So the issue of creating value with data or agentic and so forth starts with this. How can you actually create data products that are correct?
Welcome to It's About Data episode 77. This is a conversation with Florian Douetteau, CEO of Dataiku. Well, hello everybody. We are actually at a really special occasion today. We're in San Francisco for Snowflake Summit 2026. But technically we're actually offsite, about a block and a half away at Dataiku HQ. Dataiku of course was named once again Snowflake Partner of the Year this year. Congratulations. And we're joined by our special guest, Dataiku CEO Florian Douetteau. And I hope I pronounced it correctly all this time.
You did. And thanks for having me, and welcome to the nest.
So Matt, you said that you have a stumper to start out with because you're the data scientist.
I wouldn't call it a stumper. Actually, it's from the press release for the Product Partner of the Year 2026 Snowflake. It talks about how Dataiku is very good at helping companies move from AI pilots to governed production scale deployments. And frankly, governed and production scale are two things that we talk a lot about on this show, but they're hard, right? So give us, you know, what's Dataiku's perspective on this? How do you make AI go into production scale and governed? What are the steps to make that happen?
I think in the enterprise market as we are today, we have to acknowledge that creating stuff is no longer a limit. That's my mindset at least. You can in theory code everything into existence over the weekends, you can probably code anything you want and have lots of fun. But imagine you're back on Monday in your company. How does it work in practice? And that's where governance kicks in. Meaning any data you produce needs to be validated. You need to be able to explain it and so forth. And so the issue of creating value with data or agentic and so forth starts with this: how can you actually create data products that are correct? Starting point. And correct meaning that they are tested, you've got multiple people in the organization that actually understand it, because if it's only one person it doesn't work. And how do you put some form of governance and testing around it, repeatability and so forth? What we are building with Dataiku on top of Snowflake, and notably one product we launched called Co-Built for Snowflake, provides this ability to create and vibe code things into existence, but vibe code data pipelines or agentic pipelines that are visual, tested, and governed, meaning they are within a framework where it's easy for people in the business to understand what's happening. Because the issue we are already having, all of us, with agentic technology is not about imagining things and turning something we imagine into something that looks real. It's about testing whether the thing is actually the thing we expect it to be. So that's what I focus on, and I think this need of control and governance is now what's blocking people from creating value with data.
Right. Let's step back here and go to some building blocks we have to work with. And you know, we're a little biased here. Our podcast is It's About Data, so I'm going to start with data. You take a look at the landscape in most organizations today, data is scattered, inconsistently governed if it's governed at all. But now obviously with AI, it really raises the stakes. So the question I want to ask you, Florian, is at this point, what does it take to make the data AI-ready?
I won't go into let's all build semantic models and ontologies and data governance, because you've heard that before and it's not new. So I won't repeat that, even if I believe it. But my particular take from a couple of weeks is that even if we do all of that, in practice the data that matters, the data products that are hidden, those shadow data products, are for instance in Excel. Do you remember Excel? Those Excels with 30 tabs and maybe even some VBA in the middle? Those shadow data products are the ones used by people in the business. And the next step of data readiness for agentic is about migrating those shadow data products into governed areas. Move them into Snowflake or whatever. And it doesn't mean just move the Excel spreadsheet into Snowflake as a table, that would be too easy. What you really need to do is translate the business logic that is within an Excel into pipelines so that you can repeat them and turn them into a process. And what we've seen more and more among your customers using our platform is indeed using our platform to do this type of automated translation of Excel, for instance. Because that's the issue of data readiness: you still have too many things, especially the things that are available, stuck on your laptop, scattered. It's not just scattered across various data marts and data warehouses. To some extent that part is manageable. The next layer is everything that is at the bottom, on the laptops, and move that up. And not move that up as just copy the thing because it has no value. It's move the actual business logic. And that's I think the next step for data readiness, which is probably not understood enough in terms of how important it is in order to actually create value with agentic down the line.
That's huge. So let me... This really resonates, this Excel conversation. This is exactly what I've seen over and over again doing data engineering consulting projects. In fact, quite often if you talk to the company's CFO, they're building out the quarterly report for the SEC and Wall Street in an Excel spreadsheet with 30 tabs, just like you say, with VBA. How can agents contribute to this process, but in a way that's trustworthy, production-worthy, and governed properly?
Now you can actually in our platform translate this type of Excel spreadsheet into an actual pipeline, meaning something deterministic. And this translation can be largely automated. We've automated this translation. It's no longer a big data engineering project to rebuild the business logic of the CFO into a few weeks consulting gig. That's something you can do in a few minutes to a few hours depending on the complexity, including all of the application on top so that the CFO can actually look at the data and create a summary of these hypotheses with a summary of the reports automatically. So those things now take minutes. And that's what's different. You can actually translate any type of Excel into a pipeline with its business logic. And indeed before, we all heard about those projects where you had this complex financial modeling that needed to be translated and data engineering, let's build the data model, da-da-da, and a few weeks later it's not really happening, it's complex and doesn't reconcile, and so forth. And then you just dump the data engineering projects to the bin, which is why everything stayed in Excel. Now we've seen it and people are doing it with our product, it's no longer the case. And so you could actually go beyond that now. And it's important because having this reconciliation as a data pipeline is the first step for the CFO to then be able to add some agentic process on top that will inspect automatically some things and so forth. Because if you don't do that first, you can't really apply the agentic layer automatically and reliably.
Right. And it's a very interesting point that agents can really help us do this work of taming data, of governing data. But let's look at it from the other side. From your standpoint and your work with your customers, the whole industry is talking about agents right now. Agents are the future. Where are customers at with regard to... obviously within your solution you have agents that can perform a lot of these functions. But for customers who are trying to work with agents, where are they at right now? Where's their comfort level? Where's their understanding?
I think it's still baby steps and early. Meaning you've got customers having from a couple of agents to some having thousands of agents. But if you go one step further in analyzing what's happening, when it's a few thousand, typically a lot of those are more glorified assistants, as in glorified RAG, as opposed to true agents. And I think we are at the beginning of it, but we start seeing customers getting more and more sophisticated in terms of their agentic use cases. What I find interesting as a subset of agents, and that we focus on in particular at Dataiku as I think it's interesting and worth building, is the category of agents that are semi-deterministic. Kind of unclassified today in all those agentic categories. But in particular it's things such as procurement optimization or supply chain or building your marketing budget. This type of thing where you need some form of determinism. It's not like an open clue doing project management that will run over the weekend hoping that you will get smarter and smarter every weekend so that you can end up planning your vacation. We're not talking about this type of agent. We're talking about an actual thing that can optimize your procurement, for which lots of practical rules need to be in place. And so it actually looks more like something that is agentic at some edges and some steps, and we can adjust the data and be smart on some steps, but also have lots of deterministic steps because you want to be sure about what it's doing, and maybe even at the middle it's a pretty good old decision tree that is actually taking the real decision you want to take. So this category of agent is super interesting because I think it's very important in particular for back office tasks, which is where a lot of the leverage is for agentic today.
