 Live from Las Vegas, it's theCUBE, covering IBM Think 2018, brought to you by IBM. Hello everyone, welcome to theCUBE here at IBM Think in Las Vegas at the Mandalay Bay. I'm John Furrier, the host of theCUBE. We're here in this CUBE studio as a set for IBM Think. My next guest is Jennifer Shinn, who's the founder of Eight Path Solutions, Twitter handle Jen J, S-H-I-N. Great to see you, thanks for joining me. Yeah, happy to be here. I'm glad you stopped by, I wanted to get your thoughts, your thought leader in the industry, you've been on multiple CUBE panels, thank you very much, also CUBE alumni. IBM with the data center of the value proposition, the CEO's up on stage today saying, get data and you got blockchain and you got AI, which is essentially infrastructure of the future, and AI is software of the future, data's at the middle. Dave and I are talking about that as the innovation sandwich. The data's being sandwiched between blockchain and AI, two super important things, and she also mentioned Moore's Law, faster, smaller, cheaper, every six months doubling in speed and performance, and then Metcalf's Law, which is more of a network effect, kind of teasing out token economics, you see kind of where the world's going, this is interesting positioning from IBM, I love when I like it, is it real? Well, it sounds very data science-y, right? You have the economics part, you have the networking, you have all these things that you're playing. So I think it's very much in line with what you would expect if data science actually sustains itself, which thankfully it has. And I think the reality is, we like to boil things down into nice little simple concepts, but in the real world, when you're actually figuring it all out, it's going to be multiple effects. It's going to be a lot of different things that interact. And they kind of really teased out their cloud strategy in a very elegant way. I mean, they essentially said, look, we're into the cloud and we're not going to try to, they didn't say it directly, but they basically said it. We're not going to compete with Amazon head-to-head. We're going to let our offerings do the talking. We're going to use data and give customers choice with multi-cloud. How does that jive for you? How does that work? Because end of the day, I got to have business logic. I need applications, you know, whether it's blockchain, cryptocurrency, or apps, the killer apps now money. If no one's making any money, no commerce is being done. Right. I mean, I think it makes sense. You know, Amazon is such a stronghold in the infrastructure part, right? Being able to store your data elsewhere and have it be cloud. I don't think that was really IBM's core business. You know, a lot of, I think their business model was built around business to business relationships. And these days, one of the great things about all these data technologies is that one company doesn't have to do all of it. Where you have partnerships and actually partners so that, you know, one company does AI. You partner with another company that has data. And that way you can actually both make money. Right. There's more than enough work to go around. That much you can say having worked data science teams, right? If I can offer some of my work to different divisions, fantastic. That'd be great. Next time we get to market faster, you know, build things quicker. So I think that's one of the great things about what's happening with data these days, right? There's enough work to get around. And it's beautiful too, because you think if you think about the concept of what cloud, made cloud grade is DevOps. Blockchain is an opportunity to use decentralization to take away a lot of inefficiencies. AI is also an automation opportunity to create value. So you got inefficiencies on the blockchain side and AI to create value, your thoughts and reaction to where that's going to go. You know, in light of the first death of an Uber self-driving car, again historic yesterday, right? So, you know, the reality is right there. We're not perfect, but there's a path. Well, so most of the inefficiency out there, it's not the technology. It's all the people using technology, right? You broke the logic by putting in something you shouldn't have put into that data set. You know, the data is now dirty because you put in things that the developer didn't think that you put in there. So the reality is we're going to keep making mistakes and there'll be more and more opportunities for new technologies that help chew that up. And maybe- So I was talking with Rob Thomas, GM of the analytics team. You know Rob, great guy. He's smart, he's also an executive, but he knows the tech. He and I are talking about this notion of data containers. So with Kubernetes now front and center as an orchestration layer for cloud and application workloads, IBM has an interesting announcement with this cloud private approach, where data is the central thing in this because you got things like GDPR out there and the regulatory environment not going to get any easier. You got blockchain crypto. That's a regulatory nightmare. We know a GDPR, that's a total nightmare. So this is happening, right? So what should customers be