 Live from Cube headquarters in Palo Alto, California. It's the Silicon Valley Friday Show with John Furrier. Hello everyone, welcome to the Silicon Valley Friday Show. I'm John Furrier. My guest here is a good friend, John Luke Chatelain, who's the CTO of Accenture Analytics. I've known John Luke for a long time, great, great person. Former executive at HP, back in the day when they actually had great cloud, great technology, great M&A. Now they're actually going on different M&A strategies, the spin merges, and you had a great journey. We've known each other for a long time, great technical, but also a business acumen. Thanks for spending time. Thank you. So John Luke, I got to ask you, you were on my first cube in 2011, 2011, the extraction point, and a lot's changed. And you and I had conversations over breakfast many times around the transformation in the industry. But here in Silicon Valley right now, you can't get more excited. And there is so much AI, artificial intelligence or augmented intelligence. This is really the big conversation. And every venture capitalists I talk to, every startup has an AI angle. Every company has an AI product. Where did AI come from? It just came out of nowhere, but it's certainly AI washing. People are using AI as a way to try to pump up their relevance. But this is a sea change. I mean, we're seeing the industry transition from what has been a 20 year run of IT and data centers, the glass house from the mainframe to the PC growth. And then really that demarcation really came in the mid 2000s with the iPhone and cloud coming on the scene. You're now seeing a complete transformation of business. Analytics are the heart of it. And that's what you're doing. So I want to get your take. Do you think AI has hyped up right now at an all time high or has it got more hype to go? And two, what actually is AI? No, fair enough. So is there more hype? Sure. I said AI is a new big data, right? But the good thing is AI hype is really justified because the result that we see coming out of AI are pretty outstanding. And we finally are at a point in time where all that work that has been done in AI for the past 60 years in stirring is possible. And that now unleash a huge set of capabilities and it's gonna make your life and my life much easier, that's for sure. It's interesting too. And we're going to get into some of the work you're doing at Ascensure because Ascensure has been, grew up from that old big six accounting days when those guys did all the early mini computer, client server software rollouts, ERP, CRM. But now with the market with cloud, Ascensure has got this huge practice of big data and have the zillions of data scientists. And we're going to get into that. We've talked about that before. But before we do that, the thing that we're seeing right now is that the computer science explosion around software is really centered around machine learning. And machine learning has been around for a while, right? And the concept of neural networks is getting everyone super excited. That's been around for a while. It's been around for a while, yeah. So I got to ask you, and I made a comment on Facebook the other day and Twitter and I said, you know, someone's talking about machine learning, you know, it's hot. And I said real computer scientists write their own algorithms as well as take advantage of other libraries. So talk about that dynamic because algorithms are hot again. You're hearing people calling those algorithms, you know, as a new title. And that's important with machine learning because you can certainly borrow a lot of machine learning libraries out there. But in the day, the best part of machine learning and augmented intelligence is actually writing custom algorithms. True, and it's, so I think there's two sides of it. There's the fundamental science behind AI and there's outstanding work that is being done by some of our clients and our partner in the industry like Facebook and Google and Microsoft and Amazon. They do a lot of the fundamental work and the work of Yarn and Coon, for example. I said, it's pretty amazing, or Hinton, at Google. And then there is the industrialization of that work. And I think that's where it's really interesting because the neural network is a neural network and neural network, but it really, you get the power of it when you start teaching that neural network how to behave in a given industry situation. So there is a lot of generic application that are really amazing. I mean, look at the Google car, right? I mean, what they do in video analytics blow you away, right? But there's also a way that you can use those fairly modern algorithms to work on very mundane tasks that are difficult to solve, like tech mortgage processing. What if we could teach a neural network to understand the six inches worth of paper that you have in your mortgage and distill that to something a human can understand. And if you bring that to a bank, how can a bank understand the quality of the mortgage that they have by reading those documents using AI? And that's where the coding comes into play, all the training and tuning that algorithm to work for that given industry. How early are we in this analytics trend? I mean, people are certainly excited about