 Everyone, welcome to this special CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We've got a special CUBE conversation with Jonathan Bruce, VP of Product Management, Alation. Friend of theCUBE been there from the beginning of the founding of the company and the success has been phenomenal. Jonathan, thanks for coming on this CUBE conversation. Talk about some big news. Yeah, so good to be here. Let's get into it. You know, I remember, you know, when data was kind of coming out of the big data trough and then now your machine learning kicked in, now we're seeing that AI wave hitting. You guys were on the front lines of this democratization trend. Now you guys are announcing Alation's analytics cloud, basically to bring business value around that data. It's a big part of the wave that customers want. And this is now the hottest trend because AI has created this generational inflection point, certainly great going forward. But now you have data, previous data, historical data and infrastructure and software practices that's full cloud native and distributed computing based. This is going to change the game on how people manage their data, get the value. Take a step back and give us context to why this cloud exists. What are some of the pain points that you guys are solving with your customers? Why now? That's a great question. So I think for me, thinking about the kind of challenges we see our customers tackling, it's about how they can conquer their enterprise data, which often we find is highly siloed across their enterprise, across the globe. It's a function of their business growth, the acquisitions that they've done. In relation, we pride ourselves in being able to build a more holistic view that ultimately to get our customers to a point whereby they're able to operate on their data with a greater level of data intelligence. And data intelligence is hugely actionable here in that it allows them to curate, to govern and then fully understand their data such that they can better serve their customers and better serve their customers means that they are able to more adaptably, be agile in the business environment that they find themselves in. So, honestly, it's what attracted me when I came to join Alation just over a year ago. Super excited to talk to customers every day about this because at Alation, we deal with customers that operate over so many different verticals. Not one customer is the same, but what is the same is the kind of challenge they see with their data and how they can operationalize around it. It's so interesting to go look at the history of how enterprises dealt with data. Dave Vellante and I talk about this on theCUBE all the time. He uses the word stovepipes to describe silos. Yet verticals get all the benefit with data because of AI, but also it's a horizontal opportunity as well. It's not just vertical and stovepipes. There's a lot of horizontal access, there's horizontal impact. I mean, some of the thing has been a team sport. I mean, you guys have been on this. How do you get the best of the horizontal scale and the vertical specialization? It comes down to one word for me. It's democratizing that data access and data discovery. So if you can get to a point whereby you have sufficiently governed your data. Now, nobody wants more governance. You want just enough governance whereby you have inherent trust in your enterprise data. That's such a great inflection point to get to. You talk about horizontal, that's whereby you can take that stovepipe as you describe whereby you've developed a best practice in terms of how you deal with your data. Then ultimately you can rebroadcast it across your organization. That's where we see some real power. And in relation, we have had some customers who've seen huge success in this regard. But I think generally speaking, the journey that we see our customers going on, which really drives us forward here, is the ability then to really fully understand not only do I have access to the data, not have I empowered my organization to actually take action with the data, is to actually understand the value, the key critical value to my business in actually doing so in the final place. And here in relation, we think about that every single day. Why? Because our customers are telling us, but also what the catalog has done for them is kind of get it off them closer to be able to connect the soft dollars, if you will, to the much more hard dollars of what it is to be able to take action upon their data. And that's the journey where you're on. Before we get into the product and the announcements, but I want to get into the hard news, why is, what's the blocker for the customer? I mean, we just talked about, it sounds easy. Oh yeah, horizontal scale. We'll take the stove pipes down, make it all work. What is the blocker? Why is it so hard? What's their challenge? It sounds... There's not one challenge, I don't think. It is a mix. It's a mix of both technical challenges. It's a mix of, shall we say, how customers are able to deal with a set of, generational technology differentiators. It's a political challenge often. It's a challenge in terms of how you can evolve organization to actually fully embrace this challenge around us. I would say that there are a couple of patterns that I have seen in various customers and various enterprise I've dealt with. So often you run across an organization that takes a top-down approach. Thou shalt now do this. It almost comes off like an edict. I