Data Visionary Bob Muglia

This week we have the pleasure of having former Snowflake CEO Bob Muglia on the show again. Bob is an active investor and sits on the boards of many next-generation data platform companies, and more recently, he launched his first book — “The Datapreneurs.” Given the long history between Bob and Madrona Managing Partner Soma, we had to have Bob joins us again to talk about his new book and dive into the world of data and AI. These two old friends discuss what exactly a datapreneur is and the Arc of Data Innovation concept Bob wrote about in his book. They also talk through how companies can add value with AI through copilots and agents and what white spaces and opportunities there are for entrepreneurs right now — especially when it comes to semantic models.

The 2023 Intelligent Applications Summit is happening on October 11th and 12th. If you're interested, request an invite here.

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This transcript was automatically generated and edited for clarity.

Soma: Hello, everyone. I'm Soma, a managing director here at Madrona. Today I'm really, really excited to have Bob Muglia, a datapreneur himself with a large body of data platform work to his credit across Microsoft, where he was one of the topmost senior executives and then, most recently, he was the former CEO of Snowflake. Bob is also an active investor and sits on the boards of many next-generation data platform and tools companies. Before we launch into our conversation today, Bob, I do want to take this opportunity to congratulate you on publishing your first book, Datapreneurs.

Bob: I appreciate it. It was more work than I anticipated it was going to be.

Soma: But Bob, given your experience and accomplishments in the world of data over the last 35 plus years, I'm not at all surprised to see the focus of your book, which explores the people and critical pivots and technology history that catapulted us into the modern age of computing and AI. Bob, I thought we'll just jump quickly into a set of questions that I have for you today to start off the conversation. First, how do you define datapreneurs, Bob, and more importantly, how do you see their contribution in building the data economy of today and in the future?

Bob: Well, a datapreneur is simply the concatenation of a data entrepreneur together, and that's where the term comes from. This realization, I worked on the book with my co-author, Steve Hamm, and when we first started writing the book in 2021, we really didn't have a specific objective in mind. We didn't start by saying we're going to wind up writing a book. I knew I'd had some things to say and communicate, and we were trying to decide the best vehicle to do this, and after Steve and I talked for a little bit, it was obvious there was a narrative that could be had and turned into a book, and so we started outlining the chapters and things.

In that I realized, in that process of those conversations, I realized that throughout my career, even though I was working at a really large company like Microsoft for a good part of it, I was actually working with very entrepreneurial people and teams building largely new products for Microsoft and for the industry and certainly doing things that were very much revolutionary for the industry. I realized that I'd been working with entrepreneurs all along, even though they were at this big company. So that's where the idea came from and the recognition that the technology that we experience and live with every day is really the culmination of the work of thousands and thousands of people at many, many companies.

It certainly includes the work of some of these great datapreneurs that I highlight in the book, and their accomplishments have really led us to where we are today, so I wanted to highlight that and describe why some of these things are so important — and give us some people and some background on the technology so they have some foundation because obviously, it's impacting all of us as we see AI being really the topic du jour of 2023 and clearly going to have a huge impact on all of us.

Soma: That's great, Bob. Having read the book, I can tell you that you referenced a number of datapreneurs that you worked with in the past and ones that you're working with currently in a variety of, sort of different companies. But first and foremost, Bob, when I think about you, I think of you as a datapreneur, given what you've accomplished with data at Microsoft and then more recently at Snowflake and the impact you're having today with a variety of startups. I see you first and foremost as a datapreneur. But you also worked with many other datapreneurs over the span of your career. How would you categorize the role the datapreneurs have played in your career?

Bob:Well, they're the source of all inspiration in some senses. I'm not the deep technologist that builds the product and the code. Mostly I was highlighting the people who did that who were actually in there building core parts of the technology and creating some of the revolutionary ideas that have led us to where we are today. My role, as you know so well, since we worked together for many years, my role was really what Microsoft would call a program manager, the modern term is more of a product manager. But in the Microsoft definition of it, it was really more about the product and building the product and defining the product for the customer.

