(bright music) >> Welcome back to Vegas, guys and girls. We're pleased that you're watching theCUBE. We know you've been with us. This is our fourth day. We know you've been with us since day one. Why wouldn't you be? Lisa Martin, here. As I mentioned, day four of theCUBE's coverage of AWS re:Invent. There are north of 55,000 people that have been at this event this week. We're hearing hundreds of thousands online. It really feels like old times, which is awesome. We're pleased to welcome back a gentleman from Dataiku who's actually new to theCUBE but Dataiku is not. Jed Dougherty is here, the VP of Platform Strategy. Thanks to joining me today, Jed. >> Oh, I'm so happy to be here. >> Talk a little bit, for anybody that isn't familiar with Dataiku, tell the audience a little bit about the technology, what you guys do. >> Dataiku is an end-to-end data science machine learning platform. We take everything from data ingestion, piplining of that data, bringing it all together, something that's useful for building models, deploying those models and then managing your ML ops workflow. So, really all the way across. And we sit on top of, basically, tons of different AWS stack as well as lots of the partners that are here today. >> Okay, got it. >> Snowflake, Databricks, all that. >> Got it, so one of the things that, it was funny, I think it was Adam's keynote Tuesday morning. I didn't time it, I watched it, but one of my guests said to me earlier this week that Adam spent exactly 52 minutes talking about data. >> Yeah. >> 52 minutes. Obviously, we can't come to an event like this without talking about data. Every company these days has to be a data company. Whether it's my grocery store or a retailer, a hospital, and so- >> Jed: It is the lifeblood of every modern company. >> It is, but you have to be able to access it. You have to be able to harness it, access it, derive insights from it, and be able to act on that faster than the competitors that are waiting, like, right back here. One of the things Adam Selipsky talked about with our boss, John Furrier, who's the co-CEO of theCUBE, they had a sit-down about a week before re:Invent. John always gets a preview of the show and Adam said, you know, he thinks the role of data analyst is going to go away. Or at least the term, because with data democratization that needs to happen. Putting data in the hands of all the business users, that every business user, whether you're in technology or marketing or ops or finance, it's going to have to analyze data to do their jobs. >> Could not agree more. >> Are you hearing that from customers? >> 100% >> Yeah. >> I was just at the CTO Summit of Bank of America two weeks ago out in California, and they told, their CTO had a statistic, 60,000 technologists in Bank of America, all asking data-type questions. You can have the best team of data scientists in the world, and they do. They have some of the best data scientists in the world there. And this team of data scientists could answer any one of the questions that those 60,000 people might have but they can't answer all of them, right? You need those people to be able to answer their own questions. I don't know if the term data analysts are going away. I think, yeah, everybody's just going to have to become a bit more of one. Just like how Excel taught everybody how to use the spreadsheet, in the future, in the next five, 10 years, the democratization of AI means that tools like Dataiku and other data science tools are going to teach everybody how to analyze data. >> Talk about Dataiku as a facilitator of that, of that democratization. Giving, like the citizen technologist who might be in finance, the ability to do that. >> So, a lot of data science tools are aimed at your hardcore coder, right? Somebody who wants to be sitting at a notebook writing (indistinct) or something like that and running models on some big fancy Spark server. Dataiku is still going to be running models on some big fancy Spark server but we're really obfuscating the challenge of writing code away from the user. So we target low code, no code, and high code users all working together in a collaborative platform. So we really do, we believe that there is always going to be a place for data scientists. That role is not going away. You will always need hardcore coders to take on those moonshot very challenging topics. But for every day AI, anybody should be able to do this and it should be open to anybody. >> Right. >> Jed: Really aim to facilitate that. >> I would love to hear some feedback, you know, this is day four of the show as I was saying, and day four is packed. I mean, this is energy-level-wise, guys, it is the same as it was when we started here on Friday night. But I'd love to hear, Jed, from your perspective some of the customer conversations that you've had, what are some of the challenges? They're coming to you saying, "Jed, Dataiku, help us eradicate these challenges so we can transform our business." >> What I'm hearing from customers and partners and AWS here is, over and over, we don't want to buy tools anymore. We want to buy solutions. We want a vertical solution that's pre-built for our industry. And we want it to be, not necessarily click and run out of the box, but we want a template that we can build off of quickly. And I've heard that customers are also looking to understand how tools can be packaged together. You got how many booths are here? 1000 booths? >> Yes, easily. >> You have 1000 different products being talked about, right behind us. Customers need to know which of these products are friends with each other and how they fit