it. My young son is 21 and a lot of young people are getting into the business and software is attracting a lot of people into computer science, which is fantastic, but you've seen a lot of cycles of innovation. Where are we? Peg Progress Bar, we're analytics. You remember in 2011, we spoke about this and I guess you and I were pre-scient, right? Because you said, where should I send my kid to college? What should I major in? I said mathematics because data scientists are going to be very accommodative. So we are in a stage now where analytics are being understood by at least the CXO level. They understand that being a data powered enterprise, leveraging analytics to derive business changing outcome, is there. Now, we are not there here in the total industrialization and deployment throughout all the firm, but the awareness of the good of analytics is there. And that, to me, it's very important. And it's the best time to be in this industry. I don't want to retire, I don't want you, but... I wish I was 21 again because, I mean, there's so much great action in terms of development. It's totally cool to be writing code and there's so much range now in what you could work on. It's not just writing code and programming. You can actually work on architecting great stuff. And it's so great, I mean, but you're seeing the transformation and here's my big thing. I've been talking about this in theCUBE in my show for, since we started doing this a couple months ago, is that the impact to society is huge. I mean, just this week, we saw the United Airlines guy getting yanked off the plane because they were overbooked. It was the dumbest thing I've ever seen because they could have easily solved this with really good analytics. And so their business is under fire now. They lost a billion dollars in value because of this incident. Forget the lawsuit from the guy who was dragged off, but the point is it could have been avoided. It could have been avoided more than actually with the right kind of analytics. Maybe it boils down, you know, one of the big issue in analytics today, it boils down to availability of skills also. That there's a lot of aspiration that companies have to do something with analytics and they stumble in the fact that they don't have the data scientist so they don't have the IT department that understand the complexity that is behind the scene to make some using AI or using any form of... Well, this is an example of, to me, I use that, I use that because it's in the news, it's hot, everyone's talking about it, it went viral, and it's just not stopping, it's just being trolled by their competitors. But this is happening to a lot of businesses. This isn't united, it's just one that we know about because it's front and center in the world right now. People fly a lot and people are outraged and it's a tragedy, really. But this is an example of what's happening to businesses everywhere, by not having the right data, not having the right analytics, they're not incorporating data into their business and stuff is failing. Right, they're not powering their business by data. I think that's the issue, right? They're using data, they've been using, you know, way to price, for example, their tickets for a long, long time, right? But that's about using data as opposed to understanding that you're going to power your firm by leveraging data. And it's a change of behavior. But we, I think, enterprise today is still like the tri-ball model, right? Oh, I know what I've been doing for the past 20 years and I'm an expert, and all that. Daniel Warehouse, blah, blah, blah, blah. But you know what? You have to reinvent yourself every day now as a human being, especially in our industry. And firms have to really reinvent themselves and understand that unless you start making decision based on data, right, you're going to make the wrong decision. That's no doubt, and there's a war going on at this data layer we're seeing at the cloud. And so I got to ask you, I mean, obviously you see the big cloud providers. You've got Google, Microsoft, Amazon, Oracle. This should not be discounted because they're, you know, they have database advantage and they've got some good numbers, seeing some traction there, trying to get into the cloud native world. But if you think about the evolution of the industry, right, we mentioned from, just take some dates. 1980 to 1995, internet. You had TCP, IP was really a kind of an enabler around standardizing the network stack, right? Everything else was still proprietary and applications were above the IP layer. So everything got standardized, it created massive growth. And of course you had the PCs coming in, Steve Jobs and Bill Gates and Intel, just volumes of PCs being connected. So you had internet working, all that stuff created massive industry. And then it kind of became an IT problem. Yep. Now the same, that's the plumbing. Now the plumbing is turning into machines. So you have machinists versus plumbers and the cloud is that new force. So that is, that transition is happening. So the cloud is over the Wild West, mobile devices are the new PCs. So you have a proliferation of devices, internet of things, PCs. So now the cloud wars are happening where there's jockeying. So I want to ask you