>>From the Mandalay convention center in Las Vegas, Nevada. It's the queue at IBM. Insight 2014 here is your host, Dave Vellante. >>Hi, welcome back to IBM insight everybody. This is Dave Volante with John furrier. We're here with the cube. The cube is our live mobile studio. We go out to the events, we extract the signal from the noise. Carla Gentry is here otherwise known as at data nerd. Carla, great to see you. Welcome to the cube. You are a data scientists. Do you have your own company? Um, we were just talking to, uh, to dr Ahmed Bouloud from a university in um, Istanbul and he said, well, it's data science. It really, really isn't a such thing as a data scientist. And so he and I are arguing a little about it. So I said, come back and see Carla, right? You're a data scientist, right? >>Well, you know, right out of college I started with a RJ criminal associates up in Chicago. And um, that that's what we all were a bunch of data nerds in there playing around with terabytes of data before anybody even knew what a terabyte one terabyte was really big. Right? Right back when the terabyte was big data, but a, you know, gleaning insight for a discover financial services. And then, you know, I've worked with consumer packaged goods, the education, I mean it's, it's been a wonderful, wonderful career. And what's so great about this is to be able to walk around and see how much data is a part of more people's lives now than it was 20 years ago. I mean, 20 years ago you couldn't have, you know, gotten thousands of people together talking about data analytics. Well, you know, the interesting thing about what you're saying without you, you CPG education, financial services, John and I talk about this a lot, how the data layer is becoming a transport mechanism to connect the dots across different industries and data scientists. >>You guys don't like to get locked into one little industry niche. Do you you'd like to gather data from all types of different sources? Talk about that. Well, that's the thing. Uh, unfortunately, uh, we get bored very easily because, you know, we like to have our fingers in a lot of different pies. But, you know, you wouldn't want to be necessarily siloed with just one kind of information because curiosity makes you think about everything. Education, risk, you know, I'm that way. I have no walls. You know, I can, I can glean insight from any type of data. If you've got a database, uh, we can jump in with both feet. Is data is data and why is the data more transformative today in this day and age, you know, circa 2014 versus say, when you came out of college, why is it that everybody's talking about data that data is able to, to change industries, transform industries. >>What's different? Well, now the, you know, data can actually give you, you know, an insight into your customer mean, you know, what is your customer buying, you know? Um, so when you go to, you know, run a campaign or something like that, you, you're not shooting in the dark. You know, you're actually, you have a face to your customer. So you know, you can make decisions and it's not just marketing, you know, which is what I started out in, you know, trying to do increase and lift, you know, sales. But now you know, you have risk, you have, you know, data breaches. You have, you know, what keeps CEOs up at night, you know, it's not only the cash flow, you know, it's the mitigated risk that's involved. And when you're looking at your, your data and you're collecting this information that gives you a view into what's really going on so you can sleep at night and have a little bit of comfort mostly, >>well not sleeping at night, it's a couple hours of sleep. The notification when I opened CEO's and CIO's, CFO's, chief data officer, you've seen much more formal roles around data where data is real key asset. And this is awesome because it brings to the forefront the role of data. And so I want to get your perspective on this. You brought into the kind of the, kind of the trajectory of where we've come from, um, and talking about the role of software because really what this highlights here at IBM insight is okay, it's not just data per se, you know, how software that's a key part of it. So it's now also an integral part of the platforms. You have a developer angle, you have the data asset, and now you've got this real time in the moment experience. And IBM is talking about engagement a ton here. And so what's your take on all that? I mean it's, it's exciting. Certainly if you're in the data business. >>Well definitely, I mean, real time data, of course it's very expensive. Um, but it's, it's more attainable now than it ever was. Um, the thing is now is you don't necessarily have to be a data scientist to be able to go and get at your data. I mean, thanks to software tools, you know, like IBM, they give you that benchmark, you know, the, these tools, uh, where you can use BI and things like that. To be able to get a view into your business. And you know, it's not just for, you know, your analytical department anymore. Um, so I think it's what it's done is it's actually made it more attainable now. You know, it was like people looked at data wagon back then, Oh, and it was so scary, you know, but now it's, it's bringing it to the forefront to where we can make decisions. We can want our bitter, our business better. And like I joined forces with a repo software years ago to look at the supply chain. Now when you talk about that, that's what keeps the lights on. But you're only