So I'm used to specifying things, talking to customers, understanding their requirements, and then passing those requirements on to the technologists and the architects inside the engineering teams that build things. That's sort of always been a core part of my role. As a manager and as an executive, a lot of that comes down to leadership principles, running organizations, instilling values into teams, things like that. I see my role as being very different than a lot of these brilliant people that are actually coming up with these incredible ideas. I couldn't do that, but I have the ability, hopefully, to help provide them with some guidance to help as people are building the products.

Soma: Absolutely, Bob, absolutely. I want to go back to a little bit the Microsoft timeframe, Bob now. If I remember right, you came pretty early into Microsoft. At that time, Microsoft was working on Windows and OS/2 and LAN Manager and starting to work on Windows NT and maybe even the early days of-

Bob: Actually, it was slightly after that. I mean, it really got started in the latter part, right?

Soma: Yeah, yeah.

Bob: But yes. That's the very beginning, so.

Soma: From your perspective, if you look back in time, when did you feel like, hey, data is going to be a key, key part of the future of the world? When was that moment in time where you felt, aha, there is something here that is going to fundamentally change how the world is going to be operating?

Bob: Well, no matter how much I tried to get away from it, I kept coming back to data or data kept coming back to me in a way. My first job, the technical job while I was still in college, was working for a company in Ann Arbor called Condor Computer, which had a relational and honest to god relational database. It had a joint command. It was not SQL, it was the very earliest days. SQL was just emerging at that point. This was in the late 1970s, early 1980s. It ran on a tiny microcomputer with these massive 8 inch floppies that stored almost nothing and literally 16K of memory, literally 16K of memory. That was my first experience, and that was building applications for companies.

In college, I was focused on communications, and my first role, my job out of college was at ROM Corporation, which built a telecommunication system for business, an internal switch called a PBX. Our term was a CBX. That was ROM's product. I worked on the team that configured those products. So again, I had a data-focused job, and I was building data-oriented solutions when I was at ROM. It was really that experience at ROM that led to my moving to Microsoft, largely driven by my wife's desire, our collective desire to move up to the Seattle area, we were in the Bay Area at the time, and her really finding Microsoft as being an incredible company that was emerging back then.

I wound up joining Microsoft as the first technical person on SQL Server. In a way, that really cemented the focus on data throughout the rest of my career. Because SQL Server, the PC version of SQL Server that Microsoft offer, really did revolutionize business for smaller companies. A large part of what we did in the 1990s was bring out both the server products as well as the database products and tools products. Visual Basic was a big part of that. So you had Windows Server and SQL Server and Visual Basic is the front end for a lot of those early applications. That's what automated the dentist offices of the world is that software. Some of it's still running probably, which is probably not a good thing, but that really changed the way people worked with information.

Soma: That is cool. That's cool. I'm going back and forth in time here, but I want to sort of revisit what happened in the 2017 timeframe. That is when we connected up upon, hey, Snowflake is coming along, is starting to make some revenue, has got some customers, and starting to realize the power of what Snowflake could be kind of thing. First of all, thank you for giving us the opportunity to invest in Snowflake at that time and be on the journey with you.

But one thing that I remember from one of our earlier conversations, Bob, is I was thinking about, Hey, how much should we invest? Because remember, we are an early-stage investment firm, but I remember you telling me, "Hey, Soma, you're going to make money in this deal. I can't tell you how much, but you're going to make some money." I was thinking, "Hey, how much money am I going to make?" Because the valuation was a little higher than what we were normally seeing at the time.

Bob: Seemed rich at the time.

Soma: Yeah, it seemed rich at the time. In hindsight, I would say, "Hey, you just foreshadowed what the world was going to move toward kind of thing." But I did not imagine, and I'll be the first to tell you, I did not imagine what kind of a trajectory Snowflake could have at that time. In fact, so much so that when Snowflake became a public company or went through the IPO process, ended up being the biggest software IPO in history. How did you see the trajectory, and how did you feel about Snowflake becoming the largest IPO kind of thing? But more importantly, what does that signal in terms of the importance of the data cloud platform to the future of the computing world?

