 Hello, welcome to this special Cube Conversation in Palo Alto, California. I'm John Furrier, host of theCUBE. We have a special conversation news update with Elation, Sadian Sangani, CEO, co-founder of Elation is here, ninth appearance on theCUBE, 2017 is when it all began. Congratulations on the big news. I'll let you say the news. Let's get into it. Big week this week, it's the data week I call Databricks, Snowflake, both having an event. Great stuff. But yeah, the data week is probably what we ought to be calling it. It should be a new national global holiday. But of course, great to be here, John. It's great to see you as always. I can't believe it's been nine times, but over so many years, it's not surprising. The news for us is that this week, during data week, we've been named by Snowflake to be the data governance partner of their year. This is the third time that we've received the award in a row. We've also this week been named Databricks' data governance partner of the year, which means for likely the two biggest companies in data, we are their number one data governance partner. And I think it doesn't take a lot to believe or realize that that means we're probably the best data governance partner to both customers and the ecosystem in the space. And that's super exciting for us to get that recognition and it's recognition that we intend to build on. It's a huge honor. Those companies are very well established. Snowflake also went public. Databricks is still private, still but a huge valuation. Big news there with their data lake house, having an LLM, a generative AI functionality just recently acquired Mosaic, ML. The traction towards data products, data applications is here. You guys have been there from the beginning and I remember your early conversations here in theCUBE. I'd like you to share that and introduce a list of the folks that might not know the history or what you guys do. Why the success? Take a minute to introduce the company. Yeah, so we found a relation in 2012 and when we found the company, we basically said, look there's a lot of data out there that people don't understand. So what we felt like we needed to do was to move past the old traditional paradigms of metadata management and metadata as a term and really start talking about search and discovery in the context of data. And so the thing that we built was a data catalog and that data catalog was not unlike catalogs that you have in your real life like Yelp and LinkedIn and Amazon. We took those concepts and then we extended them to basically deliver an experience that allows people to find, understand and now govern their data at scale. And since then we've been really true to our fundamental North Star as you pointed out. We really care about one thing which is enabling people to be curious and rational and to be able to answer their questions through the lens of data. Now, that's a really complicated problem but I think in this world where artificial intelligence is taking over and where data is really the competitive differentiation for most organizations, it is a North Star that has really served us really well but much more critically served our customers and the world really well. Well, congratulations on the award winning the data governance piece of it as partner of the year for Snowflake for three years is huge. And now this year with Databricks the first time there and this probably will continue. I want to ask you to get your thoughts on this because governance has come up a lot in this generative AI conversation because you're seeing the developer get involved, data becoming much more part of the company's applications where governance is still needed but they need agility and also predictability in the data. So you're kind of been doing that work on the hard side for years building that governance layer. Now you got Snowflake and Databricks and others looking at kind of this kind of Cambrian explosion of applications. It's not only just democratization it's also the value creation coming out of the data. Could you share your thoughts and vision on where the governance goes next here as you're going to have a presence at both Snowflake conference as well as the Databricks data plus AI event. The history here is really funny, John because when we found the company people thought of us initially as a search and discovery engine and that search and discovery capability was sort of our first love and really consistent with what we were trying to do which was to get people empowered with data. The next problem that came up though was that people really needed to build trust and data and as you know, traditionally when people talk about data governance it has been this massive top down initiative where people have required large teams of data stewards lots of change in business policy lots of change in business behavior as expected and there's a whole bunch of hardwired workflows that are built in order to enable this governance which really attained very limited value. And so when we started building a governance solution really well back in 2017 we really took a different approach to the market and we said, look most of these initiatives are not very successful what we needed the governance that is federated in format really consistent with these ideas of data fabric and much more critically data mesh. And so what do we mean by this federated governance? What we mean is that not all of the organization should be required to adopt all of the policies all at the same time that to get success in governance you have to look at it as an ongoing discipline and one where different teams, different data organizations and different data assets can be able to apply different levels of governance as and when and where needed. And we found that that approach requires a totally different technology architecture and a totally different software paradigm and that's what we're seeing take place. Now, to get to the second point of generative AI obviously taking that federated approach which gets to much more success gets you to this world where federated or generative AI and AI is much more possible because now you have much better data off of what you can run all of these different AI and algorithms. And step zero is get that governance federation up and running. Step one is get the AI apps going. I got to ask you because this is a key point in what we're seeing. I've been asking many entrepreneurs and also VCs and also big cloud players the question which comes first the application development of generative AI or the infrastructure data infrastructure to run on it. And