as strong as your weakest link. So when you're working with third parties, you have to make sure that everything is going smoothly. So >>I want to get your take on a couple of things in. He chose SA was on earlier and she's an awesome guest. She's been on many times. She's dynamic and articulate and super smart, brilliant and beautiful. We love talking with her. She said, I asked her what are the top three customer issues? And kind of a double edged question. She said three things, customer experience, operational assets, AKA the supply chain, and then risk security and governance. And then we weaved in context computing and then cognitive. So let's break that down. So customer experience, internet of things is a data play, you know, probes and sensors and machines certainly get that >>analogies. People are things. Yeah, well you know, here's the thing that you think about. Data. Data is a person that record that you have in that database equates to a real live person and you want to, you know, you're not going to be friends with your, your customers, but you want to know more about them so that you can serve them better. Um, you know, for me the biggest thing is, you know, people will go out and spend millions of dollars on a database but not necessarily know what to do with it. So it comes down to what question are you trying to answer? >>Yeah. And the infrastructure piece is interesting because you want to have that agile flexibility, which is kind of a buzz word amongst vendors. Hey, be flexible. But there is meaning behind it. Right. So context computing is relationships across entities. The streaming stuff is very, very interesting to me because now you have streaming data coming off of devices and again brings up the real time piece. So making sense of all this means it puts it in the forefront. >>And what you can do with that data is if you do have a client or a customer and you let them link in socially, like log in through Twitter or LinkedIn or Google, Facebook, now you can append that social data. So now you, you've got an ideal, you know sediment and you know when you're positive you it's first party data. Yeah, exactly. The Holy grail of active data is first party data. Exactly. >>Cause we'd love the crowd chatting and love people. The logging in and, and thanks for, by the way, for hosting the crowd chat with Brian the other day. It was really fantastic conversation. My pleasure. Let's talk about cognitive because this brings a human element of it. And one of the things we've been teasing out of the past couple of shows we've been at around big data is the role of the developer where the developers in the old days from even going back to the mainframe days, cold ball, they were adding in these rooms, almost like almost an image of coders in the back room coding away. But now with the customer experience front and center with mobile infrastructure, the developers are getting closer to the customer experience. And so you're seeing more creativity on the developers side with the use of data. Could you share just observation, anecdotes, things you've been involved in that can tease out where this is going and how people should be thinking about it? >>Oh, do you know 20 years ago if he tried to show someone and graft with, you know, 16 different things at one time going on, they were like, that's messy. Now you can actually find the sweet spot or where everything interacts. So you know, when you're talking to an artist, a digital artist who's working with data and giving that picture, that's exciting for me. And going back when we were talking about cognitive computing, when you're talking about the Watson on ecology, that's exciting. Yeah, that's the highlight of it's almost magic. It's almost like black magic, this Watson stuff and people are really just now getting their arms around that and that is essentially making sense of the data, but that's the thing. See, it's no longer magic now. That's what they thought 20 years ago. Poof. People like me, they kept in a little closet, you know, and then our office and they only came to Moses when they needed something. >>Now we're an integral part and we actually are in the business development meetings and we're a liaison between the it department and the C suite. One of the, one of the things that it's interesting about your role as not only you out in the field doing some great work, you're also an influencer here at the IBM influencer program, so I want to get your take on this balance between organic data and kind of structural data. Organic data means free forming unstructured data and then existing data that comes in that's rigid and structured because of business processes. And I get that is data warehousing business has been around for years, right? It's intelligence, it's all fenced in, all structured. But now you have this new inbound data sources coming in, being ingested by these large systems, data changes the data. So you now have a new dynamic where latency, real time insights, these are the new verbs, right? >>So talk about that role, the balance being organic data and the structure data and what the opportunities are. Well, the wonderful thing about, you know, now that unstructured data was scary way back in the day. So now it's not so scary, you know, now we can actually take this data and make business decisions, but uh, you know, like social data and things like that. When you can add that in a pin that and get to, you know, what we all want is a better view of our customer