together so that they are making sure that when they purchase a set, a suite of tools to do their jobs, it's all going to work naturally together. So, being able, I think this is a really vital concept for GSIs as well. GSIs needs to understand how to package sets of tools together to deliver a full solution to clients. People don't want to be, you know, I think 10 years ago, five years ago, AWS was in the business of selling servers in the cloud. But basically what you do is, you would buy an EC two instance and you install whatever software you wanted on it. I don't know that they're in that business still but customers don't want to buy servers from AWS anymore. They want to buy solutions. >> Right. >> Rent, whatever. >> Yeah. (chuckles) >> That is the big repeated message that I've heard here. >> So you brought up a good point that there are probably 1000 booths here. You could be here every day and not get to see everything that's going on. Plus this show was going on across the strip. We're only getting a fraction of the people that are here. But with that said, to your point, there are so many tools out there. Customers are looking for solutions. One of the things that we say about theCUBE is, we extract the signal from the noise. How does Dataiku get past the noise? How do you get up the stack to really impact customers so they understand the value that you're delivering? >> I think that Data science and ML sound like a very complicated topic but our value prop is relatively simple. And we appeal both to your end users who are excited to learn about how data science works and how they can leverage these tools in their day-to-day jobs, as well as appealing to IT. IT, right now, at major organizations they want to be able to build a full stack that makes sense. And the big choices they're making right now are around infrastructure. Where am I going to run my compute? So, they're choosing between Snowflake or Databricks or a native AWS compute solution, right? And so they make this big choice around compute and then they realize, "Oh, how many of our users across our organization are actually able to leverage this big compute choice?" Oh, maybe 100, maybe 200. That's not incredibly useful for what we've just decided to completely stand behind. Dataiku, all of a sudden, opens that up to 1000s of users across your organization. So it makes IT feel empowered by being able to help more people. And it makes users feel empowered by being able to use a great tool and start answering their own questions. >> And where are your customer conversations these days? As we look at AI and ML, emerging technologies, so many customers and companies, knowing we have to go in this direction. We have to have AI to speed the business. Are you seeing more of the conversations are still in IT or are they actually going up the stack? >> (chuckles) It's a great question. When you're going into large organizations, there's two sales motions, right? There's convincing the business users that this is a great thing and then convincing IT that it's not going to be too painful. You always have to go to both places. IT doesn't want to take on a boondoggler, or there's an albatross, I don't remember the word, but, something that they're going to have to deal with for the next 10 years and then eventually dismantle and pull apart. I think a lot of IT got very scared about big data platforms and solutions because of Hadoop. To be honest, Hadoop was incredibly powerful but maybe not as mature of technology as IT would've liked it to be. From a maintenance and administration standpoint. So yes, you will always have to sell to IT and help IT feel comfortable with the platform. But no, the conversations that I want to have are the use case conversations with a Chief Data Officer, Chief Revenue Officer, Chief Marketing Officer. That's who I really want to convince that this is going to be a worthwhile opportunity. >> And what are some of the key, sorry. What are some of the key use cases that Dataiku is tackling in the market these days? >> So we work a lot. Two of the biggest organizations, or verticals, that I work with personally are finance and pharmaceuticals. In finance, we are closely embedded with wealth management organizations. So, a lot of that is around customer entertainment, churn, relatively obvious, simple concepts but ones where it's worth a lot of money. In pharma, we work both on the supply side. So, doing supply chain optimization, ensuring the right drugs get to the right places at the right time. As well as on the business and marketing side. So, ensuring that your ad spend is correctly distributed across different advertising platforms. >> So if you're working with a financial organization, I want to understand from a consumer, from the end user's perspective, although obviously this technology impacts the end user who's trying to do a transaction. What's in it for me? And I don't know as the end user that Dataiku is under the hood. >> You'd never know. >> Which is good. I shouldn't have to worry about the technology. >> Jed: You shouldn't have to worry about that at all. >> What's in it for the end user customer? What are they gaining from this? >> So, from a very end user perspective, if you think about when you logged onto maybe your Bank of America, your Chase app, five or 10 years ago, maybe you didn't even have it on your phone five years ago. Or when you logged into your account online. We do 95% of our banking online right now, right? I go into a physical location, what? I don't know, once every six months or something? Get a cashier's check? I don't know. The experience