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Today, Investor Sabrina Wu hosts Bobsled Co-founder and CEO Jake Graham for the latest episode of Founded & Funded. Jake had stints at Neo4j and Intel before joining Microsoft, where he worked on Azure Data Exchange. Jake is revolutionizing data sharing across platforms, enabling customers to get to analysis faster directly in the platforms where they work. Madrona co-led Bobsled’s $17 million series A last year, which put the company at an $87 million valuation.
In this episode, Jake provides his perspective on why enabling cross-cloud data sharing is often cumbersome yet so important in the age of AI. He also shares why you can’t PLG the enterprise, how to convince customers to adopt new technologies in a post-zero interest rate environment, and what it takes to land and partner with the hyperscalers.
This transcript was automatically generated and edited for clarity.
Sabrina: Jake, it’s so great to have you with us today. Maybe to kick things off for us, why don’t you start by telling us where the inspiration came from to start Bobsled?
Jake: Absolutely. I’ve always wanted to start a company because I love the competitive aspect of business, the idea of trying to go out and win and bring something into the market. Over the first 15 years of my career, I found that you can get that in pockets in larger organizations, but it’s hard to get that feeling that it’s only the efforts of you and your team that stand between you and success.
I’ve always been on the lookout, but I’ve also always had two tough requirements before deciding to jump in. I wanted to believe in the idea. I wanted to feel, especially in my data infrastructure space and focused on the enterprise, that I understood the problem and that a product in that space deserved the right to exist. It was critical to me to have at least one strong co-founder. For me, especially, it was a CTO I deeply believed in, and I thought we could win together.
As far as how Bobsled itself came about, I sometimes joke that life has three certainties. There are death, taxes, and reorgs. And it was a reorg that made Bobsled exist. I’ve been at Microsoft Azure in a role that I enjoyed. I spent about 18 months building the strategy and plan to create an Azure Data Exchange and an Azure data ecosystem to make data significantly easier to share across Azure consumers. And we finally got a significant budget to start and hire in earnest to start building.
When that happened, a significant reorganization resulted in the birth of Microsoft Fabric. Microsoft Fabric was a fantastic decision. It was and is a product I’m excited about, but it made what I was building not make sense at the time. It’s finally starting to make sense as Fabric has gone GA. But I remember thinking, I took a run, and I realized that I’d finally uncovered a problem specifically in this case; it was around not just data sharing but changing how data is accessed for analytics and evolving data integration into being cloud and not AI-native. I was motivated to solve that problem. I’d finally found something that deserved the right to exist as a successful business.
It would also be totally unfair for me, not to mention that my wife had been pushing me to start something for a while. She realized that — I wouldn’t say that I would be happier founding because this is a really hard job — but that I would be a lot more fulfilled. So, my wife, Juliana, was the secret co-founder who pushed Bobsled to exist.
I spent a couple of days maturing the idea. I started thinking, who do I know I can start to bounce this off of? I called the person who is the best engineer I’ve ever worked with. The gentleman I worked with at Neo4j. We hadn’t spoken in probably a couple of years. I gave him this idea, and he said, “Jake, are you asking me to be your co-founder?” And I said, “I mean, I’m starting to think about this.” And his response was, “I’ll quit tomorrow. Let’s do this. How long do you need? I think we should get going quickly.” I was really taken aback, and I said, “Tomorrow feels a little fast, but we can talk again tomorrow.” At this point, I sat back and realized I was starting a company.
Sabrina: I love that, and I love the co-founding story. It’s definitely a testament to you as a founder to be able to attract great talent and build Bobsled. Can you help us set the stage more and explain what exactly Bobsled does? What traditionally were companies doing before Bobsled to access and share data?
Jake: Bobsled is a data-sharing platform that allows our customers to make their data accessible and available in any cloud or data platform where their customers work without having to manage any of the infrastructure accounts, pipelines, or permissions themselves. Fundamentally, the product is pretty straightforward. You grant Bobsled read access to wherever you store your data, whether in files in an S3 bucket or indirectly within Databricks in BigQuery, Snowflake, et cetera. You reach out to our API to say, “This data needs to be consumed by this individual in this cloud, this data platform, and be updated in this way.” We manage intelligently and efficiently replicating that data to where it will be consumed for white-label infrastructure. Whoever’s consuming that data feels like they’re getting a share from ZoomInfo or CoreLogic, or any of our other customers, but they didn’t have to build any of those pipelines. It allows them to move from putting all the work on their consumer to making it easy without suddenly having to manage an infrastructure footprint in every platform where their customers work.