they said both but more heavily on the development side right now the running it cannot be ignored has to be discussed. That's some of the interplay between the foundational infrastructure you need to set up the app. So I have to ask you with respect to generative AI what role does elation play in helping organizations successfully adopt and train some of the generative data models. If you have the foundation, what role do you play? This is the hottest conversation this week at both events. So any software, any model starts somewhere. You've got to start with a set of assumptions and a set of information. You've got to then build the model which tells the computer how to treat that information. And then you get some output. That's all software that's ever been built or made. So that's not very different. And I completely agree that you start with the application you have to start with the end business purpose and work backwards. If you have a customer forecasting model that forecast, which are your best customers, you want to start with a limited set of customer data. You want to build a model. You want to get success. You want to improve it over time. And improving it might mean that you have to improve the underlying data infrastructure. So no arguments there. I think all of us see the world in the same way. But if you want to use data well, if you want to build AI at scale or even at limited scale, you need great data. AI is a garbage in, garbage out process. And so what we like to say at elation is that trusted AI needs and requires trusted data. And if you think about that then, how do you get trusted data at scale? Well, you need data governance. You need people who can know that the data that they're leveraging is compliant, is of high quality, is secure, is going to comply with the various regulations that are out there. And if you have that data, you're going to be able to run much faster and you're going to be able to get much better results. And so it stands to reason that every single company that's trying to do AI is really also trying to clean up their data infrastructure at the exact same time. As Bill Walsh said, the famous football coach for the San Francisco 49ers, when asked why he's so successful, he says, I just take care of the inputs. The scoreboard takes care of itself. If you do that right on the input, the score and everything else in the outcome happens. And this is kind of what you're getting at here. And this is a key focus for figuring out how to architect data. It's almost like a, it feels like cloud solution architect meets the data world. And, you know, governance is a key center point, value proposition to make all that happen. What's your thoughts on that? Do you see the same thing? Or what is the relation doing in this area? How are you talking to customers? There seems to be a lot of movement going on to, as you said, scale, trusted scale they want, but scaling up their data seems harder to me right now for companies to get their arms around. What's your thoughts? Yeah, it's an interesting question. Perfect is often the enemy of the good in this space because what ends up happening is people say, well, look, I need data governance. And when people think about data governance, they think about a lot of different things. They think about securing their data or ensuring their data is of high quality or making sure their data is appropriately mastered or making sure they have the right retention policies around the data or making sure that it's adequately described and stewarded. And all of those are great and laudable goals. And where people fall down often is they look at that and they say, wow, that's just this massive exercise. And if I'm going to do this, gosh, it's going to be super costly. And I don't necessarily know how to approach this problem and where do I go and start? And so when they start with these initiatives, often what'll happen is they've got really big aspirations and then they find that because the organization doesn't have the time or the patients are fundamentally the resources to accomplish all of these things, they'll not just scale it down to what they can accomplish right now, but it's something much more minimal and they'll feel like the initiative has failed. And that's really the challenge is, how do you then take on the right level of scope so you can use that as an accelerant to get to the next milestone and to build even greater heights or two even greater heights than you would otherwise initially imagined. And so what we find when we're talking to our customers often is that they're asking us questions like, what do I do? Where do I start? How do I get to success? How do I measure that success? And how do I build to get to the next milestone? And that is really why our federated approach helps so much and makes so much sense for many of our customers because they don't try to boil the ocean as many have tried to do in the past. The other thing too I want to highlight and get your thoughts on is being the data governance partner of the year is interesting, right? I mean, data governance to be a partner of the year, that means you have to be enabling some value in an ecosystem, say Snowflake, third year in a row, Databricks first year, both companies are building ecosystems. So being a partner in those ecosystems, data governance is not just a checkbox, it's an enabler, which you guys have done. Could you share your thoughts on how that plays out in an ecosystem? What's it like to be a partner of the year? What makes that happen? What are some of the dynamics? Every one of these companies that are at some level of scale, that's true, this is true of Databricks, this is true of Snowflake, and this is certainly true of Elation, where we ourselves are trying to and continuing to build a platform and an ecosystem are really trying to differentiate on multiple levelers with these relationships. The first and most obvious one is the product. If the products don't work together in a seamless fashion, you can make all of the claims that you want to on the sales and marketing side. I can get up on theCUBE as many times as our marketing team would permit me, it wouldn't make a difference to customer outcomes. Ultimately, the products have to work together. So in the Snowflake case, we've built an initial integration starting with their partner connect capability. And from there, we built a massive integration with their governance capability on top of the baseline integration that we had before. From there today, what we're announcing is that we're also giving customers the ability to have Snowflake