979: Agentic Data Management and the Future of Enterprise AI, with Rohit Choudhary

23:49And when you get the alert, there are a lot of existing systems which go and do the alert sifting and they tell you which alerts to pay attention to and what not to. But I think the world that we are going to go look for, and I'm looking forward to that, is that which are the alerts that I should be fixing right away and which are the alerts that I have time. But not just that, give me the fix which I can then go and deploy in production and sort a lot of issues. So the speed and the velocity of execution will increase. Now, one of the things I've been talking about, you know, for the last few months now, is the cognitive overload of these systems.

24:26Producing so much information at such rapid pace is something that we'll have to get used to. We as humans are not used to this mode of communication. And I think, you know, while there will be agents which will keep assisting us, I think we will also have to develop mechanisms through software, which will take us to the most important things up front, as opposed to asking us to sift through all the details.

24:52Jon Krohn:Right. That makes a lot of sense. We've picked up on other interviews where you've given where the whole thinking shifts around how you need to build your data systems, that the governance needs to be completely different, that you can't just have a one-time governance initiative that's fixed and is going to be lasting for years. Instead, you've said that we need active governance to be watching the system and figuring out where we can be fully automated versus where we need to still have judgment made by humans. Absolutely. I mean, applying regulation and governance post data is already ready for consumption is not the best idea in this world because agents are going to apply themselves and extract information from along the life cycle, from the point of origin to the point of consumption, agents are going to make queries and access data for their own purposes and benefits across this pipeline.

25:49And so if you think of governance only being limited to the end, to the very end, which is data which is ready for consumption, I think it's already too late for factors that we talked earlier, but also because agents are no longer limited to the consumption data, which was known through the medallion architecture traditionally for human consumption. That's changing. And so therefore, the governance which is required is actually more operational in nature as opposed to a process. It's no longer a regulation slash compliance need. It's actually a company operability operations need. And therefore, governance has to change.

26:26It is very, very different from the governance that was applied for many, many years ago. And I'll just break it down for you. If you entered a data governance officer today, maybe two years ago, maybe the view is shifting pretty dramatically right now, but two years ago, every single governance unit or team in any significant enterprise would go with the following stack. Let's go get a data catalog. Let's find ways of doing master data management. Let's go then find out a data quality tool. The problem is that each of these companies, they were operating off a substrate of metadata that was disconnected.

27:06And that disconnected metadata was fixed by this whole new paradigm of data observability. So data observability provided us the capability to have metadata, which was not just from the consumption layer, but all the way from where data was getting produced to all the way how it was being shifted, moved, translated, and finally how it was ready for consumption. Now, on top of that substrate of metadata and access to high quality of data, now it is possible to do governance in a completely different way, which is real-time, which is model-centric, which is inference-centric, because inferences are not waiting for two months.

27:43Inferences will happen when the agents are able to glean information across your pipeline, as opposed to just the end. And so I think there's going to be a massive shift in the way people think about data governance. It's going to become operational first. The second thing, which is an even bigger trend, is many of these tools that I talked about earlier, they were owned by different groups, some with technology, some with business. There's going to be a big convergence of personas because as the tools converge to the substrate of high quality of metadata and the workflows on top, what you will end up finding is that different governance groups which are using different products and tools today will rationalize those tools and say, all right, we're going to build these agentic flows on this platform, on this substrate of metadata.

28:28And I think that's a big shift which is coming in the next five years, particularly when humans and agents have to get access to the same data.

28:37Jon Krohn:Speaking of organizations trying to do things the old way when it doesn't work anymore, you've also stated previously that enterprises are trying to retrofit the previous generation of application monitoring platforms to monitor their data platforms and that that paradigm doesn't work at all? Yeah, that paradigm doesn't work at all. And I'll just break it down. It's fairly simple to understand. Applications were built for human interaction to monitor clicks, to monitor browser strokes, click strokes, experiences, and the interaction between microservices and the information that it was extracting through CRUD databases.

29:16Now, given that there are some hyperscaling or hyperscaler-like companies like Meta, Google, Facebook, they have a completely different requirement of their relational databases. But if you go to a typical enterprise, they probably have hundreds of thousands of users, in some cases, high million users, and that is not actually creating the problem. The real problem is getting created after the fact, when petabytes of data flow into data lakes every day. The customer conversations that we have are as follows. We have 1 ,000-plus applications, IT applications in general, which span different domains, different countries, different lines of businesses.