just from as a technologist and someone who's out in the field with customers, what has to standardize in the cloud for companies to truly interoperate across multiple clouds, to have full frictionless access to resources from application standpoint? Yeah, I think there's still a need of kind of having a cloud neutral type of set of API so that customers can leverage different clouds without being tied or hocked tied to a given pass layer or something like that. So I think there's still a lot of opportunity here to ensure that your application John can run on CloudX on Monday and CloudY on Tuesday or run on both at the same time so that if one goes down, well you can flip to the other one. I think there's still a lot of efforts to be done and in that neutralization of the API layer to talk to the cloud infrastructure. That's where there's the opportunity. But the cloud's the right place to be. There's no other doubt. And a lot of what has helped is also the level of automation now that we have behind the scene to power those clouds. But this is a place to be and I'm kind of surprised that, well, maybe I should not, but. Say it, go ahead. I'm kind of surprised that there's still a significant amount of enterprise that are afraid of the cloud, right? As that what I call unfounded fear of security in the cloud. They don't know what to do. I mean, my take is they just don't know what to do. They're out of their comfort zone. They're out of their comfort zone. You know, people still like to hug their hardware. I am convinced that some CIOs. You're a hardware hugger. Some industry huggers here in California. You've got the hardware huggers. I think some CIOs in some industry probably go in their data center room and go hug their graduates, right? Well, a lot of times they spend a lot of cash for this stuff, so it's like buying that big car that no one wants anymore and sitting in your driveway and you're like, oh my God, I want a driver. I paid a lot for this month. I got to drive this beast around. Yeah, why don't you take Uber instead, right? It'll be so much easier, right? We're here with Jean-Luc Chetlain, my good friend, CTO of Accenture Analytics, really in the position to see everything in the industry. Sees a lot of customers. Also as a great technologist background, it's been the CTO of HP back in the days. You've had great health experience, seen many cycles of innovation. There's a sea change. Certainly this IT transformation has been this, you know, 20 year operational system and people personnel and process. Now completely being radically disrupted. We use the United Airlines example that could have been avoided and any lean airline should have never had that problem. And they'll be disrupted by another airline invented by a millennial, probably. But this is the issue, right? So how do you hire people? I mean, as someone who's a CTO, you got a lot of, I don't know what's the current number of data scientists on your team, but 1,300, huge amount. What's the age like? What's the background? How do you hire? And what do you look for in these data scientists? When you talk to people, they got to solve problems. It's not so much about the tech anymore. That's kind of like tooling, but you got to build something. There's an outcome involved. So we are very selective in our hiring, right? But we try to find the best out there and regardless of where they are. I mean, we are literally a global company. We will hire people from any country as long as they have the right credential with a master or PhD. For example, we love computer science. We love, of course, we love mathematics, right? But we are not completely closed, right? Because no one has a monopoly of innovation. So what you have to, I think when you talk to a candidate, yes, you have to qualify the beneficiary in terms of education in the space that we're interested, but you have to kind of see that person's got that innovation fiber. And that's really what's important to me. What's your take on Silicon Valley? And I know you've been here. You live in Atlanta now, but you travel all over the place. You practically live here. But there's now a global landscape. You're starting to see the entrepreneurship fabric be global. China and Asia Pacific is growing like crazy. Connectivity is everywhere. Internet of things devices from autonomous vehicles to sensors on airplanes or whatever. People and watches that can detect diabetes are being developed. This is kind of people have the things on their body, the wearables and you've got cars now out there. It's a big data world, but it's not just Silicon Valley anymore. So what's your take on Silicon Valley these days? Well, I think Silicon Valley is still the model, right? I mean, everybody aspires to have the same fabric. It's very untouchable, right? But everybody knows everybody around here, right? And so there is a great ecosystem which is a long time. I mean, it will be a model for a long time, but I'm happy to see the same thing happening in Paris, the same thing happening in Atlanta, happening in New York, happening in Israel or in countries where you're not seeing. Italy, for example, as in a lot of soft world times. We're in Italy. Which city has the most action going on? I would say Rome, Milan