partner capabilities for our data quality integrations and initiatives, also Snowflake capabilities for our connected sheet integration. And so what you see is us continuing to, on an evergreen basis, innovate and deliver more and more integration on top of that Snowflake architecture. On the Databricks side, you'd see the exact same thing. We recently announced that we would integrate with and have integrated with their Unity catalog. And on top of that are now today announcing that we are integrating on top of their partner connect capability, so that literally customers can click on a single button and get a deployment of a relation working with Databricks at a single click. And so that's the kind of innovation that customers expect. So it starts with product, but beyond that, you've also got to be a great partner in the field, which means one first and foremost, you've got to have great successful scale of customer implementations. I'm proud to tell you that we've got over a hundred integrations live in the field with both Snowflake and Databricks, individually as companies. And then on top of that, your sales people have to know and understand what the value proposition is and to be able to communicate that to customers. And so we really integrate at all three levels and are constantly working to make it better and better and better, just like you would with any partnership, right? With any marriage or with any friendship, you're constantly trying to figure out how to help your friend and how to take their help. And we're trying to do the exact same thing. And they're needing you to be accountable for your product, working well with them and vice versa. It's give and take back and forth, good stuff. I was going to ask you to put a plug in for some of the news. You're going to be at Snowflake Summit and the Databricks data plus AI event, ones in San Francisco, ones in Vegas, the cubes here at both. What's the story? What will be sharing at the booth? You got the news out there. What other things are you talking about at the event at Snowflake Summit and Databricks? Take a minute to explain what's going on at the events here. First and foremost, we're going to be talking about Elation as a data intelligence platform and how we can help organizations scale getting more intelligence from their data. And there are multiple use cases. The most obvious and traditional ones are self-service analytics. Obviously we're going to be talking about data governance. More recently, we've been talking a ton about AI enabling AI for the enterprise. And so those core use cases are going to be really the front and center of what we're demonstrating and what we're talking about to customers. In both summits, we've got great customers who are going to get up on talk about their experiences with Elation in sessions that are now, to my knowledge, completely booked and sold out. So if you can get into one of those sessions, I'd encourage you to do so. If you can't get into one of those sessions, then I'd encourage you to come to one of our demonstrations at the booth where you can hear all about the products at Snowflake that's booth 2320 on the show floor. And frankly, even if you don't remember that number 2320, you should be able to find us because the presence is going to be pretty large. And then at Databricks, you're going to be able to find us at booth 529. Again, that's booth 529. So come and look for us, come and find us, come and watch our customers in action. And learn a ton about what we are doing, both on the product innovation front, but much, much, much more critically on the customer innovation front because really the proof is in their outcomes, not so much in any of the capabilities that we're releasing yesterday, today or in the future. So that's been great to have you on. One minute left, I want to get you to explain again, everyone's talking about LLMs and foundational generative AI models. It's here, it's making data more important for this next gen set of applications. Data products are going to be feeding into applications. So you got the foundational layers that you're building. What does it mean for Elation to help me as a customer? What's the pitch? How can you help me get to that generate AI future? The really interesting thing about all of these generative models is for them to work on scale or at scale for any given enterprise, they have to combine structured data with the semi-structured data that these models are trying to offer. And these models really only understand text or data-like text. And so then what you have to do is actually get the model to really understand that semi-structured data or structured data is actually something that it can consume and replicate at scale. And if you're going to do that, you better make sure that data is right because we've seen all of the implications for these data models gone wrong. I think we all saw that New York Times article where there was that model for Microsoft that was trying to convince a reporter to leave their girlfriends. And that was all based upon hallucinations which can come from really bad data. And so what we're trying to do and it is really inform enterprises that to get these algorithms right, to get these outcomes right, to get these suggestions right, you've got to have data sets that are well-labeled, well-described and of high quality. And if you can have those things, you can get some really impressive and amazing outcomes. But without those things, you're not going to be able to get to any level of scale success. Good inputs, good prompts, good tuning, clean data, has better outcomes than data that isn't elation. Congratulations, South Indian. Really, really proud to see you guys so successful. You've been there early. You did all the work, hard work. And again, three Pete award for data governance partner of the year at Snowflake and first year with Databricks. Congratulations on being named data governance partner of the year, multiple years in a row with Snowflake and this year at Databricks. Congratulations. Thank you, John. It's always fun to be on theCUBE and it's always great to see you and look forward to doing more work and seeing you soon again. Keep riding the wave. We've got more AI coming. It's only going to make data more and more important. This is the CUBE's conversation news update with elation. Big time news here, data week. You have Snowflake summit and then Databricks data was going head to head and a variety of satellite other events like trend of AI, data, foundational services, all happening more and more here at Data Week. Thanks for watching.