mean, edict is not something people typically rally around. I think where I see the real differentiators are those companies who embrace more of our, well, as a movement, as a company, we're gonna embrace the value of knowing our data, building intelligence around the data, empowering our people to take action because we trust you. Treat them like adults with their data in a way that they can move forward, in a way that they can discover more value in the data that you thought was actually there. And there are so many cases and so many stories about this in adulation. We deal with healthcare, insurance, banking, you name it. There's some really empowering these cases that we've uncovered there. That really kind of are hugely motivating both to me to my team, but I think we're only just getting started in this regard, but I think exciting strides we've made thus far. I like the term edict because the whole top-down, take that hill. We need to have standards. Let's put AI in everything. Like that's that down. And I always say from the board room to the dorm room, you have AI everywhere, but the actions at the bottom's up, it's organic growing. AI is surge and open source. You guys have been on this democratization way, which is kind of a bottoms up philosophy. So now it's like meeting in the middle, the timing of scale. I like the meeting in the middle. I mean, I think we work with organizations whereby it's at a product level and that's the world I live in. Have we got the tools where people can deliver the ability to have success at small scale and then we broadcast that scale across their entire enterprise, having the right tooling so they can understand where they are on that journey from a curation, from a stewardship, from a governance perspective. That's super important because ultimately that's the ammunition. That's the material that allows them to really stretch for the North Star, which is to kind of have that data intelligence that they see others having that could be in across different organizations within certain groups in large, highly complex enterprises and allows kind of the C suite to say, okay, we have some really good operational maturity here. Now let's emulate that. We have the right material analytical data to support what we are able to do here. Now let's kind of bring that across more departments to go from there. So I think that's exciting. You know, Jonathan, one of the things that we're seeing in this AI wave is there's two schools of customers, right? Ones that have been doing work with data and having data initiatives, ones that have been not really leaning in, maybe have some initiative but not really heavily. Reminds me of a COVID. When COVID hit, the people who were in the cloud took advantage of that dark time and had scale whenever one worked at home. So you saw the winners had cloud experience. Ones were tooling up quickly. Here with AI, the folks that have strong data initiatives are well positioned to take advantage of that AI. So we're seeing people become very successful in AI, just even in the early days here because they've done labeling. They have data initiatives. So this brings up the news that you guys have. Are announcing the Elation Analytics Cloud, a unified reporting platform. Your customers are kind of set up nicely for having all that work done with the data initiatives. Is this cloud going to bring value to that? Can you explain what this news is? Why, what is this analytics cloud? And what's the benefit to customers that are have leaned into data initiatives? I think the benefits are grounded on what the catalog was born to do. The catalog was born to bring together these data silos so you can build trust in your data. Now, I love how you just described it. There's a nascent advantage that those who have a more mature data strategy that have been leaning into this for some time are ideally positioned now to take this holistic view that we can offer whereby they understand the maturity of the data governance that they have, the value of the data that they have, to then apply that in a safe and trusted manner to be able to meet the anticipated and highly excited goals around what generative AI can do for many of their customers. Now, if I talk to some of our larger and more medium-sized customers, the excitement is equally shared in that. It's going to deliver a set of automation, a set of velocity that they did not have, but none of them want to go down this path unless they've ultimately have the right level of instrumentation and insights around the value and the maturity of that data unless they would do so. Because if they were to do so at that level, without that insight, without that reporting capability, they're taking on too much risk. And that is unacceptable to many enterprise, but rightly so. I think about some of the success we've all seen with the likes of ChatGTP. It's fun, it's great, but let's remind ourselves, it is trained on the corpus of data of the internet. So it is likely to say things and do things that are wildly unacceptable in the enterprise context. So take a step back. If you're able to get to a point whereby you have the right level of governance, controls, insights and observability around your data, you understand the value of the data, you can be more assured that the inputs you're putting into these generative AI models are actually going to result in a business outcome that is actionable, that is trustworthy, that will actually have material difference to