It’s still crazy to me that the volume of data used for analytics, data science, and machine learning has grown a couple of orders of magnitude over the last decade. The actual mechanisms for that data to be accessed are almost exclusively the same as ten and even 20 years ago. The overwhelming majority of data that’s used to drive any form of data pipeline is either pulled out of an API or an SFTP server. That doesn’t make sense in a world where so much of the value being generated by modern enterprises is in data and in which you need that data to be consumed by others, whether in your organization or others, to extract that value. We needed to see the cloud-native data exchange mechanism take off.
The reality is that the right way to solve this problem is generally by the actual data platform itself. The best way to access data within Snowflake is Snowflake Data Sharing. They have built a fantastic experience where you don’t have to go through and ETL data from another platform. You can query live, well-structured, up-to-date data as long as it’s already in the same cloud region and in Snowflake.
That sharing mechanism was pioneering, and every other major platform followed it. The problem is that it puts a significant infrastructure burden on the actual producer. We want to move away from a world in which every data consumer has to ETL the data out of whatever platform it’s in. The issue you get with that is that sharing protocols aren’t connectors. It’s not just taking the traditional model of we’re going to toss data over the wall. They provide a better consumer access experience because you have to bring the data to where it will be consumed. You have to structure it in a way that is ready for analytics and then grant access to it.
I believe data sharing would be how data is accessed for modern analytics and ML pipelines. But in order to make that happen, the data producers needed a way to interact with all of these systems without having to do it all themselves. That’s fundamentally what Bobsled is.
Sabrina: As you alluded to, data sharing is a critically important part of the stack. I love how easy Bobsled has made it for data providers actually to share the data and how you’re also agnostic, to your point, across the different cloud platforms. If you’re on Snowflake or you’re on Azure, maybe it isn’t easy. But you’re allowing companies to share across different cloud platforms.
You’re flipping this idea of ETL on its head, which is one of the parts I love the most about what Bobsled can do. I’m curious, though, to this point about different cloud providers, what role does cross-cloud data sharing play in this new age of AI and the new way that companies are starting to build?
Jake: Someone recently told me, “I really hope this ML wave sticks this time.” I’ve been working in ML for over a decade, and it gets bigger every year. This just seems kind of like a natural evolution of what we’re doing.
Something you talk about a lot is the idea of intelligent applications. That term applies to an enormous amount of where the market is going. There’s something there’s a really strong definition around. The way I think of them is they’re applications that leverage data in order to better automate whatever workflow they actually solve for, and then also generate more valuable data as a part of that. I think that’s true whether you’re leveraging an LLM, or you’re leveraging more traditional machine learning, or whether this is built on more human-in-the-loop analytics. In order to continue to move toward this more data-driven and now AI-focused age, data has to be able to move between the systems and organizations where it’s generated.
One of the things that people are starting to realize is that often, in any application, a lot of the value it provides is actually in the data generated by running that application. There’s an enormous amount that you can do in that workflow to use that data to improve it and continue to automate it. Another thing we’ve learned about data over the past decade is that it becomes valuable when it’s blended with other data sets. Within data, almost always, the whole is greater than the sum of its parts. When you realize that if you want to think intelligently and predict, and I think even more if you want to do that in an automated fashion using LLMs, you have to be able to bring in the data that represents different aspects of a problem, and that is never sitting in one system.
We saw a push led by Salesforce around the Salesforce Data Cloud. Well, if we can get everyone to bring all of their data into our application, we can solve all the problems. And the answer is no, you can’t. You might be able to answer many questions, but in reality, data is being generated across this enterprise and its partners and other vendors. It needs to flow into the systems where it’s going to generate insight. I fundamentally believe that data sharing will be the mechanism to do that.
How I think this shift enables the move toward the age of AI is we’re going to allow every company to create data products and to have them be accessible wherever they need to work without, again, having to manage an infrastructure footprint and have an army of people who understand how does clustering work differently in Databricks versus Snowflake? How does the permission protocol work differently in an S3 endpoint versus an Azure Blob store? How do I replicate this data? There are better things for people to be working on. Bobsled is going to be a lot of the plumbing for how the world becomes AI-driven.
Sabrina: Data is key to what everyone has referred to for years as a critical part of the modern data stack. You’ve hinted that you think the modern data stack is at a turning point. We’ve talked about this a little bit, but I’d love you to walk me through your thought process. What is this turning point? How do you think that might impact the tech sector and startups overall?