29:56All of that data needs to be harmonized inside data lakes and data warehouses. And when you think of that world, the application monitoring paradigm was built for the system of records and the thousand IT applications that get deployed, including microservices, monolithic services, etc. It has nothing to do with how data is moving, migrating, translating inside data lakes and data warehouses. is the consumption unit and the users who consume this data, the kind of processing that happens today across CPUs and GPUs, they have nothing to do with a primary CPU-based architecture, which essentially is meant to store and provide back the information when it is queried.

30:38These are completely different paradigms. And therefore, the APM world just doesn't apply to this modern new data world.

30:45Jon Krohn:As speaking of things moving quickly and past paradigms no longer being relevant, while reading your blog posts and watching your presentations and interviews as we were doing research for this episode, it was apparent that your vision for Excel data transcends classic terms like IT, data platforms, infrastructure, and pipelines as passive tools. It's more about the enterprises, teams, and individuals who will be active participants in an intelligent operating environment where data, AI, human judgment converge to drive decisions and outcomes. In fact, we've got a blog post that I'll put in the show notes.

31:21Jon Krohn:It's called Convergence of Personas, How AI is Reshaping Data Management Functions. And in that, you wrote that AI is eroding clear boundaries, collapsing personas into a more fluid, dynamic model where functions blend and expertise shifts. How should organizations rethink career paths and incentives to ensure depth of expertise isn't lost, but we have that kind of flexibility anyway? I think there's a lot of value in what AI is bringing, and there's a lot of value that humans will bring to these AI systems. There's a period of transition right now. And the transition is from going from completely human-centric systems, designs that were built only for humans, to a world in which this is going to be a collaborative world where agents and humans will have to work together.

32:09It's a reality. I think people will have to wake up to that. I think OpenClaw has been a fascinating moment for all of the AI world. and I'm actually pretty excited about seeing where this world goes from here on because there's so much work that these agents are autonomously going to do and accomplish for these teams. Now, if you think of what that world means, it effectively means that if you are a person who has the capability of doing critical thinking and structured language, you can accomplish a lot. So the previous paradigm had certain restrictions that you had to be a great programmer and then on top of it, you would need the business domain and the expertise.

32:52Today, what has happened is that the level at which you can interact with these business systems, the GPUs and the agents which can do work for you depends upon the clarity of your thinking and your own curiosity. And if you are a person who has a lot of determination of being very clear in what you expect the output to be, along with the expertise that you know your industry better than the others, you're more likely and most likely to produce outcomes and systems which are way better than some of your competitors, peers will produce. In that world, I think one will have to prioritize the presence of these skills within individuals who possess both of those things, creative thinking and a lot of structured clarity of thought.

33:38And I think that is the only way that organizations will progress further. In terms of the value of domain, like I mentioned, there's this period of interim transition between human-centric to agentic plus human-centric world. And in that world, I think a lot of domain expertise is going to get used to train internal agents to fine-tune LLMs for LLMs or SLMs to suit the purposes of your own organization. And those skills will then be required in the future.

34:09Jon Krohn:Nice. You used a term there, SLM. I'm sure all of our listeners are familiar with LLM, large language model, SLM. Probably most listeners are familiar with that as well, but small language model. And this is a really exciting area where you don't necessarily need to have these big, think about Claude Opus. You don't need to have a Claude Opus size model running for every kind of task. I've had a lot of success in doing consulting for enterprises or in startups that I've been a part of fine-tuning very small, say, open-source LLAMA models that just have a few billion parameters, they can become very, very good at a narrow task when they have a high-quality training data.

34:48Jon Krohn:You wouldn't ask them to do anything, but for that one task or this relatively narrow set of tasks that they're fine-tuned for, they can excel. 100%. I think SLMs and LLMs will be part of the enterprise stack, I think as will CPUs and GPUs and ASIC. It's got to be an XPU architecture with SLMs and LLMs. And I think a lot of IP will start getting embedded in the SLMs as well, obviously because of privacy concerns and everything else. I mean, if you were to just think and just abstract it out, I think AI is breaking the centralization model. When I think about this whole world, for the last 10, 12 years, the whole thing was about centrality of let's get all your data together into a data lake, into a warehouse, into a location, into a cloud on-premise environment, whatever's your preference.