and to be able to, you know, do better business with them. Like, um, like supply chain management and things like that. I mean you're, you're looking at open people, you know, collecting information from varying sources and this all has to be put together. So I think they mentioned earlier this morning how 80% of it is we're data janitors cleaning up this, that and the other. >>Whereas what we really want to do is, you know, glean the insight from it. But I think, uh, the tools these days are making that much more easier no matter what the source is that we can actually put it all together, what we used to call the merge Burj back in the old days. It takes weeks to do the merge purge and yeah, who all here knows what a DLT is trying to solve this problem for a while with traditional technology 17 years. So let's talk about, you know, the, the promise of BI and the traditional data warehouse 360 degree view of my customer, real time information. And that's what it's about. It's about drilling down predictive analytics, all these promises. Did the data warehouse live up to those promises in your view? Well, initially, maybe not, but you know, things are, it just seems in the last few years that people have had an epiphany of how this is really adding value to their company. >>Now back in the old days, they all knew that, you know, insight is wonderful, but now you can see it visibly showing signs actually making a difference in company so they can keep an eye on everything that's going on. Now, going back to what keeps CFOs, you know, up at night with the risk and stuff, there's still always the risk, but at least now you can get a little better handle on it. And thanks to the age of technology and the data that we have accessible to us today and the tools we have available to us today. It's, it's made a dramatic change. What are the technology catalyst? Is it do? Is it no sequel? Is it, what are the, what are the tools that are sort of the foundation of that change? Well, I think always the, you know, the new tools and making it so that you don't have to go out and learn SQL. >>You don't have to be a programmer, you don't have to, you know, go to college for four years and learn mathematics and engineering to actually be able to work with this data. So thanks to, you know, tools like had it been other tools. I mean you can really sit down and glean insight without having to write one single line of code. So the things we're getting some questions in the crowd chats, um, um, at furry, at data nerd, what are the key things that are messy, scary right now for CEOs and CFOs? So things are becoming less scary. What is the scary things right now? Oh, the scary thing is the breaches. You know, when you hear about target and these big names, you know, people getting access to your, your credit card data. That's, that's scary. So, you know, we've got to really try to lock down that risk, you know, and I know everybody's scrambling scratching their head, figuring out how we're going to keep these breaches from happening again. >>Yeah. Big data solves that. I mean you have big data technology, which is a combination of machine learning, streaming where you're getting massive surges of data coming in to these ingest systems where you can apply some reasoning to it, some cognitive, some insights to look for the patterns and that's where machine learning shines. Um, how do you see that aspect of machine learning and these new tools affecting that kind of analysis? Will I see it opening up a lot of different doors for a lot of different people and making a difference because, uh, you know, everybody knows that data is important, but not a lot of people know how to deal with it, especially when it gets into the zettabytes of data. When you have tools, you know, like the IBM tools that can handle this type of load and be able to, to give you, you know, instantaneous information. >>And, and like what we saw this morning where, uh, like risk, I mean an oil and gas industry, you know, you, you have to worry about, you know, as someone going to get injured on the job and they showed the the center, whereas she walked toward it, it went off. I mean the internet of things, being able to let us know in real time if there's a danger, you know, to personal life or to your database and then predictive to be able to say, well this is what we think is going to happen in the future and to be able to move and act on that. It's a very exciting time. You mentioned IBM, so obviously is a leader in here, >>Jeff Kelly's report shows IBM is the number one big data player. But big part of that is IBM. So big, right? >>Well and you guys were around a long, you've been around a long time. You guys were playing with big data way back before. Big data was big data. So yeah, we guys, us guys, yeah, well social, social data, >>those guys, right? So we're not all right, but so, but, but so you bring up IBM, a lot of people have a perception IBM big, hard to work with, but you're, >>but that's changing. So talk about that change. What I'm excited about is the Watson's analytics. I mean that in itself right there and made me sit up and, you know, get excited about the data world all over again. You know, to be able to excite you about Watson analytics platform? Well, I really like, uh, the, uh, the oncology, uh, Watson, um, they had the, the one for the, uh, not necessarily for the police, but for the, uh, the crimes. I mean, in real time, if you can see that a crime is about to happen and you can prevent