Jake: My general feeling about the modern data stack is it’s no longer a valuable term because it won. There was a time when the modern data stack described a few companies and categories that were bringing analytical infrastructure into becoming cloud-native. That was a relatively exclusive set of companies, so it didn’t include any of the previous vendors who weren’t cloud-natives, whether that was Informatica or even Microsoft, in a lot of ways at that time. And it also didn’t include any part of data that wasn’t pure analytics. There was a separate data science and an L stack, and there’s been a separate operational stack.
I think what has happened is that those incumbents have caught up and are now cloud-native. The companies that weren’t purely in the analytics stack have started to move in there. The more successful companies in the modern data stack are branching out beyond it. The discrete categories within the modern data stack are blending.
There’s been a lot of unnecessary angst around the death of all the companies in the modern data stack. I view it as a victory. The modern data stack is just now a key part of technology. We’re moving from a purely software-centric technology market to a data-centric one. That’s the idea for me of intelligent applications, or if you want to call it the age of AI. The software is incredibly important, but it needs the data. It’s no longer enough to build for a very specific set of users in a very specific category. We now have a much larger field to play in, but also, it’s a much more competitive field.
I think that’s a little bit of what people have been shying away from in the modern data stack. It is like: But wait, this company we thought was going to be a unicorn isn’t going to get there just by solving this slice of the problem.
I go back to the first thing I said: I love the competitive nature of this. I love the fact that every day you can wake up and figure out, okay, how do we execute our way to win, and how do we make sure that we’re solving real problems and that we can bring those solutions to market, and that we can get that feedback loop going? A lot of modern data stack companies are going to be incredibly successful. I don’t think that term is valuable anymore.
Sabrina: Do you think there’s going to be more consolidation of the players within the data stack? Do you think people are going to start to bleed into the different swimming lanes?
Jake: It’s going to happen for good reasons because the most successful company is going to want to keep growing back. Again, this is a competition. You have to win more every day. You’ve earned the right to expand beyond existing categories. I think that’s really good.
I use a joke term: the enterprise strikes back. The vast majority of software spending in the United States is done by the Fortune 1000, and that’s true in the world. The way in which enterprises buy technology is fundamentally different. The idea of fully adopter-led tying together many different solutions is just not working.
Benn Stancil is probably the best writer in our space. I don’t want to parrot his words and take credit for them myself, but he wrote a really great piece on this, with the example of Alteryx. About a lot of startups are looking at Alteryx doing all of these things, and they can only say they do their best in class. It became easy to say, “We’re going to attack this part of it.” Until you get in and realize Alteryx is selling to the enterprise and that complexity, it’s not for nothing. It’s because they built it to meet their customers’ needs. And that breadth is in and of itself a feature.
We’re seeing a little bit of that enterprise strikes back — of the way in which software was built and brought to market for a long time. There’s still a lot of value in it. We need to learn to blend some of the PLG and more pure means of software and product development with some of the realities of your enterprise is going to get you to the promised land. If you can’t add value to the largest companies, you’re putting a pretty low ceiling on yourself.
Sabrina: One phrase that I’ve heard you say before, and we’ve talked many times before, is that “You can’t PLG the enterprise.” Maybe you could talk a little bit about what that means and how you view that statement.
Jake: It means a couple of big things to me. Holistically, as an industry, we’ve lost respect for the enterprise sales part of enterprise software. The pendulum has swung a little bit too far toward the product should sell itself in some ways that’s for really positive and great reasons. It has pushed us to think about product design and user experience. A lot of it has been pushed by individuals within organizations being much more empowered to adopt technology. There are hundreds of millions or billions of dollars in truly product-led growth revenue happening every day. I’m not saying that’s not real, but it doesn’t consider how large enterprises make decisions around technology.
If you think of a few things, often, your buyer and your user are not the same person. Generally, if you’re building something that’s of strategic value and is looking for, you’re not starting small; you need to be attached to a strategic initiative in which there are multiple decision-makers, not just the person who will be using your product. Creating not just a sales motion that allows you to get in front of those people, understand their requirements and goals, navigate their organization, and transfer your excitement about your product to them. That’s a big part of what people have missed: the art, craft, and need for actual enterprise selling.
The second part is that you also need a product development process that feeds into that. Every company, but really every startup, you live and die based on your feedback loop. One interesting thing is that, as an industry, we’ve all internalized the idea that our product ideas are not that good. Focus on a problem, not your solution; ship quickly, get feedback, and iterate. That is awesome in a PLG world where the cost of getting that feedback is incredibly low. It’s challenging in the enterprise space because there is a gap between your buyer and your user. It’s often easier to get time with executives and the users who are going to implement. You’re not getting perfect information there. If you are building something entirely net new, like there is no direct equivalent to Bobsled, even your user will think they’re going to understand how they’re going to use their product. And it’s going to be somewhat wrong once you actually get into production.