Bob: It was really great working with you in the early days. That was really when we were looking at opening an office in Bellevue and bringing in talent in the Seattle area. That was driven by our desire to move to Azure as a platform, as a second platform. We were on AWS already, and we wanted to add Azure as another choice for customers. The realization that there was nobody in the Snowflake team that could work with Azure down in San Mateo, California, we needed some people up north to help that. Obviously, this has worked out very well. I knew the company was going to do very well. It certainly exceeded my expectations in that regard, and the IPO was extremely successful.

I would say it was really successful from this perspective, but for public investors, I have a little bit of concern because the stock went way high, and it's now still trading below that amount, which is definitely hard for public investors. But it's been incredibly successful, and I knew it would do well. How well, I didn't know. The reality of it was the reason it's been successful is because it solved a problem that was not solved before, and that was how do you scale your databases for analytics to be able to consume and work with all the data you wanted to at the same time and make that information available to all your end users.

Up to that point, the technology before Snowflake really didn't enable that, and Snowflake was a breakthrough product in the sense that it really was the first to enable a general-purpose SQL database that allowed for unlimited scale. I think that has been amazingly valuable because people are working with data and information. It's only getting more valuable over time as we begin to find more and more uses for data, and, certainly, the emergence of these large language models and artificial intelligence that is obviously of great interest right now, that raises the importance of data even further. So I think all of these things are important.

Now, Snowflake, as I've said a number of times, I mean, I see Snowflake as being in three somewhat different businesses. They're in the sort of more traditional data analytics, data warehouse business. They're in the data sharing business and have now established a very strong position in helping companies access data that is outside of their organization and make sure the data can be appropriately shared within the organization. Then more recently, they've been really focusing on an application platform, a coherent application platform, the so-called data cloud that provides a set of coherent services for people that are building these next-generation intelligent applications, AI applications, data applications, call them what you want, and then enabling those to run anywhere that Snowflake runs.

They've emerged as an important player in the application market, and that's a fairly new space for them, but it's going to become increasingly important as we begin to build application services, these intelligent application services, that take action on our behalf. The fundamental distinction is, is that a typical business application acts on behalf of a direct request from an end user, from a person. These class of applications take actions based on information that's coming into them, and they can do things on their own. So it's a whole different way of working than creating different business process, but I think it's going to become a very big part of what people are doing in the next five years.

Soma: The thing that was interesting to me, Bob, as I thought about Snowflake and the Snowflake's journey kind of thing, you literally have five what I call data cloud platform vendors of some scale and consequence in the world, right? You got the three hyperscalers, the cloud infrastructure guys in Microsoft, Amazon, and Google, and then you got Snowflake and Databricks. I don't know that I would've predicted even, say eight years ago, that there's going to be five cloud data platform at scale vendors kind of thing, right?

So it's just fantastic to see how the hyperscalers obviously, that's not a surprise that they are sort of the meaningful players here, but the fact that Snowflake and Databricks have been able to come to where they are today, it's just fantastic to see the innovation and how broad the space is and the opportunities ahead in terms of how this can fundamentally change what I call another world of computing and by extension all applications and everything else that people do on that.

I want to come to your book now for a second. There was a great thing that you talked about in your book called the arc of data innovation. To me, now, I was excited when I sort of read through that because it sort of helped me think about, "Hey, how do we visualize that technology progress while making predictions, knowledge predictions, or what could happen in the future kind of thing?" Can you talk a little bit about how you came up with that and how that concept came to life in the book?

Bob: It was always, in a sense, the central concept of the book. It had been present from the earliest versions of it in some form. The idea is that there has been an acceleration of progress over time as technology has continued to improve. That's why I drew it as an arc because the line represents the speed of progress, and that's increased as the decades have gone by. The idea of it was to identify both the key data types, the key types of data that were introduced. People don't always think about it. There's structured data, there's text data, there's semi-structured data, and then there's what people often call unstructured, which I would call complex data, and that's video and audio. It really has structure associated with it. It's just that the structure is so complicated that we tend to think of it as unstructured.

Those sources are now real sources of data that they never were before in the past. That idea of that progressive evolution was always present. The interesting thing is that the finish of it changed while I was writing the book because when I started writing the book, the apex of it was the data economy and the idea that data is a central part of what we do in our business. What I recognized was that as I was writing the book and I saw the explosion of what's happening in the AI space, I realized that the horizons that I had thought in my head for when some of this intelligent technology was going to become available and useful and really achieve some major milestones like artificial general intelligence, and potentially super intelligence, those were going to happen in a much shorter horizon than I expected.