it, or if you see someone's health is failing and you're able to step in. And that's why over there, earlier I was talking about IBM cognitive abilities can save lives, you know, so I mean, my, my mom passed away from cancer, so, you know, the, the, um, oncology Watson was very exciting to me, but it's gonna make a difference. And I think the thing is now is that how it's changed is to make them user friendly where you don't have to have a data scientist or an analyst to come in. You know, they talk about how expensive data scientists are. Now the reason I opened my business was to make it affordable to small businesses, you know, so although you know, people look at IBM and think it's scary, I think they're going to see now that the, the direction that they're moving is becoming more user friendly and more available. >>So Carla, I wonder if you could talk about how you engage with clients. So you mentioned small business, right? Cause you have a lot of, a lot of businesses, small midsize companies don't have the resources. Right? Um, so where did they start? Did they start with a call to you and, >>well, uh, most of the time it's a call where, you know, we spent all this money on this database and we still can't get what we want out of it. So it comes down to what question are you trying to answer? I think that's the most important thing because that directly deals with what data that you need. And if you don't have it internally, can we get it externally? You know, can we go through open source, can we get census data? Can we get, you know, work with hospitals and doctors and things like that and use this to be able to feed this information into them to make a difference. >>So what do you do? I mean, are you so CEO calls up small companies, is that got all this data? It's unstructured. I get some social data. I get my customer data trying to make sense out of. I'm trying to figure out, you know, who's >>ready to buy, where I should be, you know, focus my products. Uh, and I got all this, this, this date. I don't know what to do with it, but I know there's some gold in there. I know there's a signal in that data mining, right? So how do I get it? How can you help me? Well, it's gap analysis. First off, I would come in and I would sit down and first of all, I need to see what variables you're collecting. Uh, if you're telling me you you're collecting your name, address and phone number, but you want to do a predictive model, we can't get that. So, um, you know, the question that you want to answer is, is most important? Are you wanting to increase your sales? Are you wanting to get your, to know your customers better, to be able to service them better? >>Like in the healthcare industry, you know, you really want to know what's going on health wise, you know, so, uh, I sat down with them when we do a gap analysis, what are you missing? What do you have? How can we get it? What do you want? Where are you at? Yeah. And here's, here's what you have, here's what you're missing. How do we get at that? And that's oftentimes starts with data sources. Exactly. So then you go get the data sources and then more than what you do, well then we merge it back in. And here's the thing, you have to have that way to connect them. You know, the relational databases will always exist to where you have, you know, client information here and you've got other information over here and you have to always bring that back together. So, um, you know, it's a wonderful time. >>You're a data hacker in a sense, right? Is that fair data nerd in a complimentary way? I mean hacking is about exploration. Yeah, exactly right. So I mean, so you have the skillsets as a data scientist to pull all this data together, analyze it and well, you're going to bring in an external source and then when you bring it externally, you want to make sure that you can match it back up. And now that's the important and without a unique quantify or how do you do that? And that's why when you see databases with all these little arrows and everything pointing to where things belong, I mean we have to be able to pull that in to make decisions. >>Yeah. We were talking with frons yesterday to another influencer. We were talking about this particular point. He was ex P and G back in the day, which is very data-driven. Of course, they're well known for their brand work and certainly on the advertising side, but they're, they're quant jocks over there. They love data. Their data nerds over there, they're kicking out on data. And he used to say that the software would cut off data points that were skewing way outside the median. And so they would essentially throw away what are now exploratory points. So this kind of brings up this long tail distribution concept where, okay, you can get the meat of what you want in the head of the tail and distribution, but out into the long tail is all these skew data points that were once skew standard off the standard deviation that are now doorway. So, you know, we're old enough to know that that movie with Jodie foster with contact where they, they find that little white space, they open it up and there's a, a huge puzzle. That's the kind of things that's happening right now. So exactly >>the same thing. Well, yes, yes. I mean, you know, the thing is, uh, you know, a lot of people don't necessarily have the information that they need. So they're seeking it, you know, when they're going to what Avenue, where, where do I go to get this data? You know, and thanks to open source and