Pre Gartner Data & Analytics Rants with Juan and Tim

[Music] Hello everyone, welcome to Catalog and cocktails, your honest no BS non-salesy conversation about enterprise data management with Tasty Beverages in hand. I'm Tim Gasper. Hey, one. Tim, we are at our hotel and we're recording from our laptop right now with that in my so apologies if it doesn't sound so great. Yeah, hopefully. This is the best background noise. This is the honest no BS we're doing this like this so. All right, this episode is just two of us. A lot of our travel special didn't get us so we can have another guest. We wanted to check in with you all and just say we're excited next week. We're our gardener conference. Yeah, we're going to be in Orlando hanging out with customers and partners and friends and and all of you. So look out we're we're going to be there so please find us. Tim and I will be at the service now booth and we'll be wandering around so yeah we'll love to catch up. Yeah, but so gardener. What are you excited for? What are your predictions about gardener? What are you what's on your mind of what's going to happen over there? I'm excited for. Sorry sorry, by the way, what are you drinking? Oh yeah, let's talk about what we're drinking for us. We're drinking for us. I can't talk about what I'm excited about before talking about how excited I am for my drink. So I have a a mezcal grapefruit cocktail. Yeah, forget what it's called. Mead night mezcal for midnight or something like that. Yeah, so it's very grapefruity. I was just telling Juan before we started the podcast here. I was like, I don't taste any mezcal in here. It just tastes like grapefruit juice. But it's good. It's good. Okay, and then I'm having to talk about the lost boys. It's like a sazirac. All right, the honest don't be honest. These are our hotel cocktails. Not the grape, but. What's fun, Miranda? They taste okay. They taste very little pricing. Cheers. All right, to back. What do you think about gardener? Another we've gotten that out of the way. So the things that I'm excited about at gardener. In addition to seeing friends and connecting with all the people, right? Because that's always the I think the most fun part about gardener. I'm curious to try to find signal from the noise. Because I think that everyone's going to be talking about yet again, talking about AI ready data and how do you get ready for AI? And I think we're going to hear context context context context. I think that's going to be everybody everywhere, every sub-industry, whether it's governance or BI or whatever is going to be talking about how the key to making AI successful is you need context. And so we're going to hear that a million times. I feel like so I'm excited about finding the signal and the noise around all of that, right? Because I think that you've got semantic layers at BI companies that are trying to navigate this. I think you've got the data lake companies that are trying to up level what they've been doing around medallion architecture and data products and elevate that into more of a context story. And I think you've got software and platform companies like ServiceNow and SAP and Salesforce and Palantir, etc. who are thinking about and talking about context as well. And so I think it's going to be interesting to see what's working, what's not, what's on the mindset of people, where's the innovation, and what's going to be most impactful? So what's on your mind? Well, of course we're going to see context. Like, this is a reminder. So remember when everything was data mesh and everything was data products and stuff like that thing, that's kind of, it's really crazy. It's really fascinating to look at these waves, right? So definitely the wave is going to be content. I'm actually curious to see how much the word metadata is going to be. Metadata has not been replaced by context, right? So even if it's like semantics is not even the word anymore, it's context. But I'm feeling that context is becoming the superset. Yeah. Like, well, metadata is a kind of context, right? Yeah. So then one thing I fear is that we're going to be focusing so much on the context conversations and you need context for AI because AI does understand your business and get it, get it. And I'm like, what? Remember, that's just a means to an end. And I feel that I bet that we're going to have so many conversations. We'll talk about this and vendors will be talking about the stuff, but they're talking about the means and the means and then like, oh, this is context for AI, but at the end of the day, AI is also a means like, what are you going to be doing with it? What are you trying to do with it? I think this is the most important thing that we're going to go focusing on right now is what I bring up is like, organizations don't just have a data problem, a context problem, and they have problems. I mean, yes, they are. They do have those problems, but that's not the ultimate goal that they need to go solve is that they have is this, think about, I call it like the work problem, but more of them, they need to get work done. And I think the problem about how they get the work done today is because we live in so many silos, there's all these gaps. And I've been thinking about this and I'm calling it like the execution gap or the action gap, which is, think about data teams, they own one part of, let's call it way on their silo, they're on the part, so they get all this data from different places from all these operational sources, it ends up inside of their data warehouse like something, do things with the data that generate those insights, and that's where their world ends. And that's where the responsibility ends, that's where their accountability ends, and they're so there in a way, they're doing their job, but then they still complain that they can't show all the airlines, but then, but then you have your operational teams, right, your different business lines, they start at a level at an area which is not connected to where the insights are. So there is this gap, there is the whole insights, and then how you want to take action with those insights. And there's that gap, and actually, that's where everything is, that's why the work isn't that efficient, that's why we have all this AI and all these agents, and I'm like, you have, you give the data to the data to the agents, you give the context to the agents, you give the insights to the agents, you give the governance to the agents, but we need to be able to bridge that execution gap and somebody needs to take ownership of it, and I think that's the opportunity for data teams to start taking that ownership. And I really, that's where I'm going to be pushing people in our conversations of like, if you're thinking about context stuff like, perfect, yes, we need that, definitely we need that. How are you bridging that gap, but I'm calling this execution gap, and that's what we need to be focusing on. I think you're bringing up a really important point, and if I was going to make a prediction, I have a feeling that next week in Gardner, we will see a lack of emphasis around the action piece. Because I think you're right in that, like, why do we care about context? Like, why are we even talking about that right now? It's because we want AI agents to, when given data, do the right thing. And at one level, that can be answering