The biggest part of it is that the gap between my interest in using this product, I’m evaluating using this product, and actually using it in production is unpredictable and longer. What you end up with is if you don’t build a product development process that views your sales process as a key component of feedback, you end up going back to building in a vacuum. You really need to get to a place where, one, you trust and listen to your sales team or your go-to-market team in general. Two, you’re much more actively asking your customers questions. Third, you’re much more willing to ship even more iteratively and recognize even faster that, a lot of times, what I talk about the team a lot is how we win by fixing the problems our customers identify with our product faster than they think it would even be possible.
If you take the existing agile processes we’ve all built over the past five to 10 years and try to apply them to this more challenging feedback loop, I don’t think you’ll figure out how to interpret the signals. When I say that you can’t PLG the enterprise, it’s not just that you can’t put a trial on your product and hope people come in. It’s not just if you don’t figure out how to navigate the enterprise and get to real decision-makers, budget, and stakeholders. If you build your product in a way that doesn’t take into account how feedback is generated from enterprises in your development cycle, you’re not going to build the right product.
If you’re a product like Bobsled in which larger customers experience our problem more acutely, that’s where we’re starting; you won’t build a product they can adopt. I think you’ll also have the other thing that many companies, especially from the modern data stack age, is we know we solve a real problem, but the product and the go-to-market don’t seem to quite fit where the actual money is. How do we get over that? I really think the answer is to fall in love with enterprise sales again. Really care about it.
Sabrina: Many companies that we talk to are struggling with this because PLG has become so favorable and fashionable, to say the least. But in reality, it sometimes comes back to basics. That’s what you’re alluding to here: How do you get that feedback loop going? How do you listen to your customer, especially when there’s a disconnect between the user and the buyer?
One critical component for Bobsled is being friendly with all the hyperscalers, but you partner with many of them, including Snowflake, Databricks, GCP, Azure, et cetera. How have you gone about managing these partnerships? Especially as an early-stage company, I think it can be very challenging. These companies are very large. Do you have any advice for entrepreneurs who might be navigating some of these relationships?
Jake: I’ve always been inspired by Apple’s 1984 commercial where they made clear they had an enemy, in which case was IBM, to try to motivate the team. I assumed it would be one or a few of these platforms because they were building walled gardens, and we were building a product to bring those walls down and connect these different platforms. I was shocked when none of those platforms oriented Bobsled that way. They all oriented to Bobsled for, “Oh, wow, this solves a real problem for our customers. It’s one that we don’t want to solve ourselves.” I go back to our specific problem, it involves managing infrastructure across all these different platforms, and that’s a hard thing for them to do themselves, although there are plenty of examples of them doing it.
We were pleasantly surprised. There was a warm reception from executives early on to partner. I was fortunate to be on the other side of partnership motions both at Microsoft and at Neo4j, from both the startup and the hyperscaler itself. I think they’re super dangerous for early-stage startups and that they can suck an enormous amount of your time, energy, and mental thought into them in ways that will eventually pay off, but not in the timeframe that you need them.
My advice for the early stage would be to focus on partnering with individuals at hyperscalers. So all of these companies have effective machines that move billions of dollars in revenue for partners, and almost none of those billions of dollars in revenue come from early-stage startups. You need to get to a place where you’re at scale, and then they can help you scale more effectively. At that point, you need to be able to be relevant to the individual AEs across every platform with an incredibly repeatable sales process, pricing motion, and mature integration. I look forward to that day, but it’s not series A.
The way that we’ve approached it, which has been effective, is we’re now starting to clip into the machine a bit more and start training sales teams at some of our partners like Snowflake and Databricks. The first two years have been finding executive champions willing to help us navigate and take calls with prospects and propose Bobsled, who are willing to individually send us leads. I can think of a few of our first enterprise customers where we’d be on, I was sharing a cell phone, actually my head of sales was sharing a cell phone with an executive from, in this case, Databricks. Or we were having joint meetings with executives at Snowflake and having them really reinforce the story, not just that the problem we’re solving was real, but that we are the company to help solve it.
You’re not winning by convincing 5,000 companies at once or by getting 50,000 Microsoft sellers to sell your product. You’re winning one by one. It’s like a day by day, you go out and convince these customers that your product is worth betting on. Focus on those individuals, get those wins, and that will earn you the right to scale.