35:39I think there isn't enough time for such large scale migrations to take place. I think AI models will have to get closer to the data where it resides and operate on that to provide the enterprise outcome that people need. I don't think the speed of AI matches the physical realities of large-scale migration and centrality. So it is going to be a decentralized world in the future. And if you just extend or extrapolate that argument, it's hard for me to see that only LLMs will win. I think LLMs plus SLMs will win.

36:13Jon Krohn:I agree 100%. And the frontier LLMs from proprietary providers, they allow us a very easy playground to experiment with what's possible. but it's not always going to be the best production decision. That's right. It also depends upon what's the complexity of your use case. Are you looking for a full-time PhD researcher or are you just looking for, you know, and what's the best way to put it? An intern who's willing to work on a few projects for you. It just depends upon what the complexity of your scenario is. Right, exactly. For navigating these kinds of complex scenarios, a particular role that a lot of enterprises have latched onto that have made important in their organizations is a CAIO, a chief AI officer.

36:59Jon Krohn:And last year you wrote an article, a chief AI officer won't fix your AI problems, where you criticized organizations that appoint a CAIO without spreading the responsibility across the organization. You argued that AI isn't a standalone initiative. It's a capability that should be woven into every facet of the business. And that over the next few years, CAIOs will likely begin to disappear from organizational charts. And I've actually, I've had guests on in the past who are experts at bringing AI into enterprises. And they've said that the chief AI officer, when one is hired, they often barely last more than a year because they're kind of expected to change everything, but then they're not given the the responsibility or the power to make a lot of change?

37:46So that's definitely one part of it. I think there's the other part of it, which is what is the real problem of AI? I think it's a three part problem. One absolutely is the availability of the right set of data, which most of the enterprises are still struggling with. I think it's also identification of the most critical use cases and being very clear about what are deterministic outcomes and what are probabilistic outcomes at the moment. Because what ends up happening is that many of the POCs that are currently running, they never make it to production. And if that continues to happen for over a period of time, the overall enterprise disappointment with these AI initiatives just keeps growing.

38:34And so while it is extremely easy to see the chat GPD-like excitement and seeing how much content and tokens can it generate and what answers can it give, the answers that the enterprise needs are not generic in nature. They're actually more specific in nature. And so the more specific that the AI officers can get the use cases and define and have narrow boundaries for AI projects, I think the faster that they can actually accomplish a lot of change. What I also find, which is the third pillar, which is it is also a question of adoption because, you know, AI is a big hit, a roaring hit in consumer.

39:14It's a roaring hit in prosumer for companies like, you know, Cursor Cloud, you name it. But where it is kind of, you know, not there is in the enterprise right now. And I think I'm going to make a prediction that in the next 12 to 18 months, we'll see massive use cases coming out of the enterprise. And it is because what it requires is that enterprise AI is not a solution to all your problems, but it is a great solution to a lot of your complex problems. And I think across the enterprise, when you go and look at and ask the leaders, what are you doing today? They are already doing a lot of automation on the front office.

39:51They've done a lot of automation in the middle office. And I think the back office is just there. We saw this with cloud adoption as well. And so there are like ringing parallels on this. You know, initially it seemed like cloud adoption was great and then it slowed down and then it picked up like it never picked up before. So what ends up happening is that certain enterprise harness capabilities that you require from a security privacy governance point of view, along with a reference architecture, it evolves and after that, you know, it's go, go, go. And I think, you know, we're at the cusp of that moment.

40:25Jon Krohn:Really great perspective there and so easy to understand. Another piece of this puzzle, in addition to having the right people or giving them the right responsibilities, say a chief AI officer, another big issue that enterprise face is having data or not having data, their own proprietary data. And this seems to have a big impact on their competitive positioning. So given that both high quality data and AI will drive large parts of the economy in the future, you've previously made a sharp distinction between companies that are data haves and data have-nots, as well as companies that are AI haves and AI have-nots.