things like that. You, you know, we've been able to get more information and bring it together than we've ever been able to do before. And I think people now are more open to analysis where it's not necessarily a dirty word. It doesn't necessarily mean you have to go out and spend $300,000 a year to hire a data scientist. You can sit down, you know, and look at what you have and uh, someone else mentioned that. Take the people that you have that know what's going on with your company. You know, they may not be data scientists, they may not be analytical, but they have insights they have. >>There's more of a cultural issue now around playing with data and an experimental sandbox way where you don't need to have the upfront prove the case. And then pre prefabricated systems you can say, I'm going to do some stuff in jest, for instance, bringing in data sources and play with the data. >>Well, and you mentioned, you know, outliners I mean everything when, when you look graphically at data, you expect everything to fall within this little bubble, this, you know, this thing. But when you see, you know, all these outliners going on for me, usually that means a mistake. Okay. So, and if it's not a mistake, it's something that calls attention. So it's definitely not something you just want to toss aside >>talking about creativity because creativity now becomes, you know, uh, uh, an aspect of the job where you gotta be creative, where it's not just being the math geek or being super analytical and you have to kind of think outside the box or outside the query, if you will, to do the exploration. What's the role of creativity in the new model? >>Well before, I think that we always thought of ourself as just being, you know, matter of fact, you know, just the facts please, you know, but now, you know, you can look at things visually and see, you know, and it is an art form to be able to find that sweet spot in the data. And um, you know, before, you know, years and years and years ago when you would take something like that to a CEO, he would say it was messy, you know, so now you get that creative side where you can actually make things visually attractive. And I think that's important to people too because it's not just data, it's the way you present it. >>It's also the mindset of understanding MSCI is a good start, start with messy and then versus getting the perfect answer. As we were saying, using it with pop-up Jana earlier about, don't try it at the home run right away. Hit a few singles. He's in the baseball metaphor given the world series going on. So totally awesome. Um, but I want to get your final thoughts as we wrap up the segment here on the practitioners out there. What's, what should they do? So there's an approach to the job now, right? So there is a shift and inflection point happening at the same time. What advice would you give to folks out there who say, Oh, I love Carla's interview. I want to do that. I just don't know where to start, what to do. How do I convince management I want to be, I want to get going. What do you, what would you share for advice? >>Well, I'm sure it's the platform. I mean, you know, think about the foundation of a house. Now if you have a strong data foundation, you can build on that. It's just like your house. If you have a weak foundation, your house is going to tumble down. So if you have a strong, you have a strong foundation or with your data and everything is built right now. When I say built right means, what are you trying to do? What are you trying to accomplish? You know, if it's risk, then you need to be, you know, looking at those, those factors. You know, how many people have been hurt? How many of you people been injured? You know, how many people died? You know, I mean, how many breaches do we have? You know, so it starts with the question, what is it that you're trying to accomplish? And then you go from there and collect the right variables. So don't wait, you know, a year later and call a data scientist and going, I've spent, you know, millions of dollars on this. I'm still not getting what I want. So think about an initially in the setup and you know, be involved, involved your analyst, involve your data scientists, make sure that they're in your business meetings because we're the liaisons between it and the Csuite. >>Yeah, and that's the key roles team as a team, that person really is collaborative. We heard from a med earlier pair programming pair, not pay eggs in an accent, pair programming, work in pairs, buddy system. This is really a true team effort. >>Well, I always said, you know, I am a team of data. Scientists can write programs, we can glean insight, but the team part has to come from working with it and working with your C suite. So very much agree. It's definitely a team sport. >>Carla Gentry, owner and data science analytical solutions influencer here at the IBM special presentation and second experience, second screen here in the social media lounge. Really doing a real innovative social business. Again, activated audience, you're an influencer, but also you're really a subject matter expert. Thanks for coming on the cube. Really appreciate and thanks for hosting the crowd. Chat with Brian Fonzo is really good content now. This is the cube. We are live here in Las Vegas. Extracting the ceiling from the noise, getting the data and sharing it with you. I'm John Frey with Dave a lot there. We'll be right back after this short break.

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