that you're getting and the amount of information you're getting back about your spending habits, where your money is going, what your credit score is, all of these things are being driven by these big data organizations inside the banks. Also, any type, this is a little creepier, but any type of promotional emails or the types of things that you get feedback on when you use your credit card and the offers that you get through that, are all being personalized to you through the information that these banks are collecting about your spending habits. >> Yeah, but we want that as a consumer, we want the personalized. >> Yeah, of course. We want it to be magic slash not creepy. (laughs) >> Right, I want them to recommend the best card for me. >> Right. >> The next best thing. >> It's good for me, it's good for them. >> Don't serve me up something that I've already bought. That always bugs me when I'm like, I already bought that. >> I get that all the time. I'm like, yeah, I have that card already. It's in my wallet. Why are you telling me? >> We only have a couple of minutes left Jed, but talk to me about from a platform strategy perspective, what's next for Dataiku and AWS? >> So we are making a matrix transition right now and it's core to our platform. For a long time, the way that we've installed Dataiku is, we help our customers install it on their AWS account so it runs inside their tenant. This is very comfortable for, for example, large banking clients, pharma clients that have personally identifiable information, all that kind of thing. They own everything. However, as we were talking about before, we're really moving from providing a tool to providing solutions. And part of that is obviously a move to SaaS. So two years ago we released a SaaS offering. We've been expanding it more and more to, this year, we want to be pushing SaaS first. So Dataiku online should be the first option when new customers move on. And that is a huge platform shift. It means making sure that we have the right security in place. It means making sure that we have the right scaling in place, that we have 24-7 support. All this has been a big challenge. A big fascinating challenge, actually, to put together. >> Awesome. Last question for you. Say you get a brand new DeLorean, I hear they're coming back, and you want to put, you really, really want to put a bumper sticker on it, 'cause why not? And it's about Dataiku and it's like a sizzle reel kind of thing. >> A sizzle real, alright. >> Yeah. What does it say? >> Extraordinary people, everyday AI. >> Wow. Drop the mic, Jed. That was awesome. Thank you so much for coming on the program. We really appreciate the update on Dataiku. What you guys are doing for customers, your specialization and solutions for verticals. Awesome stuff, we'll have to have you back. >> Thank you so much. >> Alright, my pleasure. >> Bye-Bye. >> For my guest, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (bright music)

>> Narrator: From the Computer History Museum in Mountain View, California, it's The Cube, on the ground with Accenture Labs' 30th anniversary celebration. >> Hello, everyone, welcome to the special coverage of The Cube, on the ground here at the Computer History Museum in Mountain View, California, the heart of Silicon Valley. It's The Cube's coverage of Accenture Labs' 30th year celebration. I'm here with Paul Dougherty, the chief technology and innovation officer at Accenture Labs. Welcome to The Cube conversation. Thanks for joining me. >> It's great to be here. >> So first I want to toast you guys to 30 years from turning to an accounting firm, Arthur Anderson, to Accenture Labs Consulting. Guys are really changed. Congratulations to all your success. Thanks for having us. >> Yeah, thanks, it's been an incredible journey. If you think back in the 30 years, it's the 30th anniversary of Accenture Labs, and the transformation of our company to now be an innovation-led company, leading in IT services and IT innovation, and with the amazing innovations that are happening in technology, it's a great time to be doing what we're doing. >> So the theme here at the party is magic. There's a magic show going on. We can't get coverage. It's a little private event, probably some G-rated, probably ... >> Lots of magic. >> A lot of magic. But there's magic right now. We were commenting earlier, before you came on, about, you know at my age, I love this innovation cycle, but if I was 20 years old, I'd really be excited. There's so much going on. It's really magical. You've got the convergence of infrastructure, cloud, software. You guys have been on all sides of innovation, from the mini-computer boom, all the way now through now, where AI and software and now data science is coming together. What's the exciting thing for you right now? Because it's beyond software eating the world, it's beyond data eating software. This is real applications. >> Yeah, this is ... We're at an era where technology is the driving force behind every business. There was a survey recently of CEOs, and they asked CEOs how do they view their business, and 81% of CEOs, 81%, said their company's a technology company. And that was a cross-industry survey. And that's why it's an exciting time, because the option we have as Accenture is to work with any company, and every company, and help them transform, change their business, and lead them through the transformation to deliver technology-enabled digital products and services. And that's why it's an exciting time. >> What I find exciting about these global system integrators, as they're now called, is that you guys have always