41:05Jon Krohn:As agentic systems like those that you have in your ADM, in your agentic data management platform, as those agentic systems become more powerful, do you see this divide between haves and have-nots becoming a long-term structural advantage in most industries if you do have the data and if you do have AI? I think so. I think public markets are a great place to look at. If you look at what has happened in the SaaS economy in the last few weeks, I think everybody's aware of what is going on. One of the perspectives that's evolving, and obviously these change from time to time, but the important thing that I found out from this recent collapse in the SaaS valuations is because the market is actually not willing to price a future AI.

41:53It's actually looking at what are the capabilities of AI that you have today and how soon are you going to be able to generate revenue out of that. And I think that distinction is going to grow wider and wider. And the comparatives are shifting quite dramatically because an entropic is going from strength to strength, destroying value accrued in different industries and domains by just the announcement of agents, which are expected to do the bulwark of what these industries were supposed to do. So I think it is a moment in which the AI haves will have the preparedness and the willingness to have AI first revenue versus the AI have nots will unfortunately be on the other side of the equation because either they don't have the comprehensive AI capabilities in their platform or those AI platform capabilities are not being used by their customers.

42:47And I think both of those would be disastrous.

42:50Jon Krohn:Right, particularly in an environment where the amount of data that these halves have is increasing so rapidly. Something that surprised me because I very frequently state that the amount of data, so when I'm giving a lecture, giving a keynote or whatever, a stat that often I bring up is how the amount of data that we have on the planet that we're storing is about doubling every year or something like every year, year and a half. But at a conference, you said you expected enterprise data in particular, not to grow twofold year over year, but you're seeing now more like four to five X. And that in a couple of years from now, you expect it'll be seven to eight X.

43:31Jon Krohn:So basically, let's call it 10 X. It's kind of like, it's exponentially increasing. It is happening. I mean, think of it this way. If there were a million users, using your product and they were creating logs, interactions, and all of that data for you. Imagine what will happen when you'll have 10 million agents running a mock on your database, on your customer base, on your data. And so the amount of activity that is going on right now in the enterprise and is foreseeable in the near future, then you're actually looking at data, which is going to go through the roof. And are we prepared for that?

44:06And that is the real question and the real answer that everybody's trying to figure out. Think of it the other way around, and I'll give you a different perspective now, and I'm not talking about activity monitoring. I'm talking about imagine the number of applications that are expected to go live in the next two years. You and I today, John, can sit here, and while we're speaking, we can build applications and deploy it on our favorite platform. And it'll take us less than 20 minutes, and it will be an extremely meaningful workflow that our customers might like. And so what ends up happening is that a lot more applications are therefore going to be live.

44:44And therefore, they're going to capture a lot of data. And therefore, that's going to hit your databases, data lakes, and data warehouses. It is going to hit your network. It is going to hit your storage. It is going to hit the speed at which you are able to store and retrieve data. So this is a completely different world. And data, therefore, is going to go explore, expand. And the question that I keep thinking about is that in this decentralized architecture, do people have a unified control plane through which they can manage their data? And that is what, you know, our mission all along has been that, you know, we're going to enable the management of this massive scale data, which is coming, you know, to all the enterprises today.

45:24Jon Krohn:Yeah, you're definitely building the right product for today as well as for the future over there at Excel Data, no question. Let's talk a little bit about how you came to be in this market-leading position, market-defining category, the leader in agentic data management. From founding a mobile meeting solution and leading engineering at Hortonworks to creating this new product category with Excel data, your journey has been driven by a product-first, customer-anchored philosophy. you've in the past described startup building as not a marathon, but like a Ironman triathlon followed by an ultra marathon right after it.

46:08Jon Krohn:Um, and yeah, that it takes six or seven years before a product becomes mainstream. And that whole time you're educating analysts, buyers, users about what it's like. So tell us about that journey from when you founded Excel data almost eight years ago now to being able to now have created this new product category of Agentech data management? Yeah, I think obviously my leanings have always been in terms of products because I think that's the best form of expression that I know. And I appreciate the problems that enterprises face at a very, very deep level. Having been an operator myself, I understand how difficult and complex it gets to run engineering teams, production support and what taxes engineers the most.