If you asked me in 2021, when would we have AGI, I would've said 2100, and now I will say 2030, thereabouts. That's a pretty dramatic change. Frankly, it was a breathtaking realization for me because I had always believed that we would develop this intelligent software, that that would come, but I just didn't think it was going to happen in my lifetime, and now I realize it is. The implications to all of us are just really profound. So the arc of data innovation really now goes to super intelligence and potentially a technological singularity, which is just really a continued acceleration of progress largely faster than human speed. I think it's going to happen. It's certainly something that, in general, I think it'll be very positive, but I know it's also a bit scary at the same time. I think it is not 50 years away, I think it's probably a lot less than that.

Soma: Yeah, I think this notion of predicting when you're going to see major inflection points in technology, I think it's anybody's guess kind of thing, but it's one of those things where sometimes we overestimate, and sometimes we underestimate. I actually remember when I was still at Microsoft back in the 2012, 2013 timeframe, if you talk to any of the traditional automobile companies, "Hey, when are you going to have a truly self-driving car?" They were all talking about 2030, 2035, 2040. Then for a while there, now with the self-driving progress that companies like Tesla made, everybody thought, "Hey, in the next three years, it's going to happen." The reality is it's somewhere in between, right? But the rate of innovation is the one that I think I'm more strict there about because that sort of signals what is possible and whether it is actually 2021 or 2025 or 2030. We cannot debate that.

Bob: Well, I mean, having played with Teslas and things. From what I could see, the technology was still a few years away. I had thought, and this is just getting reinforced in the past set of months, that the 2030s would be the era of robotics and that we're going to see another explosion of innovation in autonomous robotic devices that are part of our lives in a variety of ways, ultimately resulting in humanoid robots that have intelligence and can do things on our behalf. The interesting thing about a car was the autonomous car, which was I think about an Uber ride, and every Uber ride that I've ever been in ends with a brief conversation with a driver about where to drop you off.

I was like, "Well, how are you going to do that with a machine?" Now I realize you can just tell it. Now you'll be able to tell the robotic car where to drop you off. So I mean, some of the problems that seemed very intractable, now that English is a language that computers can understand, I mean, a whole bunch of problems that were there before disappear and become at least potential to solve.

Soma: That is very true. That's a part of NLP or natural language processing. I guess you're right. Hey, you can talk to a machine. Now pretty soon, I think I know you can already touch a device with touchscreens. You can now speak to a device. The interaction — the more you can interact with the device or with a machine or a system, similar to how you interact with each other, the more natural it's going to be, and the more people can seamlessly think about computing in an ambient environment as opposed to, hey, I need to walk up to a machine, or I need to walk up to a device, as opposed to, I'm going through my life, I'm doing my job, I'm sort of having fun, and I can leverage technology in my natural workflow, so to speak.

I think that's an exciting world that I'm looking forward to. Like you said, for a while there, it wasn't clear whether it's going to happen in our lifetime or not, but I think the rate of innovation, there is a fond hope that could happen well within our lifetime. So we'll see how it goes.

Bob: Even for old people like us, even for old people like us, but for the youngsters in there, I think they're going to see a whole lot. For people who are in the earlier stages of their career, they're going to see a massive amount of change. I mean, think about the fact that these devices have intelligence in them. We've always been able to put rules, and we've always been able to program rules in a computer, but now you can actually take and have intelligence associated with decision-making. It's really extraordinary. It's an extraordinary innovation.

Soma: That's true. Bob, you can't talk to a company today without them telling you why they're an AI company. Every company says that.

Bob: You can't, you can't. That's for sure.

Soma: But here is the thing that, and we sort of have this taxonomy, at least a mental model that says, "Hey, there are going to be AI-native companies, and then there are going to be AI-enhanced companies." Which means most companies that exist today have to think about how to incorporate AI in fundamentally core parts of what they are doing as a business or as a company kind of thing. The challenge, though, is all of these companies have an existing what I call data architecture, how they think about data, how they deal with data, how they process data, how they take advantage of data kind of thing. As AI becomes more and more central and core to every business and every company, how should an existing company, where should they even start about, hey, I want to be a AI-first company, but I do have a way of doing things today with data. What should I do? Any sort of thoughts on what you tell companies?

