Business Breakdowns
· Alan Tu
[00:00:00] S1: Going back to the starting points of Databricks and Snowflake, Snowflake was really the next-gen cloud version of the data warehouse, which was a market that did exist. In the case of Databricks, it would have been that data lake market, but that had never been as well established for basically lack of good enough technology. There had been a lot of attempts at creating data lake companies. There was a technology that predated Spark called Hadoop, and there were companies that were built on top of Hadoop like Cloudera that went public at one point, but the problem there was just that, simplistically, the technology wasn't good enough. So, to your question of TAM, I think the market technically existed, but there was this period, people forget, in the early 2010s when "big data" was a very sexy topic. And there was a period where companies had the recognition, or at least the inkling, that data was valuable. There was a whole period of a number of years where companies were storing a lot of data, volumes and volumes of data, with this underlying view that, "We should be doing that because big data, why not?" But the reality was that there was, in Gartner's term, a very hard "trough of disillusionment" where it was, "Okay, now we've stored all this data, what are we going to do with it?" And turns out, it's very difficult to get anything out of it. And so, that's what again, going back to Databricks' core value proposition, it was really solving that problem. And that was massively TAM expansionary.
· Matt Reustle, Alan Tu
[00:00:00] S0: Yeah, that makes a lot of sense. And "data being the new oil," I think there's certainly some truth to it, but there's also this massive challenge of understanding that, "This probably has value, but how do we unlock that value and do something with it?" [00:00:00] S1: Exactly. [00:00:00] S0: And that being a problem they solved is quite interesting. I want to get a little granular just in terms of a use case so I truly understand what is going on, to the extent that you can answer this. One of the examples that I saw presented is: I make a credit card transaction. All of this data is flowing through the pipes of my credit card company, maybe my bank. Databricks is involved in that. And if they see I make a transaction with a non-traditional vendor, it's for a very large amount, and it's in a country that I never make transactions with before, I can get a fraud alert. And from my understanding, is that flows through the Databricks pipes to some extent, just in terms of managing all the variables at play that would cause a fraud detection. In that example, how does it work with Databricks actually having the ability to make the decision on the behalf of the credit card company to send me that fraud alert versus them presenting this alert back to the company and coming back to me? It paints an interesting picture of how ingrained they are in terms of with their customers. And I know it's going to differ by use case, but can you talk a little bit about that?
· Alan Tu
[00:00:00] S1: Sure. For any given credit card company, the implementation might be different than another. But to bring your use case to life, it is very accurate to think about that core value proposition that Databricks provides, which is that you can imagine the amount of data that goes into making a decision of whether or not a transaction is worthy of a fraud alert. There's a tremendous amount of input that can go into that. And there's probably never enough; you could always add more data to that analysis. And that hits on the core thing that Databricks provides, which is the pipelines to bring in all that data, process that data because all the different types of data are going to be in different formats, and feed that into a machine learning model that gets fine-tuned by the data scientists and tweaked and will constantly be updated based off of the facts on the ground and the empirical data of also evaluating these models: are they actually accurate after the fact? And then tweaking those models again. All of that is core Databricks value proposition. Now, once Databricks helps a company come to that decision, who is actually sending the fraud alert. Again, there can be different architectures here, but typically, companies will build another application that actually takes the action, and the model output from Databricks will inform that action, is maybe a classic way to think about the architecture.
· Matt Reustle
[00:00:00] S0: That makes sense. The application that sits atop it's ultimately informed by the Databricks models that are analyzing all those things. And I can imagine it being rules-based: if a transaction meets these criteria and it gets sent up, then that triggers the fraud alert warning. Where I was going with that is, I'm trying to grasp again, with you'll have companies that are using multiple different vendors, obviously, there's so many different use cases. The one I just brought up is a cost savings. We've already talked about revenue growth and how this can be used. But in terms of ways to measure stickiness and ramping with customers, what does that look like? It just assumes the more you would use the model, the more ingrained it would be in your business, therefore less likely to churn.
· Alan Tu
[00:00:00] S1: Totally. They've disclosed that their net dollar expansion rates are greater than 140%. So, there is embedded in that a high level of stickiness and also embedded growth. So, quantitatively, that's the numbers that they've disclosed. But I think qualitatively, the right way to think about it is that many of these use cases that we've discussed are very core to the fundamental product that businesses are selling. Sometimes in the world of data analytics, you can just envision a data scientist or data analyst in the back office running an analysis for the strategy team that may or may not feel sticky. But when you think about these use cases where this is a content streamer that is suggesting the next movie you should watch after you finish a movie, that is core to the product, and that can oftentimes be revenue generating and very mission critical. So, there is that level of stickiness in terms of once you get embedded into use cases in production. And then there is also the added layer of stickiness around just the fact that there's a concept of data gravity. And once you put in the work to store and catalog data within a data platform, that becomes very sticky. So, there's a lot of different dimensions with which Databricks becomes very embedded within a company. I think the last thing I would mention is, if you've done the work to process data once, you could potentially use that for multiple use cases. You can then again imagine how that becomes very hard for, even if a certain product at sunset, but there's another product that's still leveraging the same data, that's very sticky as well.
· Matt Reustle
[00:00:00] S0: You've made the allusion, and it's the elephant in every room now, of AI. I guess I'll just start with the very highest of level of what are the impacts of AI on a business like Databricks, to the extent that they're beneficiaries, to the extent that there's risks associated with it. I'll let you wax poetic.