Most organizations have had thousands of these dashboards and reports proliferate across the organization and then they need to be customized. It just takes a long time for users inside of the business to actually get access to the data. AI now is really making that a lot easier from just the analytics perspective where we can now democratize access to the data, which has really been the holy grail for most data teams. They really want to get out of the way and just give the right data to the right people inside of the business with the right access.
With a product like Genie at Databricks, you can just use English language or whatever your language is to ask questions of the data. And it’ll give you back data that answers your questions in context. It’ll give you not just what ChatGPT will give you, which is information about a topic that’s on the internet, but it will actually tell you, “Well, why did my sales numbers not reflect what I expected in the month of April?”
It’ll give you some root cause analysis based on your enterprise data. Genie is going to be one of these things that’s really important where it’s going to truly kind of democratize data inside of the business. That’s kind of this OLAP world, which is what the Lakehouse is. More recently, we’ve come to market with what we call the Lakebase, which is the OLTP world. What we’re finding is that agents are now being deployed in these organizations, and those agents need a place to keep all of their orchestration, all of the context of what’s happening in that particular workflow. On the one hand, you’ve got users just asking questions. On the other hand, the next chapter is going to be around automating an entire business process. If you’re taking a function like generating a campaign in marketing, right? There are a lot of tools you use and a lot of steps you use.
An agent can come in and really automate a lot of that. But on the back end of that agent, you’re going to need to stand up a real-time database to keep track of all the things that the agent is doing. That’s what Databricks has brought to market, which is this OLTP Lakebase solution. The innovation that we have brought to market is that it’s a modern kind of Postgres database where we have separated the compute and storage, very much like what we did with the data Lakehouse with the data warehouse. But on the Lakebase, the data is on one copy inside of your cloud storage, and then the compute is separated and it’s serverless. You can do things like branching and you can start up the OLTP database really quickly. What we found is that agents are actually starting these Lakebases because they can very quickly go start one up, keep it running, put it down when it needs to, make a copy of it.
Agents are doing this, then they need the velocity, they need a cost-effective solution. And the beauty of all this is when you take the OLTP, which is all around the Lakebase and the real time, and you take the OLAP, you now have one system for all your data. You don’t have to copy the data around, you don’t have to manage all the permissions, you can set the context against it. We see these AI apps being really the future of how businesses run, where they’re going to take away all of the bottlenecks that humans are having to do repetitive work and automate these using LLMs and all these new technologies. We want to be the default for powering all that because we believe that our Lakebase technology is going to be faster, cheaper, and more secure for an AI database.
Megan: Sounds like a real game changer. And we’ve touched on this a couple of times already, I mean, this idea of value. We know that engaging the commercial value of investments into AI is really high on the priorities right now for senior leaders. How important is this value measure piece when it comes to creating AI-ready data systems, Rajan? How can organizations ensure they’re monitoring what is delivering and what isn’t?
Rajan: This is the paramount importance and most of the successful AI implementations or agentic AI implementations really required this value measurement. I’ll just extend the client example that I talked about, the large food products company, the global products company, to explain this question. I just want to create a metaphor. When the initial digital world came, we have a lot of these analytics primarily around defining those performance management KPIs, fact-based decisioning and other things were evolving over a period of time. Typically, a lot of these metrics are going to be very critical for them to measure how a function, how a business is doing. On a similar line for the value measurement, if I take the same example of the client, what is very critical for an organization is actually to map your outcome that you are expecting.
Iin this case, how do I optimize my spend on direct and indirect purchases? So by applying AI, I would like to identify the areas where I can optimize the spend. That means one of the critical measures that you have is, what is your indirect expense classification and what spends you have been classified and how much you are able to reduce by bringing in this. Establishing these measures and the metrics is going to be very, very critical. And once you establish these base metrics and the measurement, and the beauty of it is some of these metrics, to just extend what Bavesh was talking about, the capabilities that Databricks gives you, like metrics view, features, tools, and other things would actually help you to translate those AI telemetries, business telemetries that is coming from your applications into a measurable metrics in terms of an outcome, which you can actually measure using the Genie room for value management measurement.