questions, right? And I think a whole question answer to the chat with your data. Yeah, there's a lot of the sort of imagination around AI agents in the data space is limited right now around kind of question and answer paradigm. But we, like, is that all we want out of AI is we want to be able to ask come into customers to what I have and it to be able to say 500, right? I think we want more. I think we want agents to then do something. Yeah. Right? And actually, I have faith that it's actually going to be, we're going to have those conversations of action. And it's because of the coming popularity I'm seeing about decision intelligence. Right? There's this you magic quadrant of our list of its companies. And actually, gardeners have been talking about decision intelligence for a while, right? Now, but if you look at those vendors in those areas are very disconnected from the data analytics for right? So I think the conversations are going to start to happen, but they're going to happen again in science. But I really look forward to like, this is going to be my test is like, if people at Gardner wrote the data leaders, practitioners are going around and are they making those connections? Are they realizing, okay, yeah, we can't just write, but I think the AI to be able to go to the work. And there's this work, I need to be able to bridge this gap. This is what I'm calling this execution gap. And I need to be able to take a decision and insight that he's asked upon and where does the decision need go to? And I'm like, this is where I really hope that that people really start seeing this. And that's that's why we're going to be pushing forward. So this is what we need. Like, otherwise, we're going to keep keeping the same in the means of means and at the end. And think part of it also is to start to figure out how to go quantify what is the cost of that gap? Because the cost of that gap is there's like the execution latency of doing things. There is the cost of people deciding we need to move so much data between things. People trying to bridge that gap. They're trying to go swivel sharing so much around that stuff, right? You don't have the clear context of what the operational things mean for this. So you're you get the wrong ant, you get answers that you can't trust. So we need to start kind of really quantifying what these the cost of this gap is. I think that's something we're still open to go do. We need to go figure out. But at the end of the day, I think the way to start bridging this gap is to have this optionality that we can say we need to there's this execution, this work thing to happen. And I need to bring data to where the work is happening. And that data can be maybe you're moving it, maybe it's virtual zero copy style. I need to bring the context to that so the context needs to be managed, but you know that context needs to be used by different places. The insights need to be brought to where the work is happening. The governance you happen, the governance you're brought into the work is happening. So I think those are all kind of the pillars, kind of the technical pillars. But at the end of the day, we need to understand that there is that execution gap. Anyway, that's I'm hopeful. Yeah, I think I think you're thinking the right way. I think that what's interesting is, you know, data people are increasingly moving away from being the sort of the plumbers and the pluggers, right? And they're becoming the managers of knowledge, semantics and context. And I think that shift is going to be highlighted. And it connects to what you're talking about because what's going to start to happen more and more is the data team is going to be the ones trying to take the context and then closing the loop with the business so that the right automated actions are being informed by the right signals. And so more and more the data teams going to get plugged in. And so actually, I'm going to make a prediction that actually faster than we expect. Maybe I don't know how much to come up at Gartner next week, but faster than we expect actually the decision intelligence side and the data side are going to collide into each other fast. And that they won't stay silent very along because I, you know, we're going to see, you know, a big data warehouse or data lake company buy like a process mining company or something like that. Like, like, things are going to happen that are going to really quickly collide these things. Well, we're I think that needs to happen. And I think it's already starting to happen not to get too vendourished, but we do hear it. So we're not too, so, but anyways, one more thing which I don't think is going to happen at Gartner, but I'm curious if people are going to think about it is if context is the most important is the next big thing, what about vendor lock it? Because we talk about, oh, we have the vendor lock you think about data, okay, my data. And now that's why we've had their data gets moved. People want to go move it. They don't want to go move it and so forth. And then like they're the concern on locked him with this vendor. And one of the reasons why we have all these like open table formats and like iceberg and something we'll be able to do with that. But now if you're managing your context, the question is how are you making that context? Where is that being managed? You're going to have now a new type of vendor lock him when it comes to this knowledge and the semantically context. Are people thinking about that? I don't think that they're thinking about that right now. I'm wondering who is. And my answer to this stuff is like that's what you have to have standards open standards that already exists. If I already have it, it will exist. I'm curious if that's what people are thinking about. If they do think about standards, they're probably thinking about the whole OSI stuff, which I still think is more about the knowledge, which is definitely a first step. But when we think about context, it's much more than just getting a little stuff. So that's all I'm curious to see if this is on people's mind. Yeah, which I doubt it is. I'm curious to see. Yeah, I think it's I think that's a good question. Yeah, I guess we'll we'll find out more next week and we'll we'll report back and kind of what we're finding. But obviously it's clear that context is very top of mind for for people, for organizations. And you know, I think every single kind of topic area from machine learning to business intelligence to decision intelligence to data management. It's all going to tie back to like what role does it play in kind of collecting, managing, disseminating context? So to summarize, context, you know, is going to be a big thing. The question is if it's thinking people think about context with respect to actually doing something like taking the actions. What about the whole decision intelligence? How is that going to come together? And are people thinking about context as like the vendor locking for context? Yep. All right, we'll see. We'll report back. Combine us and part of the planet. Maybe we find people who can quickly kind of have a quick interview, ask you a question about what keeps you up and I, whatever, just to see what some people find. Ping us, ping us on LinkedIn or X and let's connect. We'd love to chat and ask you some questions. Here what you think. Cheers everybody. Cheers. Cheers.