been a consultative organization to customers, helping them through their journey of that generational shift. Now it's interesting, with cloud computing, you guys are not only just advising, you're delivering services. A mindset transformation as well as talent, technology, process, and people. How are you doing it? What's the secret formula? >> Yeah, absolutely. I mean, what we found, the reason we've driven our business model in that direction, is our clients need help throughout the cycle. So we help with Accenture strategy, with advising our clients. We help with Accenture consulting, on helping our clients transform. Accenture digital, bring the digital capabilities in. Accenture technology, building the solutions in. Accenture operations, providing business process, infrastructure, and cloud operations. So, we've found that our clients, they need help with it all. They want to understand where to take their business, they want to understand how to get there, and they want somebody to help them manage their business as they do. And that's why we've taken the business in that direction. >> Not to give you guys a lot of props, but I do want to give you guys kudos, Accenture, Accenture Labs, is that all of folks might not know, or some, you guys probably do know, you've accumulated a lot of data scientists over the years. You've got thousands of data scientists, a lot of talent coming in. Accenture Labs is a booming operation, it's not just a throwaway lip-service kind of operation for customers, to say "Hey, we got some smart people." You guys have actually have a real organization. What are some of the cool things that you guys are doing? Can you give some examples? >> Yeah, let's just step back and talk about Labs a bit, and then I'll give some examples. We've been at Labs now for 30 years, hence the celebration we're talking about, and it's thousands of patents, it's billions of dollars of impact on the revenue of our business. And really, you're driving innovation that sets us ahead in the marketplace. And it's a fabric of a global organizations. We have labs here in Silicon Valley. We have labs in Washington, DC, that focus on security and other things. We have labs in Dublin, Ireland, in Tel Aviv, in Bangalore, India, in Beijing, in Sophia Antipolis in France. And it's that global infrastructure that allows us to tap into the innovation, I think in the key hot spots where it's happening. The kinds of innovation that we've driven are, think back to the early days of the cloud, we were doing R&D in patents and research in the cloud before the term "cloud" existed. And once the cloud phenomena took off, we had assets and architectures that we turned into the Accenture cloud platform, which has made us a leader in the multi-billion dollar ... Built a multi-billion dollar business in the cloud market. So that's an example of research and idea in early patents going to scale business for Accenture. That's the research to results that we talk about and what makes a difference in our business. >> So, talk about AI. AI's a hot trend, it's a great buzzword. I love AI because it gets young people excited about software. IOT is a little bit more boring than AI. But AI is augmented intelligence, also a little bit of artificial intelligence. Look no further than a test load, look no further than some of these cool things. How's AI impacting your world? >> AI's massive. I would say AI is the biggest single innovation and the most disruptive innovation of the information age to date. And probably, the biggest impact on how we work and live since the industrial revolution a couple hundred years ago. That started a couple hundred years ago. So AI is a big impact, and we're just at the start of it. That's kind of a paradox, though, because AI has been around for 60 years. The term was coined 60 years ago in 1956 at Dartmouth. And it just did it kind of slowly, but now we're at the inflection point where we have the computing hardware and the data and the processing power to make it really happen. So for the next five to 10 and 20 years, it's all about applying intelligence to augment the way we as people work and live and really create new opportunities to improve the productivity and creativity of humans. That's why we're excited. >> It's a perfect innovation storm. You've got great compute capability, almost unlimited capacity, software, new developer, open source is booming, and now you have STEM. >> Well, before you get to STEM, let me just make one comment on that. I think the other exciting thing about AI is we've been working with dumb technology up until this point. Think about the way we interact with our thumbs on a mobile phone. Think about the way you use traditional software in an enterprise on your PC or your screen. We're slaves to dumb technology, and the power and potential of AI is to make technology smarter, more human-like, and really enhance our ability as humans to use it. And that's why it's an exciting era. >> That's a great perspective from someone who has been in the process business. The classic example is, does the process work for you? Do you work for the process? >> Dougherty: Yeah. That's what technology ... >> And technology, we don't work for technology. They should work for us. >> And that's what's changing. That's the inflection point. >> So now, 30 years now, a lot's changed, certainly in Silicon Valley lately. Women and the role of women in the industry is certainly important. We're going to be at Grace Hopper for the fourth year this year as part of our women in tech celebration, in California this year covering women in tech. STEM is