Sabrina: You thought about all these different partnerships. Were there any ways that you’re prioritizing them? Or thoughts on, obviously you said it can take up a lot of time, and so if you’re an early-stage startup, how do you think about who might be the most valuable partner to you? Or how do you stack rank as you’re thinking about building? That’s a critically important part of the process for founders, so I’m curious how you thought about it.
Jake: I mean, part of that goes back to the last piece of advice that you’re partnering with individuals, not with organizations at that point. So, where you find individuals who really want to lean in and have influence in their organization, and really the specific part of their organization you want, you should be a lot more opportunistic, I think, than anything else.
For us personally, what we found, and despite my coming from Azure and having a lot of close contacts at AWS and GCP where we are partnered, was that the breadth of their portfolios made it much harder. So for us, Snowflake and Databricks, because everything that we do helps them achieve their goals. We’re in one of the few positions where it’s by working with Bobsled, a customer does two things immediately. It enables them to share their data on every one of these platforms, which directly drives consumption for every one of those platforms. So, getting data shared in is a good thing.
The other thing it does is it allows you to centralize more and more of your data in the platform of your choice because you are now able to access these other platforms, and you don’t need to worry about lock-in. So we’re in a bit of a unique position with most of these platforms where they all win when we win. It’s one of the few times where we could go to an enterprise and say, “Hey, Databricks would like to talk to you with us because they’re going to win if we win. And Snowflake would like you to talk to us because they’re going to win if we win.”
Anything that you can do to focus on where you are driving value they care about is similar to enterprise sales in the same way. If you’re trying to sell something, you must attach it to a strategic initiative that people care about. You’re not going to close your first half-million-dollar deal on a science project. Over time, it needs to tie to something. It’s the same thing with a partner. Find an individual that you attach to those strategic priorities. As much as you can, find the actual part of the business or possibly the entire company where you’re helping to drive their strategic priorities. You’ll find that they pay a lot more early dividends than trying to figure out if I could get in front of GCPs 30,000 sellers; I know that I wouldn’t have to hire my own sales team.
Sabrina: That’s great advice and I’m sure founders listening today will find that to be very helpful.
As we wrap up here, Jake, just a couple of questions left for you. One is I’m curious, what’s an unexpected challenge? Maybe one of those oh my gosh moments where something just didn’t work out the way that you thought it would, and how did you deal with it?
Jake: I think the one that’s coming to mind right now is that I believe in executive coaching, so everybody at Bobsled has a coach, and I have two. One of the challenges of being a CEO is figuring out the right timeframe for you to focus your energy on. You are responsible for the strategic vision. Especially if you’re building a VC-backed business, you can’t just be focused on, well, if we just win these customers. It needs to be a part of a larger master plan. Moments where I drove the least clarity were when my mind was focused on what it would look like for Bobsled to win two years out or even a year out and not focused on what exactly we needed to do to win right now.
I have an amazing leadership team that is often much more experienced than I am in the roles they bring. I can’t just say, “You figure out now, and you go figure out where we go next.” That’s not how this works. Make sure you’re actually defining what winning looks like for your team and talking constantly about how to win right now.
Make sure you’re still separating the space for yourself to evaluate: Are we going in the right direction? Are we building ourselves into a corner? Is there enough total addressable market here? When is the right time for me to start thinking about expanding our TAM? When is the right time to think about bringing on that next leader? But don’t get caught up in constantly building and thinking about the future. Make sure you’re focused on your actual wind condition today.
Sabrina: I love all that advice. It’s critically important to stay focused on what’s happening in the moment but also have that broader vision, knowing that the 10-year plan is potentially to get some other big wins, maybe that IPO down the road, or whatever else you’re looking forward to.
Jake: You’ve got to be able to convince yourself that there’s a path for you to get there. Otherwise, don’t take this path.
When we had a customer win, I must’ve had at least two minutes of real excitement around that before I thought, okay, we need 100 and some odd more of those to IPO. Like you just got to, you can’t celebrate the touchdowns. That’s the reality of VC. Madrona didn’t invest in Bobsled because they thought we could get to the Series B, you’re investing in Bobsled because you think we can go significantly further. It makes this decision point harder. Well, it’s like, well, I’m building for IPO, and I need to validate that. If we don’t do what we need to do to get to series B, it’s all for nothing. How do you constantly live in that time shift? It’s a mental challenge I hadn’t thought about, and I now spend a lot of time thinking about it.
Sabrina:
Well, it has been a pleasure and honor working with you, Jake, and the rest of the Bobsled team. We’re excited about what you guys are building and know that the best is yet to come. So thanks for allowing us to be part of the journey. And thanks for joining us today. Really appreciate it.