and Bologna, all places. One of the oldest university in New York is in Bologna. I think it's probably the oldest university in Paris. And in France, you mentioned Paris. Any place else in France? I think it's a great place to live there and there's a lot of smart people that come on math. It's kind of a place to be. So there's some pockets of innovation in what you traditionally wear industrial space. For example, Toulouse, right? Which is all around aeronautics as some pockets of innovation there, but really it's centered in Paris and the surrounding area. So is there a correlation between food and analytics in terms of horsepower? France, they know how to eat over there. America, you know, we'll get in and out for a bit right out on the corner here. Food, I don't know, but the better the wine, the better you'll know. Better the software. The better the software. That's a golden rule. Let's add butter to the software and a good glass of wine every time. Okay, so I got to ask you about the startup scene because I know that you keep an eye on a lot of trends. You were early on Object Store when you went back to DDN as one of the co-founders of that whole deal. You know the founders there. You saw a lot of trends and you saw the train coming so you were way ahead. Again, what's on the YouTube? We were talking about it in 2011. We were pretty much right when we talked about it. But the startup scene has certainly changed a lot. Hadoop is not what the end game is and CloudArea filed their S1. I was very surprised to see that they had a massive liquidity event in 2015, which means everyone got most of their stock out, but now they're going to go public. Intel's holding the rest of the investment. But Hadoop didn't become the big deal. It became data. And so that shifted to the startup scene. What's the startup scene like in the data area? You see a lot of vertically oriented. Oh, I'm a food thing. I'm doing some point application. Right, so I see a few things. There's a really interesting space. It's what some people call data engineering, right? From the moment you get the raw data from your various operational system or your external data source, to the moment you can make the data consumable by the data scientist and the models, right? That area, which used to be called data integration or data quality, data preparation, was never well regarded. It was kind of like the thing you don't want to do behind the scene. In fact, you'll go higher and I'm sure to do it for you. You've been punished. You do the data wrangling. Yeah, you go do data integration. Scrub the toilets. But this area is really facing what I call a renaissance. It's a renaissance of data preparation as a first citizen of the whole value chain because you don't get good insight out of bad data, right? So in the startup world, we've seen the emergence of many newcomers that want to change data preparation, making it much more accessible to business analysts as opposed to accessible by geeks, right? And think about it, data scientist today, Spain spends 80% of its time or her time in data preparation. Do you think it's well spent? Probably not, right? So there's- Well, talk about the old way and new way because the old way you mentioned was a remedial task. People did it. Like I say, I was joking. You go to Siberia and do data wrangling. But that was more of a blocking and tackling kind of mundane task. And there was known reports and they were kind of slow. Today, the data is really, really important because you have real-time needs. You have real-time. Also, you don't really know where the data value's going to be because the contextual elements of it could change overnight. Yeah, exactly. So there's a strategic role here, isn't there? I think for a long, long time, what under the banner analytics or BI, that it all has been about verifying answers to known questions. And now, with the arrival of big data and powered by capabilities like AI, you can find new question to ask of your data. I think that's really what's transformative. Now, we can get a data scientist to let him swim in the data, for lack of a better word, and get there a hard moment and then operationalize that into the firm. So what's changing, the phases in data preparation haven't changed. You still need to capture data. You still need to clean data. You still need to keep its traceability, its provenance, its lineage. But you need to make it consumable by normal people now. So instead of something which is done in the bowels of IT with, believe it or not, most of them very little understanding of the context when you're in IT is just bits. They don't understand, they're not smear, right? But instead of doing that, you can now with those new tools that are coming about, automate that value chain more and more, and then make that data available in a catalog that you can offer to all of your business analysts, wherever they are in the firm, and they can go and use that data to produce whatever is useful for them. Either it's report, whether it's dashboard, or whether it's calling their friendly data scientist and can't find some new answers in that data that I have. We're here with John Luchat Lane, CTO of Accenture Analytics. We're seeing all the great data work that you guys