Bob: One of the really interesting things about this technology is that it really can be additive to existing applications and add value. I think Microsoft demonstrated that with this co-pilot approach, which I think will be the predominant, at least initial, way in which AI will be incorporated into existing applications, which is these agents that sort of support you in your efforts to do things. I think most companies who have an existing product can take and modify that product to put in one of these co-pilots that can build off of the knowledge basis that it has, and fundamentally, essentially just be a much more effective help system to help you work and navigate through the product you have.

If you don't have an existing product but you have a business that you're running, one of the interesting things that now there are opportunities to build applications that couldn't be built before. Here I always just say, "What is the domain expertise that you bring?" Because it's now possible to effectively take that domain expertise and leverage the intelligence in these large language models to bottle effectively the expertise you have and put it inside the application so that the knowledge and behavior and processes that you created can now be run and executed by the machine versus by people that previously performed these actions.

So there's opportunities across the board to do that if you're in an existing business, whether you have an existing application where you can obviously take the knowledge base and everything you did and directly incorporate it or whether you're building something new. Then I'd say, just to continue along the lines of the different types of apps, you say there's just new kinds of applications that you couldn't build before that now, for the first time, can be done.

Soma: That is true. I fundamentally believe that AI is going to permeate every industry, every company, every walk of human life kind of thing, okay? But having said that, if you think about the different verticals or the different industries, do you think some are more naturally attuned to having the most innovation and positive impact by AI? Or do you think it's just going to be across the board?

Bob: I think it's pretty horizontal. I mean, like anything, there'll be uneven distribution of it. Certainly, different industries will have different speeds of adoption somewhat based on the regulatory challenges they face. Frankly, in some industries, take the medical industry for example, we have to make sure that the technology is matured to the point where it's working correctly. I mean, if AI hallucinates and writes a poem incorrectly or something, or it puts something in a poem, it's one thing, but if it misreads a doctor's notes and that results in a patient getting the wrong drug, that would be very bad.

So you want to make sure you have very high degrees of accuracy in certain scenarios. So that will take a little bit more time, but every domain will be impacted. It will certainly impact a lot of people in their careers and jobs because the more mundane sort of repetitive tasks, the tasks that tend to be repeated, those are the ones that are historically been the providence of people. Those are the sorts of tasks that will wind up being automated first.

Soma: One of the things that I really enjoy about your book is in addition to talking about data, data technology, the modern data stack, what's happening in the world of data kind of thing, you go through what I call case studies or specific examples of companies and the kinds of technologies that they're building. You talk about many, many different companies developing solutions for what I broadly refer to as the modern data stack. But having said that, do you think there are still white spaces or big problems that need to be solved? Particularly if you're talking to the next generation of startup founders, what would you tell them about, Hey, what opportunities exist in the modern data stack? Or do you think that's all being solved, and we should just move on?

Bob: Well, it's not all being solved. Let me just start by saying that. The modern data stack has made great progress. As you mentioned earlier, there are really five different vendors providing solutions. It's good to see those solutions maturing over time. They all come from different places. They all start from a different place, but they're all kind of building the same product now. They're all heading towards similar products that they're creating. Although there's strengths and weaknesses of the vendors, like I say, largely associated with their history.

There are definitely open spots in the modern data stack that still need to be filled, largely around compliance and management. Those are areas where I think there's a lot of challenges still. Certainly, access control and managing coherent data access control continues to be a significant problem that I don't feel is well solved. I think one of the biggest areas of future potential is building and creating what people talk about is semantic models for different things. In particular, I think it's interesting to think about the semantic model for a business. If you look at any company's business product, any organization's business, and their process associated with executing their business, it's the knowledge of how that work is scattered in different spots.