doing? I mean, in your experience, customers are scratching their head. They don't want to make a wrong bet, but they need good data, good strategy. They need to do things differently. How do they get the best out of their data architecture knowing that there's hurdles and potential blockers in front of them? Well, so I think you want to be careful what you select and how much you're going to be indebted to that one service that you selected, right? So if you're not sure yet, maybe you don't invest all of your budget into this one thing that you're not sure is going to really be what you want to be paying for in a year or two, right? So I think being really open to how you're going to plan for things long-term and thinking about where you can have some flexibility where certain things you can. For instance, if you're going to be in an industry that is going to be strict on regulatory requirements, right, then you have less legal room. They say an industry where that's not going to be as much of an absolute necessary part of your technology. Let me ask you a question, being kind of a historian and has, you know, what's it, one year is like seven dog years or whatever the expression is in the data space. It just seems like yesterday that Hadoop was going to save the world. So that as kind of context, what is some technologies that just didn't pan out? Is the data lake working? I mean, what didn't work and what replaced it if you could make an observation? Well, so I think that's hard because the way I understood technology is probably not the way everyone else did, right? I mean, you know, at the end of the day, Hadoop, it just is like a way of being able to store data, right? And being able to use, you know, more information store faster, but I'll tell you what I think is hilarious. I see people using Hadoop and they're running SQL queries the same way we did like 10 plus years ago, same in efficiency, and they're not leveraging the fact that Hadoop, where they're treating it like I'm going to create eight million tables and then use joins. So they're not really using the technology. I think that's probably the biggest disappointment is that without knowledge sharing, without education, you have people making the same mistakes we made when technology wasn't as efficient. I mean, if you're a hammer, everything looks like a nail, I guess, if that's the expression. Okay, so on the exciting side, what are you excited about in technology right now? What are you looking at that's a, you know, the next 20-mile stare of potential goodness that could be coming out of the industry? So I think anytime you have better science, better measurement. So measurement's huge, right? If you think about media industry, right? Everyone's trying to measure. I think there was an article that came out about some of YouTube's failures about measurement, right? And I think in general, my Facebook is very well known for measurement. That's going to be really interesting to see, right? What methodologies come out in terms of how well can we measure? I think another one would be, say, targeted advertising, right? That's another huge market that a lot of companies are going after. I think what's really going to be cool in the next few years is to see what people come up with, right? It's really the human ingenuity of it, right? We have the technology now, we have data engineers. What can we actually build? And how are we going to partner to be able to do that? And there's new stacks that are developing. The thing about the e-commerce stack, it's a 30-year-old stack, and ad-tech, and DNS, and cooking. Now you've got social network effects going on, and mentioning the MedCash law. So with all that, I want to get your, just your personal thoughts on blockchain, beyond blockchain, token economics, because there are a lot of people who are doing stuff with crypto, but what's really kind of pointing as a mega-trend standpoint is a new class of decentralized application developers are coming in, okay? They're dealing with data now on a decentralized basis. At the heart of that is the token economics, which is changing some of the business model dynamics. Have you seen anything, your thoughts on token economics? So I haven't seen it from the economic standpoint. I've seen it more from the, sort of the, I guess the algorithms and that standpoint. I actually have a good friend of mine who's at Yale, and she actually runs the, she's executive director of their corporate law center. So here's some from her legal side. I think what's really interesting is that there's all these different arenas, legal being a very important component with blockchain, as well as from the mathematical standpoint. You know, when I was in school, we back when, we studied things like hash keys and RSA keys. And so from the math standpoint, that's also a really cool aspect of it. So I think it's probably too early to say for sure what the economics part is going to actually look like. I think that's a little more long term. But what is exciting about this is that you actually see different parts of businesses, right? Not just the financial sector, but also the legal sector, and then say the math and algorithms and you