are doing. Talk about Accenture and how you guys have changed because you guys are essentially building a very agile organization and you're using a lot of the technology. It used to be that Accenture, and I'm paraphrasing, correct me if I'm wrong, but I'm just going to say it, Accenture used to be like a consulting firm where you'd come in and all you do is you get paid and give me your watch, I'll tell you what time it is. That's classic consulting business model. Now you guys are shifted to being a technology provider. Because this is a massive opportunity so you're vertically integrated, your own cloud, your own technology, and then you bring that to customers, now you're not just consulting, you're doing that too, but you're putting it in context to delivering value with tech. Did I get that right? Except for the data watch part. It surprised me. I mean, when they reached out to me, I was like, what, I'm not a system integrator. I've never done that in my life, that's not my job, right? And I realized that it's a company that is able to transform itself in real time, which is really interesting. And it's no longer just a people-powered enterprise. It's a people and platform-powered enterprise. And I think that notion of understanding that platform is a vehicle by which they can help our customers, as we say, turning to the new, right? Changing themselves is remarkable. So it was to me, that is the best-kept secret of the industry, but the level of innovation that is coming out of Accenture in technology, not just in consulting, as you said, but in technology, in the process of helping our customers turning to the new, becoming digital enterprise, becoming information-powered, is really amazing. What's the coolest thing you're working on right now? Coolest thing I'm working on. We do a lot of stuff with drones, right? So we have work being done around, for example, surveying for a plantation and looking for sick trees in the plantation so that we can direct people that are gonna go and either put some medicine on the tree or cut it down because it could rot and be bad for the other. But we can literally send them to the tree that is sick in the plantation, as opposed to have them in jeeps, going back and forth and you're talking plantation but it's a huge tree. Yeah. So they send the drones out with cameras. Send the drone out to camera. We survey the whole forest. And you can detect the decay on the tree. We can detect, we detect also poachers, right? People that come and cut the trees and disease. They fire any rockets off those drones? Those poachers? No, that's... That's coming soon. I don't think it's gonna be in the next release, but... Do a real-time analysis. I got too many Twitter followers, so hold off on that one. But it's really funny because... This is the edge of the network though. Flying drones is a great example. You got cars that are self-driving. You got drones that have intelligence to them. This is a data challenge. Let's just kind of unpack that. A drone's flying out, it's got a camera. It's got to do a sensing, send the transmission back. It's IoT at its best, right? It's IoT at its best. And then you guys have to react to that, make decisions on it. Right. So we, yeah, it's a great example. So there's some interesting technology challenge behind it that it was a lot of fun to solve, is when you have, say, a hundred drones flying and you got to update the model because for some reason, the detection where you're looking for is not happening. You got to go and send models to those drones that are in flight, right? And so that they can have a new model and they start behaving differently or start having better detection of what's going on. And it's not limited to drones. But now you can... Emergency response, you've got agriculture, you've got all kinds of supply chains. Oil and gas, like surveying pipelines, looking for leaks, right? Wires, all that stuff. Talk about block chains, get into something disruptive because now you start bringing in supply chain. What just pops in my head is blockchain. Yeah. I have a lot of... Are you bullish on blockchain? I'm very bullish on blockchain. I don't think we are ready for large-scale industrialization of blockchain yet. Just to have some unknown question. But the idea of, at the end of the day, blockchain is a ledger that's non-temperable. So you always go back to reality. It's like back in the days where, you know, you were writing a big ledger with big pages and you could not tell that off. Yeah, double entry accounting, just to keep track of everything. That goes away. I mean, this blockchain could radically change the nature of the firm concept that was written, you know, by way back in the day, by Coase. I mean, the Coase's theories of nature of the firm was based upon old stuff. Now you have blockchain, you could eliminate. But blockchain can ensure that anything that's happening to a piece of information, right, whatever the transaction may be, is traceable, number one, and it's non-temperable, and therefore non-questionable. Good for supply chain. If you're transporting goods, you can see stuff in flight. Yeah, we can see stuff in flight. Think of, you know, what's called the track and trace, right, in the pharmaceutical