conference, so we have multiple Personas, but we're still always going to be physically present somewhere. So I have access to information. So how do you look at that in your book and in just my personal view, the role of the individual? Well, what we're going to find is that the Big Data analyses are going to drill down to individuals in very specific areas. What we're going to see is that we have often treated people as a mass, as a group. And so an example is profiling with insurance. We might look at men and women and treat them as a class. What it specifically means is that young men pay more for driving insurance, young women don't, because young men are in accidents more frequently than young women are. Okay, fine, young drivers. That's great. That doesn't make any sense. What we're going to have is data that's tailored to the individual. So the benefit is that we get rid of profiling, which nobody really likes, because it's too clumpy. It's too big a mass. The drawback is that by drilling down exactly down into the individual, it leads to a big problem. It's a new one for society, and that is propensity. We've thought about the issue of privacy. Now this is about propensity. We're having algorithms predict what our actions will be before we actually take them. They're not doing it in a class-based system, a profile. I'm an African-American motorist, or I am a... It's a real individual level. It's an individual level. And now you're penalizing me, either with a higher insurance premium, or with being denied a loan, or something else, based on my propensity to do something before I've actually acted. I'm essentially, it's thought crime, or more likely it's actually pre-crime-like. In order to report, I'm being busted before I've actually killed someone. And that actually is an affront to the judicial system, but interestingly, we've never actually thought about that before, because we've never had an environment where the judicial system, or any actually other administrative proceeding against us, would actually penalize us before we've actually acted or committed the crime, or the infraction, with big data and its predictions. That's exactly the world that we're walking into. Yeah, let's talk about, we're going to wrap up here, but I want to just drill down this one point, because a lot of Clay Shirke's work has been involved in, here comes everybody, kind of concept, and groups, and social theory, and social spaces, but you're referring to as a very slippery slope. I mean, you're talking about a dangerous environment where predictive analysts can actually penalize somebody. What do we do? I mean, how do people get involved? I mean, can you share with the folks things that you've learned around grassroots research organizations? Are there any groups involved in watching this? Sure, so the bad news is that it's early days still, we don't have to share. Or good news, it can be effective change, positive change. Well, exactly, I mean, we don't have activist organizations yet who are looking at this. We've not even really wrapped our minds too much about it. The good news about the book that we've just published is that it's going to actually reveal some of these issues so that we can actually have a debate on what this means. Great, yeah. This book is fantastic, big data. I already know it's good, because the conversation was fantastic. A revolution, I love the title because we are living in a revolution. It is a game changer. It's going to affect everyone. I'm 47 years old and my kids and their kids will be living in a whole new world we're in. So big data analytics, one of many books. But this takes a really worldview, a society view, looks at some theory, but down to practice. Thank you very much for coming on theCUBE. We really appreciate it. Kenneth Cookea from The Economist. He's a managing editor of the data section as well as a practicing, I call data scientists. Even though you might not want to call yourself that, but certainly you are. And we're here inside theCUBE and bringing you some great content. And we're here on day two. We'll be right back with our next wrap up guest at this short break.