know, having the integration of being able to build cooler things for that reason. Again, the math is certainly exciting. Machine learning, obviously, you know, that's well documented, that growth and success of what, and certainly the interest are there. You're seeing Amazon celebrating all the time, I just saw Vernard Vogels, the CTO, talking about another SageMaker success. They're looking at machine learning that way. You got Google with TensorFlow. You got this goodness in these libraries now, that are now in the community. It's kind of a perfect storm of innovation. What's new in the ML world that developers are getting excited about that companies are harnessing for value? You seeing anything there? Can you share some commentary on the current machine learning trends? So I think a lot of companies have gotten a little more adjusted to the idea of ML. At the beginning, everyone was like, oh, this is all new. They loved the idea of it, but they didn't really know what they were doing. Right now they know a little bit more. I think in general, everyone thinks deep learning is really cool, neural networks. I think what's interesting though is that everyone's trying to figure out where's the line, what's the difference between AI versus machine learning versus deep learning versus neural networks. I think it's a little bit fun for me just to see everyone kind of struggle a little bit and actually even know the terminology so we can have a conversation. So I think all of that, just anything related to that sort of, when do you use TensorFlow? What do you use it for? And then also even say from Google, which parts do you actually send through an API? I mean, that's some of the conversations that I've been having with people in the business industry. Like which parts do you send through an API? Which parts do you actually have in-house versus being able to outsource out? And that's really kind of, you're thinking there is what around core competencies where people need to kind of own it and really build the core competency and then outsource where it's more ephemeral in value. Is that, or is there a formula, I guess, to know when to bring it in-house and build around? What's your thoughts there? Well, part of it I think is scalability. If you don't have the resources or the time, sometimes time, if you don't have the time to build it in-house, it doesn't make sense to actually outsource it out. Also, if you don't think that's part of your core business, developing that within in-house to, you're sending all that money and resources to hire the best data scientist, it may not be worth it, because in fact, maybe the majority of your actual sales is with the sales department. They're the ones that actually bring in that revenue. So things, it's finding that balance of what investment's actually worth it. And sometimes personnel could leave and then you could be a big problem. Someone walks out the door, gets another job because it's a hot commodity to be. That's actually one of the big complaints I've heard is that we spend all this time investing in certain young people and then they leave. So I think part of this is actually that human factor. How do you encourage them to stay? So let's talk about you. How did you get here? School, interest, did you go off the path? Did you come in from another vector? How did you get into what you're doing now and share a little bit about who you are? Yeah, so I studied economics, mathematics, creative writing as an undergrad and statistics as a grad student. So, you know, kind of perfect storm. Natural math, bring it all together. But, you know, it's funny because I actually wrote about and talked about how data is going to be this big thing. This was like 2009, 2010. You know, people didn't think it was that important. You know, I was like the next three to five years, mathematician has been a hot hire. Don't believe me. So I ended up going, okay, well, the economy crashed. I was in magic consulting in finance, private equity hedge funds. Everyone swore, like, if you do this, you're going to be step for life, right? You're on the path, you make money and the economy crashed, all the jobs went away. And I went, maybe not the best career choice for me. So I did what I did at companies. I looked at the market and I went, where's their growth? I saw tech had growth and decided I'm going to pick up some skills I've never had before, learn to develop more and learn. I mean, in the beginning, I had no idea what an application development process was, right? I'm like, what does that mean to actually develop an application? So the last few years, I've really just been spending, just learning these things. And what's really cool though, is last year, one of my patents went through and I was able to actually launch something with Box at their keynote. So that was really awesome. Awesome. So it became a long way from just, I think, having the academic knowledge, to being able to apply it and then learn the technologies and then developing the technology. And that's a good path because you came in with a clean sheet paper, you didn't have any dogma of waterfall and older technology, so you kind of jumped into it. Did you use, like, a