(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)