the bottom line. And that I think is the real tsunami that we are seeing across many organizations that they're coming to that realization. So the scramble is really happening in terms of who are the right customers, who are the right partners to work with to help you kind of down this path to get a point whereby you're able to understand that value index of your data. And I think that's really important. So take a step back. I think there's two schools here, those who realize that they may have missed the ball here, I was like, hang on a second, I have this data, but I don't have the right level of governance. I don't have the right level of insights. I really am not clear on the value that this data can do for me or actually can be formed to be able to better serve my customers. So there's an urgency there. So companies like Elation are ideally placed to help kind of in a more democratized fashion to bring an organization up to speed such that they can then more seriously look at these models. Now, if they're not to do so, they're gonna, don't do so, they're gonna take on a huge amount of risk. And I think there are well-documented instances over the past 10, 15 years where AI has not necessarily had a positive result. I'm not gonna cite them directly, but I think they're well-documented. And I think these, this is the responsibility at the C-suite, but I don't think the responsibility is exclusively at the C-suite. What you want to do is get to a point whereby you're able to really empower your entire organization to put this tooling to good use. I'll draw a corollary, right? So in many companies have run around talking about how they go for a more declarative, low-code type models. What did that do? That democratized, it empowered an organization, empowered huge communities to be able to do amazing things with these technologies. That's ultimately the journey I think we can facilitate. Well said, I like how you put that. The value is going to be there. You guys bring a lot of value to the table. It's still early on. I think you guys are a great partner for customers, certainly with the data intelligence. I mean, data is the new intellectual property. That's everyone's kind of seeing that. You don't want to just put it in the public domain, but you want to leverage language models. So I think a lot of people are looking at data now, not just cataloging it, but the governance has to be built in to the innovation of the AI so that it's protected, it's secure, private, and actually usable, right? So this brings up the question on the news. What is the Elation Analytics Cloud specifically? What is it comprised of? What's the, what are some of the features? You guys have a couple of things that are interesting around consumption. Yeah, three critical elements for us. I think it's going to allow a new and much more kind of contained ability to be able to measure their data culture and maturity. So number one, organizations can now measure their maturity through a set of crucial components. Data leadership, data search, discovery, literacy, and governance. Those are real critical elements, all of which ultimately are kind of supporting a set of scorecards that we're able to afford here. Now, there's no point in having these crucial four components unless you can actually have a scorecard for this. So how now can you measure your progress? Because it's a journey. You're not going to fix this in one day or two days. It's an iterative set of journey, just like we talked about earlier. You start small. You focus on a particular area in your business that's most important. You develop a set of metrics around this. We're going to give you the ability to do just that in a way that has a standard set of metrics, but also gives you the ability to have a custom set of metrics. Why custom? Because custom is very relevant to the particular business that we're working with a lot. So you can kind of bring to things like your total number of assets curated. So for a catalog, that means how many databases, BI data sources. And as we've just talked about, your generative AI components, so your machine learning, which means your machine learning models, your notebooks and that kind of stuff. Modulate that around your total active users and all these other kind of critical elements. These critical elements are key indicators as to what your scoring is for the maturity of your data program that you're trying to pull off. Now, of course, how do you actually understand that? This is a set of insights that in that will be consumable for both business leaders and daily leaders. Two very different personas here, each of which want to know different aspects of that data such that they can understand what is the value that they are extracting at their data products. But now they can do this as a function of the actual data usage that they get. So we're going to have a set of standard reports that deliver these insights, a set of customizable queries. So you can understand who's doing what with what. Really important also as customers, try to understand the actual fiscal impact because you want to get a point whereby you're fully understanding the infrastructural impact of this. Many of us today are operating in a world of highly utility based infrastructure where there is at the infrastructure level at the data source level. This is a really key part of that. Now you can say, all right, if I'm spending X amount on this database and I'm on a consumptive program and I'm