46:55And so I had deeper appreciation and empathy for that, which is why we chose this industry, because it was closest to the personas that, not just I, but the rest of my co-founders also shared. I think the second thing which you touched upon, which is I think equally interesting is what actually goes into building companies. And I think it's super important to understand that, you know, obviously products are products, but you also need people and processes along with that. And you have to have the ability to keep your ear to the ground and keep listening to your customers. I had attended a great talk from somebody at Business Objects a couple of years ago.

47:37And on one of his slides, he had this, you know, beautiful quote that said, dance with Hoobrangia. And I said, okay, that's great. Because, you know, if your customers brought you along and they gave you the first few opportunities, that you might as well just listen to them because, you know, their problems are not solved. Our share of wallet for the problems that they have is not yet done. And so therefore, you've got to keep listening to what are they telling you, where the head is at, where they headed into, what is the direction that they're going to take. And the more you can align yourself with the vision of the customers and keep abstracting it for the industries that you intend to operate in, I think the easier it becomes to, you know, build products.

48:17I think at this moment in time, the entire thing is about who can build better products, who can serve your customers better, who can maintain relationships with all the customers and your customer base in the best fashion. Can you serve your customers in the way that they expect you to be served? And I think those are key things that go along in building a strong company. Obviously, none of this is possible without people. It's questionable as to how many people do you need going forward. but I think you need the best people with the most amount of clarity and the most amount of curiosity. I think those things go about building a big company.

48:53Obviously, we are still starting out. The final point I want to make was that, yes, it takes a lot of time. I reflect on the journeys that we had at Hortonworks, which was already a built technology. We had already tested most of the technology in the open source community. We had already stamped it as accurate. the same story happened at Kafka and Confluent, and the same story we saw with Spark and Databricks. For many, many years, you're almost just building without any real market traction. But once the product comes together, once you've identified, understood, and delivered to the customer expectations, use cases, critical challenges, then it's just a GTM problem.

49:34I'm not dismissing that as not being a problem, but that GTM problem does not arise till the time that you didn't get the right product.

49:41Jon Krohn:I like how you say that you're just getting started when you've been doing this for eight years and already raised over$100 million in venture capital or about$100 million in venture capital. That's pretty wild. And yeah, it shows like your big vision for what you're doing. John,$100 million these days are being spread on seed rounds, just to let you know. Yeah, but not very often. I mean, that does happen, but it's... And that also, that really only happens in the Bay Area. I think you're around seeing some of that. It's, you know,$100 million seed round anywhere else in the world, I think it's still unheard of.

50:23Jon Krohn:But yeah, you guys are doing a phenomenal job over there getting everything going. Speaking of, you know, having the right people, it sounded like a big part of how you are able to have so much success at Excel data. In a tweet, you wrote that products with global appeal are not short on capital. They can get$100 million each round. And eyes are on those teams, founders, products, where it's the imagination of different, better, and when articulated well enough, the money, the teams, the customers, they recruit themselves into the mission. Tell us about that, because it sounds like there's a lot of useful insights there for any of our listeners who are trying to build a product or build a company.

51:10When I go back to my InMobi days, where I was a founding engineer, what I found out that the team that assembled together at InMobi, and this is way back in 2008, they had the need of expressing themselves through high quality engineering work, which was then reflected in the products that we built.

51:26Jon Krohn:And so if you think that you want to build the best team, then you've got to get a mission that aligns some of the people who care about that mission. And it's therefore at that moment self-selecting. I mean, the amount of hours that we work in startups, it's insane. And, you know, a lot of people have, you know, that as the most important aspect of their life. Obviously, everything else is important, but for them, the ability to go and create something out of nothing matters more than anything else. And so there are people who actually come and join startups at different phases. There are people who are experts at getting zero to one and then one to 10 and 10 to 100 and 100 and beyond.

52:08And these are different kinds of people. But if you are a company which is continuously doing zero to ones, one to tens, 10 to hundreds, then obviously you have a lot of space for people who can join you at any part of that journey. Because if you're an innovative company, you're going to keep building newer products, which gives the ability for the creatives to come and express themselves. and then you give the operators the ability to go and manage the products that are coming through the pipeline every year, every couple of years.