industry, for example. You know, there's been a lot of track and trace effort around RFID and all kind of stuff. But what if you could do, you know, use blockchain also as a mean by which you can do blockchain? Are people kicking the tires on blockchain? Yeah, yeah, people are kicking the tires. Financial services are kicking. Are they rolling anything out into production at all, or not yet? I think it's a little soon. Not in large scale. I mean, that's... It's mostly in field R&D, basically, kind of prototyping. Yeah, or POCs, type of thing, POCs, you know, so maybe limited control environment, right? But there's a lot of... I'm bullish on black thing, blockchain. Yeah, I agree. We had some naysayers on my show that said, well, they think blockchain is BS. Here's the thing about blockchain. You need an ecosystem to pull it off. But the money that's on the table, that's disruptible, meaning if we organize together, it's trillions. I mean, it's a trillion dollar land grant if people can organize in an ecosystem way. And I think that will happen, in my opinion. I've seen it before. So I'm totally bullish on blockchain. Final question for you, I want to ask you, when you talk to your friends that aren't CTOs, they know you have a lot of wine parties at your ranch, and they say, John Luke, what the hell is this IoT thing? How do you describe IoT to non-Techies? Well, I tell you... Internet of things, IoT is Internet of Things for the folks watching. Yeah, so what I tell them is that, you know, there's a machine out there, right? Everything is a machine, a cook machine, right? A cook distributor is a machine. Your washing machine is a machine. Your fridge is a machine. And that IoT is, first, is giving a voice to those machines, right? So we enable those machines to speak, to tell us what's in the fridge, what's the temperature, or how, you know, what's going on with your laundry and the washing machine, or, you know, how many cooks are left, or Pepsi, not to favor any vendor, are left in the machine. Not a Pepsi anymore. They had that bad ad that got went viral. And now that machine can tell the supplier, right? That it's about to run out of diet coke, or out of diet Pepsi, right? And so it's, IoT is about giving a voice to machine and listening to them. They have a lot to say, right? So you can listen to the machine, and as a function of what they're telling you, but you can change your behavior. Aren't people IoT too? We are machines, absolutely. That's my favorite line is, when somebody walks into a Starbucks and checks in, they're an IoT device. Yeah, chip implants are coming next, but we are aware, I mean, Internet of Things, that book I used to read to my kid, Thing One and Thing Two, that's the story, but the point is we are things. We have wearables, we're going to have sensor networks on us. Yeah, we have proxies for us, but we are machines. Gesturing out, I mean, I was talking to a guy who runs a bank, and he's like, our future bank, we're using all these fintech startups. We don't, all those core services are moving away off our core network. They're going to become a sensor network with ATM machines and servicing consumers with data. All right, so final, final question for you as we end up here, give you the last word. John Luke Shadlain, CTO of Ascension, here with me. What are you really excited about the next few years? What's, you mentioned earlier, you wish you were, I said I was wish young, you're excited now. What are you really excited about right now, the next five years? What's going down that is just intoxicating for you? I think we can literally put, with the real emergence of tangible machine learning capabilities, I'm a very big believer in convolutional neural networks, that we can now bring a lot of goodness via native to people. The one I love is the work that's been done, for example, in mammography, right? Now you can use deep learning network to read mammograms and avoid a potential disaster for a young lady or that has an emerging cancer that we cannot miss, right? We can't miss it. It's, I think we can help educating people better, right? We can, so that's... Societal benefits. Yeah, exactly. Well, you'd be a good point, just to say. Societal benefits. Well, here's the try and here's what I hear you saying. I think this is what you're saying. We're doing things that we couldn't do for the first time that are just unbelievably awesome. And for the better. And for the better. Right. Because we have more compute and the data's available, it's just in crazy awesome time. Yeah, it's crazy awesome time. Look at what's going to happen with a self-driving car. We're going to minimize the number of accidents, right? Well, the first time legal challenge is society challenge. So great time to be a computer science and sociology major. I mean, don't you think? I agree, right? That's the curriculum of math or computer science. Lots of social good will come out. A lot of critical thinking needs to happen though. We'll pick that for another time. John Luke Shetlain here on the Silicon Valley Friday show. I'm John Furrier, great to have you. CTO of Accenture, good friend, great expert and someone who's been in the industry a long time. I'm John Furrier. Thanks for watching. See you next time.