cloud to build on? Was it Amazon? Oh, that's funny too. Actually, I do know legacy technology quite well because I was in corporate America. Oh, okay, so you know. Yeah, so like SQL, for instance, like when I started working data science, which, funny enough, we didn't call it data science, we just called it, like, whatever you call it, you know, there was no data science term at that point. You know, we didn't have that, you know, idea of what, whether you use R, I thought, I mean, I've used R over 10 years, but it was for statistics. It was never for, like, you know, actual data science work. And then we used SQL in corporate America. When I was taking data science, like, in 2012, around then, everyone swore that, no, no, they're going to be programmers, they're going to be programming. Too much, I'm like, really, in corporate America, I've got programmers. I mean, think of how long it's going to take to get someone to learn any language. And of course, now everyone's learning Exxon's SQL again, right? Isn't it fun to, like, when you see someone on Facebook or LinkedIn, oh man, Dave, there's a new oil. And then you say, yeah, here's a blog post I wrote in 2009. Right, yep, exactly. Well, so funny enough, like, Ginni Rometti today was saying about exponential versus linear. And that's one of the things I've been saying over the last year about, you know, you want exponential growth, because linear or anything can take. That's a tweak. That's not really growth. Well, we value your opinion. You've been great on the queue, great to help us out on those panels. Got a great view. What's going on with your company? What are you working on now? What's exciting you these days? Yeah, so one of the cool things we worked on, I think, it's very much in line with what the IBM announcement was about being smarter. Right, so I developed some technology in the photo industry, digital asset management, as well as just being able to automate the renaming of files. Right, so you think, you probably put your digital camera, you never moved over. Because you don't remember the process, you open it, you rename it, you save it, you open the next case number. And sometimes the same number, I got the same version files as the nightmare. Exactly, so I basically automated that process of having all of that just automatically renamed. So the demo that I did had 120 photos, we renamed in less than two minutes. Right, just making it faster and smarter. So really developing technologies that you can actually use every day and leverage for things like photography and some cooler stuff with OCR, which is the long-term goal, to be able to allow photographers to never touch the computer and have all of their client's photos automatically uploaded, renamed and sent to the right locations instantly. How did you get to start that app? Just, are you into photography? Or was more of, I have a picture problem and I got to fix it or? Well actually it's funny, I had a photographer taking my picture and then she showed me what she does with the process. I went, this is not okay. You can do better than this. So I can code, I basically went to Python and went, all right, I think this could work. Built a proof of concept and then decided to patent it. Awesome, well congratulations on the patent. Final thoughts here with IBM Think, overall sentiment of the show, Jenny's keynote, you get a chance to check anything out. What's the hallway conversations like? What are some of the things that you're hearing? So I think there's a general excitement about what might be coming, right? So a lot of the people who are here are actually here to share notes, right? They want to know what everyone else is doing. So that's actually great. You get to see more people here who are actually interested in this technology. I think there's probably some questions about alignment, about where does everything fit? That seems to be a lot of the conversation here. It's much bigger this year, as I'm sure you've noticed, right? It's a lot bigger. So that's probably the biggest thing I've heard. There's so many more people than we expected there to be. I like the Big Ten event, it's been a fan of it. I think if I was going to be critical, I would say they should do a business event and do a technical one under the same kind of theme and bring more alpha geeks to the technical one and make this much more of the business conversation because the business transformation seems to be the hottest thing here, but I want to get into the down in the weeds, get down and dirty. So I would like to see too, that's my take. I think it's really hard to cater to both. Like whenever I give a talk, I don't give a really nerdy talk to say a business crowd. I don't give a really business talk to a nerdy crowd. You know, it's just you have to know, right? I think they both have a very different sensibility. So really, if you want to have a successful talk, generally you want both. Jennifer, thanks so much for coming by and spending some time in the Cube. Great to see you and thanks for sharing your insights. Jennifer Shin here inside the Cube at IBM Think 2018. I'm John Furrier, host of theCUBE. We'll back with more coverage after this short break.