52:34Jon Krohn:I think the second most important thing is that what is your personality type? Do you believe that you have the need to build something or do you have the need to manage something? And both of those options are great, but those are two different options. And therefore, it depends upon what you want to select and what outcomes do you want from it from a life perspective. I think the third thing which we have emphasized on is that we have values. Now, we have global offices and therefore culture in all these offices are local to those centers, but the values are global. And the values for us are that we are builders at heart.

53:11We're going to be extremely genuine, respectful and humble about our interactions and simple about our interactions with our customers, with our internal employees, with our investors, with any other stakeholder that we find. either internally or externally. And if you align with those values, then you know you're a great fit. Obviously, we also pay a lot of attention on your craftsmanship. Do you care about the work that you're putting out? Would you actually take it very personally if the work that you produced had some critique and comment, or will you just look at it and say, okay, here's an opportunity that I'm going to just iterate on it and make it 10 times better.

53:49And if you look for these kind of mission-driven people who care a lot about the quality of the work that they put out and the kind of teams that they would like to work in, you end up finding the right people.

54:00Jon Krohn:Nice. Beautifully said. And yeah, it sounds like going back to a conversation we had right at the beginning of this episode that, you know, if these are people that you already have worked with before and you trust, there can be fewer bumps in the road potentially as you go and build a product company together. Really cool. And Rohit, we're starting to wrap up the episode. There's something that I was supposed to tell you before we started recording. So usually I tell my guests in advance that at the end of the episode, I always ask for a book recommendation, but I forgot to tell you that. So I don't know how well prepared you are on that question, but do you happen to have a book recommendation for us, Rohit?

54:45Yeah, sure. I mean, one of the books that I really liked was, there are several, can I make three? You can make three, Rohit. Zero to One continues to be a great book. It's a great read for no matter what stage of your life you are at. It's an awesome book. And the second would be The Beginning of Infinity, which I have come to appreciate a lot. It's a fascinating book. And if you're interested in things in abundance, in the concept of abundance, I think it's a great book.

55:14Jon Krohn:And so when you say that, so Zero to One is a book that I'm familiar with. I do like it a lot as well. It's got great insights on, yes, life in general, but also building a startup, building an AI product, especially now today. It was remarkably forward-looking, given that it must be about a decade old, that book Zero to One. This book, Beginning of Infinity, that you just started describing. So I thought maybe it was kind of about astronomy. But it sounds like it's about having abundance on our planet, thanks to AI and automation and abundant energy and all these kinds of things. No, I think, yeah, I mean, it talks about abundance.

55:55It's also about, you know, the creation of knowledge, which is, and it also argues that all kinds of progress, you know, whether it's scientific, societal, technological, it stems from a relentless, rational pursuit of reasonable explanations, which are consistent over long periods of time. and you know David Deutsch he proposes that this idea of knowledge creation is always at the beginning it's never you know at the end which means that you know if physics was at a certain place in the 16th century it is very very different in the 21st century and it's going to be very different in five centuries from now but it's just the beginning of the process and I think that's a fascinating concept and it helps me to have a beginner's mindset all the time that you know you You can just forget about the work that you did until yesterday.

56:47You can build on that. But the reality is today. So anybody, you know, and this especially applies in the age of AI. It's all of the things that we learned in the last 50 years. I think we're just saying, okay, that was a good foundation, but what's next? And I think that's super interesting.

57:02Jon Krohn:Sorry to quickly interrupt you before you give us your third book recommendation, but you talked about beginner's mind there. Are you a meditator, Rohit? I am, yeah. I am. I've been practicing yoga for the last, you know, 10 years. and it's a great practice, you know, highly recommended. It just cleans your mind, takes the residue of your day-to-day friction that you encounter in your life, whether it's traffic, active work with customers, it's just a great level. Yeah, yeah. Well, it actually, you know, it shows in the way that you conduct yourself in this interview from even before we started recording.

57:36Jon Krohn:We took a brief, we took a, listeners and viewers couldn't possibly know this because it happened before the episode formally started. But we took a little break before we got going. And even just the way you handled yourself in that, there was this sense of calm and ease that came across. And I just knew that this was going to be such a great episode because of that. And so I came back, sat down, was so excited to start recording. So it's working. No, I appreciate it. Thank you. I highly recommend it. Nice. All right. And yeah, your third recommendation, Rohe, your third book, Rec? There are a few, but I'm going to go with, um, the gospel of Ramakrishna.