huge, but also, the gender gap is still there. You guys have a pledge to be 50% by 2025, Accenture as an organization. Labs, in particular, getting STEM in the technical roles is also a challenge. What are you guys doing to address that, and what's your personal philosophy? What's your comment about STEM and women in tech? >> Well, look, the technology industry in general has a gender diversity problem, and we believe at Accenture, we can really set the standard for how to really get to gender equality in the workforce. And that's the commitment we've got with our 50/50 gender diversity pledge by 2025. We're well along the path to getting there, right about 36% or so. Now, with the actions we're taking, the formula we've got, I'm confident that we'll get to the 50/50 pledge that we set out there. And it's an imperative for the technology industry, not just for Accenture, because we won't innovate to the potential of the industry, and we won't create the right opportunity if we don't have the right gender balance in the workforce. That's what will lead to the right innovations. In this new era where the humanity of how we apply technology, as you were saying earlier, flipping the lens on a people-centric view, we need all the perspectives and an equal representation of the population going into the way we develop solutions. That's why it's a priority for us. And we think we can really set a standard for how to apply to the technology industry. >> It's certainly a topic near and dear to my heart and our company's heart. I want to ask one more question on that as a follow-up. Computer science was always kind of narrow, I'm not saying super narrow, but now it's broadened, with analytics, the tech science side is opening up, for all the reasons you were just talking about, the AI stuff. It's a broad landscape now for many diverse roles. Can you share your thoughts on where the entry points could be for women, where it's not a man-led culture or new opportunities or new areas, new opportunities to engage, learn? Certainly digital will help that, in terms of acquiring knowledge. But in terms of getting into the business, what is the surface area of opportunities? >> The surface, it's the whole surface area. I think the wrong approach is to think that there are certain roles that are better for women or better for any group to do. There's equal opportunity in all the roles. One stat that's striking to me is the fact that, when I graduated from college in 1986, 35% of the graduates were women. 35% in 1986. Today that number is about 18%. We've gone backwards in the percentage of women graduates from computer science programs. That's a problem that we need to address. We need to get more women into technology careers. It's about sponsorship, it's about mentorship, it's about having the right role models, and it's about painting the right picture of the opportunity in technology. One of the organizations I'm involved with is Girls Who Code, where I'm on the board of directors because of our Accenture involvement because I believe that we need that kind of early involvement with girls to get them on the right paths and make them aware of the right opportunities that we can get them into the pipeline earlier. >> Congratulations. Thanks for doing that; it's great stuff. Personal question. 30 years, you've been in Accenture for a long time, 30 years of labs now, celebrating. What's the coolest thing you've done? >> You know, the coolest thing, the coolest thing is building the fabric of innovation of the company, so what we've done with the labs, creating Accenture Ventures, which is our tool for investing in companies, formalizing our Accenture research capabilities, that we now have an innovation fabric that goes from research to our ventures into our labs and the rest of Accenture's business. So we can take innovations like quantum computing and scale it and ramp it right into our business like we're doing today. So that's what's exciting to me, is to have created a funnel that we can use to take the early-stage innovations and pump them into real impact on our business. >> Awesome, and quick, what's happening here tonight? We're here at the 30th, labs here in Silicon Valley, Computer History Museum, historic event, magic. What's the show about today? >> Yeah, it's all about the past, the present, and the future. The past is how we got here with tremendous leaders of Accenture Labs, who built the organization to where it is today. The present is what I was just talking about, all the opportunity we have. And the future is more exciting that it's ever been. The next 30 years ... My only regret is that I'm not 20 years old right now. So the next 30 years are going to be even more exciting than the 30 years that I've lived through. And we're in a great place. Computer History Museum isn't just about the past. It's about the future. I'm on the board of trustees here at the Computer History Museum, and I love the mission of the museum in the way it brings the stories of innovation to light and sets us on the course for the future as well. >> Well, since you have so much influence, we're going to have to get our genes edited for sequencing so we can actually live longer because that's coming around the corner, too. >> I think that's the right idea. >> Cheers. Congratulations. >> Paul: Cheers. >> We'll be back with more coverage here live in The Cube. Accenture Labs' 30-year anniversary. I'm John Furrier with Paul Daugherty, chief technology and information officer, great work, innovation officer, great work. Congratulations. More coverage after this short break. Thanks for watching.