spending Y amount on this particular cloud infrastructure, I can marry that against the ultimate business value that I'm getting from it as well. And then be able to map that out. Really also gives you a sense of how your organization is breathing around their data. I'll go back to some of the early lessons I learned at Elation. One of the things that I think is so fascinating is that we can help our organizations kind of bring to life the institutional knowledge they have around their data because we can bring that to life within our platform. Bringing that to life in our platform also gives a set of velocity at an individual level whereby they can understand what they should and should not do with their data. And people come and change in organizations all the time to have that continuity institutionalized and brought to life within the Elation instance. Now I'm married that together with the Elation cloud analytics is really critical. That's a great way to look at it and you brought up the couple of things that I want to just ask you on. You brought up kind of operations, right? And also people, like people staff turns over, people leave, people change jobs. Is the tooling easier? The fact that we see out there that I like to get your reaction to is that there's a massive amount of data coming still into the system. Data deluge is here, tsunami, whatever you want to call it. More data is coming in than ever before and people are now running more advanced data programs. But the budgets aren't increasing as fast as the data is. So customers are trying to get more value out of the ops as well as the business value, right? So they're kind of tying together. What specifically about Elation can you share from a product standpoint and value with the cloud and the reporting index? Has that make the customer's life easier when they have a skills gap or maybe they want to make it easier to use or the budgets aren't increasing. They got to do more, more data is coming in. That's the psychology that your customer, they want to leverage the data. They got to make it better than ever before. I think one of the things we can help break down for our customers is the fact that with each of the amounts you say recognize that more data is coming at them every day. How do they possibly handle this? So it's about building a set of automations, building a set of workflows that are both trusted, tried and true that we can learn from across our entire customer base. Now, of course, Elation is a cloud customer. We support our customers across a set of tenants using a set of robust cloud infrastructure as well. At a meta level, we can learn about how many of our customers are behaving and the kind of successes that we have. And of course, those learnings can be applied and then also be shared at a meta level across all of our customers. That's honestly the advantage and one of the most exciting things about building software in the cloud. You can take those learnings, you can repackage them and deliver those as more standard offerings increasingly around our customers. So all customers can benefit from some of the experiences across our full customer base. They come to life, as I said before, around delivering additional workflows, operational, but also kind of automation is going to drive a huge part of this as well. And I don't mean automation in a way that is going to be detrimental to people's jobs. I think it's going to be the offering, the insights as to where you can automate in a way that allows you to allow people to really focus on the more difficult tasks and more difficult tasks of really understanding, teasing out what is going on with their data in a way that maybe having a detrimental effect in how they're serving their customers. I think it allows for that capacity. So I think that's the exciting piece. So no question. I don't think you and I are sitting here if we have children wondering where they will be in 20 years, I think that the level of data coming at them will only increase. But I think it's also about the ability to look back upon your data using a set of compute capabilities that will ultimately be very compelling. But compiling in a way that will allow you to learn from the history, to kind of look for these insights, I think is going to be really important as well. But ultimately, look, there's a path that we're all on here. It's not getting to me either. I mean, like you said, it's not, people shouldn't be afraid of it because if you think about, I mean, I said this on my podcast all the time, I believe a creative class is going to emerge in tech like we've never seen before because AI will help the humans be better at their job, open up more of a window of time to do other things, right? So as you guys make things easier to understand consumption and value in the catalogs, that's going to open up me as the user to do other things. This is like the whole beauty of democratization. Yeah, and I think, look, we talked about democratization as well, but there's also a new generation of innovations that are going to occur. As you kind of layer on these kind of capabilities down the line, what we expect to see as we kind of, our analytics allows customers to understand what is going on, understand that value here that ultimately is going to provide paths for innovations that we cannot even imagine today. That's an exciting thing for me to say as a product guy, and I've done this for 20 odd years now, part of building a kind of cloud