58:14It's a deeply spiritual book. It actually moved some of my thinking. And I know it's not a very popular opinion at this point in time to, to talk about spirituality, but I think it's the most popular opinion in terms of, you know, figuring out what gives you satisfaction, what gives you contentment in life. Nice.

58:33Jon Krohn:I like that a lot. I, uh, yeah, I I've read a number of, of translations of the Bhagavad Gita and, uh, those have been pretty influential on me as an adult. And yeah, so this kind of thing I am into. And so gospel of Ramakrishna sounds interesting. Maybe I'll check that out as well. Well, Rohit, thank you so much for the sensational episode for people who want to be getting more of your ideas and thoughts after this podcast episode, how should they follow you in Excel data? Well, Excel data is on LinkedIn and on Twitter, and I'm RC online, both on LinkedIn and on Twitter. Most of my thoughts are posted on LinkedIn these days.

59:18That's where most of my professional circle is, and I care a lot about that network. So best to connect with

59:25Jon Krohn:me on LinkedIn. Nice. Yeah, that is where most of our guests are converging over the past couple of years for sure. It's funny how LinkedIn's strength is that it doesn't change. It's like, it's just so familiar. It's so funny, you know, social media platforms. It's like, they're all, I guess, like most product companies, they're constantly innovating, constantly changing. But it seems like that means a lot of the platforms that were popular in the past have kind of faded. Meanwhile, LinkedIn hasn't really changed. And that's turned out to be a strength. Yeah. I mean, I think, I don't know what the next generation of LinkedIn will be.

1:00:02Will there be like a social network for agents who are doing work that we are doing today? Will they require, you know, the whole Morgbook thing was so fascinating and obviously now open claw, but I don't know. Maybe LinkedIn is due to change. Maybe there's a disruption coming.

1:00:18Jon Krohn:I don't know. If we have any listeners making product decisions at LinkedIn, never change. Just stay the way you are. It's perfect. Okay. Yeah. Great. Great, great, great. Great episode, Rohit. Thank you so much for being on the show. You have such a busy schedule. So to make the time for us, it really means a lot. We appreciate it. Of course, John. Thanks for having me today. What a wonderful episode with Rohit Chowdhury. In it, he covered how he coined the term data observability in 2018, how fixing bad data at the point of consumption can be roughly a thousand times more expensive than catching and fixing it as it flows through the pipeline, how for your enterprise data to be AI ready, they need to satisfy multiple dimensions, technical accuracy, where numbers are numbers and strings are strings, business context compliance, where the facts in your structured data actually align with your policies, your procedures, and regulatory requirements.

1:01:10Jon Krohn:He talked about how he predicts enterprise data will grow 4 to 5x year over year right now, accelerating to nearly 10x soon, driven largely by the explosion of AI agents generating queries and activity at a scale that dwarfs human users. And he talked about how in the age of AI, the most valuable professionals won't necessarily be the best programmers. They'll be the ones with the clearest thinking, the deepest domain expertise, and the curiosity to articulate precisely what outcomes they need. As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Rohit's social media profiles, as well as my own at superdatascience.com slash 979.

1:01:50Jon Krohn:Thanks, of course, to everyone on the Super Data Science podcast team, our podcast manager, Sonja Breivich, media editor, Mario Pombo, partnerships manager, Natalie Jaisky, researcher, Serge Massis, writer, Dr. Zara Karche, and our founder, Kirill Aromenko. Thanks to all of them for producing this super episode for us today for enabling that super team to create this free podcast for you. We are so grateful to our sponsors. You can support the show listener by checking out our sponsors links, which are in the show notes. And if you'd ever like to sponsor an episode, you can get the details on how by making your way to johnkrone.com slash podcast.

1:02:24Jon Krohn:Otherwise, share this episode with someone that would like to hear it, review it on your favorite podcasting app. If you write an Apple podcast review, I will read that on air. We really appreciate you doing those. helps get the word out about the show. Subscribe if you're not already a subscriber, but most importantly, just keep on tuning in. I'm so grateful to have you listening and I hope I can continue to make episodes you love for years and years to come. Till next time, keep on rocking it out there and I'm looking forward to enjoying another round of the Super Data Science Podcast with you very soon.