It exists somewhat in the applications they run. It exists somewhat in the analysis things that people do using tools like Tableau and Power BI. It exists in the heads of a bunch of people. It exists in documentation and probably maybe more than anything in Slack messages. It's all over the place, and it's not very well defined. In order for these language models, these agents, to behave in a way that is consistent with what we want, they need to understand the semantic model of the business. So I think we have to be much more explicit about that because if we want these machines to do things for us, we're going to have to explain what we want them to do. Today, where would they look? I mean, it's all over the place.

So I think centralizing that is going to be a fairly major opportunity. I've made the statement, I think this is an accurate statement, which is one profession, which has emerged as a really interesting profession in the last few years, which is data engineering. I think that profession's going to change over the next few years to become business engineering. We'll begin thinking not just of the way the data is modeled, but of the overall business model and ultimately will derive the data models from it. That's where this concept of knowledge graphs come in. I believe these things can be very coherent with the large language models.

Soma: Bob, one of the things that you've done in the last couple years, at least now, is be an active investor, as I mentioned before, in the next generation of data companies in one way, shape, or form. If you put on your investor hat for a minute, how do you evaluate which company you want to invest in and be a part of kind of thing? What trades do you look for as you make that decision?

Bob: The traditional thing is the best return on your investment, right? That's probably the traditional focus of most venture capitalists. That's really not high on my list. I mean, I want something to be successful, obviously, but actually focusing on the actual return is not what I'm caring about as much. I care more about the technology and the impact that the technology is going to have on the industry and the world as a whole. I try and focus my time on companies that I think are doing things that have the potential to have a material change in the way the industry works.

One of the reasons working with infrastructure technologies, it provides the opportunity to do that because they tend to be very horizontal versus just applications which are focused on a given vertical or a given industry. So most of my focus is on horizontals, largely because I've always had kind of an infrastructure focus. So in a way, my criteria is about the importance of the technology, the relationship with the entrepreneur and the CEO, and the potential impact for this in the world. That would be where I really put my key focus. To me, the return is a more secondary thing. In fact, one of the questions people always ask is what's your check size. I'm like, "Really, I follow what you need and what the other investors do." To me, the important thing is supporting the organization.

Soma: I actually agree with what you said, Bob, about what you look for kind of thing. Because as much as the venture capital industry is about making sure that we have the right returns on our investment, not just for us but also for our LPs kind of thing, to me, focusing on the team and the impact automatically will lead you to great reports of goal along the way kind of thing, right? So I-

Bob: I think they're very related. I mean, they're very related together, but it's just a question of is it a spreadsheet-driven decision or is it more of an impact-driven decision. It's the latter.

Soma: Got it. Bob, before we wrap up, I want to ask you one final question. If you were to start a company today, what kind of an AI company would you start? I'm assuming it'll be something to do with data and AI, so I'm presuming that but what would you want to do in today's landscape?

Bob: I will do this serendipitously through the entrepreneurs that I work with, and I am sort of doing that in a way, but it's really what I described about that business semantic model, that to me is building high-level semantic models of everything is of interest. I think about this one sometimes, but I haven't braved myself to go into it yet, which is the semantic models associated with legal contracts and laws because I think there's a discipline called computational law where you can apply computational approaches to legal problems and really a contract is a program. It's a program that is interpreted by lawyers and executed by people.

I don't think that's the way things are going to work for very long, because, in a fairly short time period, machines will also interpret these contracts, and much of the execution of the tasks will be done by the machines. So I think we're seeing a transition of high-level semantic concepts into executable form that can be operationalized within the business. So it would be along those lines that I would put my focus right now. Whether the domain is management of the IT infrastructure, which is an interesting domain, or whether it's a different domain like law, all of those things are potential applications.

Soma: Bob, as always, it was a fun conversation. Thank you for taking the time to be here with us today and really enjoyed the conversation. Thank you.

Bob: Thanks so much. It's always great talking to you.

Coral: Thank you for listening to this week's episode of Founded and Funded. If you're interested in reading Datapreneurs, visit, www.thedataepreneurs.com. If you're interested in attending our IA summit, visit ia40.com/summit. Thank you again for listening, and tune in in a couple of weeks for our next episode, which features Mark Nelson, former CEO of Tableau, and James Phillips, former head of Power BI. You won't want to miss these two discussing product-led growth and scaling in the face of big competition.

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