capability, part of building kind of platform level capabilities like this goes along the lines of the knowledge that I'm not quite sure what people will do with this, but I'm only excited by what I'm going to learn. You got to be pumped up as a veteran in the data and business that this AI is almost like a fountain of youth for us old guys and old players. Because think about what's going to happen next is you're going to have a lot more value being created out of the data, not just managing it, like so much more enablement really is coming. That's right. And from my perspective, Elation is the nucleus out there that people are going to realize, working with companies that have Elation will ultimately under a better position for you to partner with us and with our customers to kind of unlock these opportunities that are nascently there. That's not so much an ecosystem. I think it's like a net new economy that we're going to see. And that's a legacy. I know my team is proud that we're kind of supporting and I think it's an exciting journey that we're all going to go on together. But there's going to be data around us and data packing those data insights in a way that is very consumable, very actionable, that's attached to the business value. That's the differentiator. That's the differentiator, I think, so few have been able to go through. But I think at the core where Elation stands, we stand in a very concrete ability to be able to deliver on that. You're going to help the existing and then now create a new generation of value, new people going to pop up, new value. Great stuff. It's interesting. I remember the old days of big data during the Hadoop wave. It was like, oh, bank tellers are going away. ATMs are going to kill the bank teller. They're more bank tellers than ever before now than before. And if you look at the chess market, when computers and AI came to the chess world, which they like to talk a lot online, so it's well documented, there are more grand master chess players today than there were before AI and computers came into chess. That's right. So this is what's happening here. There will be more grand masters, AKA data masters going forward probably. I love that, data masters. I think we need to build that, do a t-shirt run on that. I think that would be pretty cool. We'll do a little co-creation with a smart conflict. I love it. Let's do it. So look, I mean, I think as we look at the sum total of our conversation here, I think that's just, we are providing a set of tools, a control plane, a set of understanding to our customers, to the community, to the ecosystem at large, whereby that data understanding, the connection to where you are on that data maturity, being able to kind of have a scorecard that's credible, that's relevant to your business, and be able to have a visual representation of that in a way that is so much more consumable, is going to empower, it gives the tools for those who want to stick their neck out, neck out within an enterprise or going out on their own, right? To be able to do new and interesting things that ultimately will deliver value in ways we cannot imagine. That's an exciting place to be. We're in a good market. You got a big tailwind. Congratulations to all the success for the team and being in the industry for so many cycles. It's going to be a great Cambrian explosion of opportunity. Jonathan, let's take the last minute for you to explain and put a plug in for Elation and also your event, Revelation, which is happening in London. You've got a global conference series that's beginning now. Who should attend? What's going on with the company? Give a quick commercial. Sure. Love a good commercial. So Elation, Revelation is our moment with our customers. We come together, three parts of the world, in London, Chicago, also Sydney, Australia, to help share what we've been working on. But more importantly, the journey we are going on. The journey we're going on to help bring data intelligence to their data. This past year has seen an acceleration in investments around generator of AI, around our new ability around Elation Analytics Cloud, and a whole seven investments around what we call democratizing data cataloging, democratizing data insights across all the tools that we all live and work with today. So we all live in this highly distributed manner. You're in Palo Alto, I'm in San Rafael. I work easily with people all over the world. It's really kind of providing that messaging, providing that capability in a way that's super intangible. But the most exciting thing for me is meeting our customers, learning about their needs, and really helping share our plans, our ambitions, and helping us refine and tweak what we're going to do in the next 12 to 24 months. Jonathan, Bruce, VP of Product Management at Elation. Thank you for spending the time on this Cube Car City and breaking down the news and putting a plug in for the global event you guys having. Thanks for coming on theCUBE. Thank you. Elation is using Elation Analytics Cloud to help organizations measure the business value with their data initiatives. This is a big part of this next wave of building on previous generations of data engineering, data management, and also finance and governance. Okay, as the world comes together and starts scaling, AI will scale your value and intellect for corporations. This is what we've been covering on theCUBE. And we're here talking about it with Elation. I'm John Furrier, your host. Thanks for watching.