Snowflake’s Post

Transcript

I'm now joined by Jay Lindberg, Chief Product Officer at Atacama. Jay, such a pleasure to have you on the program today. Hey, Ron, great to be here. Thanks for having me. Let me tell you a little bit about Atacama, that'd be great. So first of all, we are an AI powered data management company. Our mission is to unlock the value that our customers have in their data. We have an Atacama one platform, it's an integrated suite of data management capabilities constituting. Data quality, master, data management, data governance, that all combined together to allow our customers to deliver and ultimately a data quality program that delivers value to business, whether that is reducing cost, whether it's increasing revenue or reducing risk. We were pretty proud this year. We got nominated as leaders in the Gartner Magic Quadrant for later generations. So really puts us in a really good place in terms of leading in the data quality market. Jay, looking forward to diving in. Data management has become a hot topic. Among business leaders, as companies jump on the AI gold rush, can you walk us through at a common approach to helping customers tackle their data quality issues? Yeah, I mean dates of quality. Sorry. Data management is the bridge between the data and the users. It's kind of the awkward middle piece that you have to get right if you're going to truly get the value from your data. The Atacama One platform allows you to really understand, profile, classify, govern your data, and put in place a data quality program. That gets you over that hurdle of delivering value, delivering value to your end users. A good example is one of our customers, T-Mobile, obviously T-Mobile, huge organization, very data hungry, over 20,000 databases, 5000 applications, 8 petabytes of data. You can imagine it's a very complex landscape for them to try to understand how they can get value and you become data-driven. They were able to implement at a Camel one and using our. Platform, they were able to identify over $350 million in cost saving. So it's a pretty good return of investment and able to do it in a simplified way using our technology. Great to hear, Jay. The future is AI and it's all built on quality data. How does Snowflakes, AI, data cloud in Atacama combine to deliver better outcomes? Yeah, I mean, AI has caused this huge explosion in data modernization that need to modernize. That's why we're here at this conference. Everyone's here because they're modernizing their data. States and bringing it into Snowflake, but the average cost of a migration to put your data into Snowflake is about half $1,000,000. That's a lot of money, but that's easily covered in terms of the value that you're getting by migrating to a modern data platform. But that's only part of the story. If you're going to go through that modernization, you've got to think about revamping your overall data management practices. Make sure you really understand your data. Put in place a data quality program so that you can then not just have your data in a modern repository. To handle it in a modern way with modern practices, but actually provide the extra information and the equality around the data to support data initiatives upstream so that you can start to use them for AI initiatives. Our approach to how we work with Snowflake, First of all, we work tightly integrated with the Snowflake ecosystem. So for example, we will allow you to profile your data, identify your PII data, understand the quality of your data inside Snowflake. We operate that within your Snowflake instance. So there's no kind of security concerns around that because we were already working in your snowflake instance. We'll also allowed to be able to scale as your snowflake data volumes increase. The outer camera one data quality profiling will scale to support you after your Snowflake instances. So yeah, really tight integration, supporting that AI, but really making sure that you can take the advantage of the modernization to allow you to take the next step and get value from your data. Very much the power of the broader ecosystem. Yeah, sure. I'd like to pivot slightly and get a little personal with you if you don't. Remind you were just announced as Atacama's Chief Product Officer. Why was now the opportune time to make this shift and what excites you most about this next chapter? Jay, I've been in the data management space for about 20 years now. That's kind of scary when I say that. And through that time, I've seen, you know, huge amounts of disruptions that take place. You know, we had big data, we had cloud, we had regulations around GDPR and that kind of stuff. And every step of that inflection point, there's always been innovative. Companies that have created and changed the game completely. Snowflakes are a great example of that, for example. And so with generative AI now being the next big disruptor, I wanted to make sure that I was in the driving seat for that and an organization that was really innovating fast in that space. Data quality is front and center of the generative AI race. You can't do generative AI unless you have a good approach to data quality and truly understand your data and how that's fed into your AI initiatives. And so at a camera was a great home for that like front and center of that AI life cycle, leaders in data quality and do already doing great things ourselves with generative AI baked into our products. Thank you for that personal reflection, Jay, from a macro standpoint, is there any advice you'd like to give to the audience as they embark on an ever increasing AI dependent future? Generative AI is everywhere. Going back to that inflection point, like everyone here is talking about generative AI and the importance of it, but we're really only very, very early on. And what that world is going to look like over the next probably 3-5, maybe even 10 years. Think about cloud. Cloud was around 10 years ago as a disruptor. And how much data has actually been moved fully from on premise systems into the cloud from an enterprise scale, it's only about 25% of enterprise data actually has been moved to cloud. That's 75% still to go 10 years down the road. So lots of opportunity to kind of get started with generative AI. People should be experimenting, identifying the use cases, but really important is the data. Strategy that backs up the AI strategy you can't forget about the data strategy you've got to have your data in order you've got to understand your data and you've got to make it accessible and of high quality an analogy I tend to use for this is around motor sport pace cars race cars the pace car is the data strategy right it's really important for a good safe fundraise it's laying the foundation is making sure the conditions are great for the race to go ahead yeah OK it moves kind of bit slower and. It's kind of boring to watch, right? But the pace car is really important for the race, ready for when the race cars start, which is the AI. When the race cars start, they need to be able to go full steam ahead as fast as they can in a safe and controlled manner. So pace cars, race cars, it's kind of how I define it. You can't forget about your data strategy. I love it. As an F1 fan, I really appreciate that analogy. And Jay, I realize we've covered a lot. Anything else you'd like to leave the audience with better watching here today? Yeah. I mean, I think it comes down to that data strategy. AI hyped, all the AI initiatives that taking place. Really, really important, but focus on your core building blocks, focus on your data strategy. Make sure that you're running robust data quality programs that look at how you can improve the data as it feeds into the AI initiatives without a camera and outer camera One platform. We're tightly integrated with Snowflake. We allow you to understand the data quality of your data as you're modernizing it into Snowflake. We allow you to operate close to your Snowflake data inside your Snowflake estate so that you haven't got to worry about security and privacy. And we can scale with that as well, and we'll allow you to keep on top of the risks and governance associated with it at the same time. Jay, such a pleasure and congratulations. Thank you again. Welcome to add a comment. Such a pleasure to have you on Data Cloud now today. Thanks. Thanks for having me. And for the audience watching, I'm Ryan Green with Data Cloud now. We'll see you soon.