Data Management for Enterprise LLMs

**George Fraser** (0:00)
If you look at the work people actually do in data prep, it's mostly going out and gathering context from the business. You have to walk around and talk to people and find out, hey, what does this field mean? There are two fields in Salesforce, and sometimes one is populated and sometimes the other. Why is that? I suspect a solution to that would actually entail an LLM agent that goes around asking people questions in order to clarify things and simplify the data that they're looking at. That's fundamentally what you're doing when you're doing data prep, is you're trying to create a simplified view of the world that obscures these idiosyncrasies that are in the original data set, that are always highly company-specific, and that are not at all self-explanatory. You can't just look at the data and figure it out. It's not actually a data prep problem. You have to go change the organization.

**Derrick Harris** (0:52)
Hi, and thanks for listening to the a16z AI Podcast. I'm Derek Harris, and I'm joined this week by Fivetran founder and CEO, George Fraser, as well as a16z partner, Guido Oppenseller, for a discussion about data architecture and data management in the age of LLMs. If you're an enterprise organization thinking about how to integrate language models into your existing environment, the good news is that, in George's view, you probably don't need to change much, at least not when it comes to your underlying data management layer. However, he notes, challenges do away when it comes to business-level concerns, such as remaking workflows and data hygiene to account for the strong suits of LLMs, and in determining just how much data analysis access is too much. As he notes, whereas good old-fashioned dashboards might get a bad name in some circles, they do theoretically make clear what matters to the business, but 10,000 employees prompting language models can construct 10,000 different cases to support their own ideas. All that plus everything from the origin of SQL to the data engineering skills of the future after these disclosures. As a reminder, please note that the content here is for conformational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a16z fund. For more details, please see a16z.com/disclosures.
So to start, George, can you just explain from the 10,000 foot view how you've seen generative AI change things in your world over the past couple of years in terms of what customers want to do, the types of projects they're pursuing. Just overall, if you went from 2021 to like 2022, 2023, and now to 2025, how that's looked?

**George Fraser** (2:34)
At Fivetran, we move data on behalf of 7,000 or so customers, depending on where you set the threshold for customer. And the problem we're solving for people is the same for the last 12 years, getting all your data in one place. People do a lot of things with that data, probably the single most common thing they do with that data is sales reporting. AI workloads are a new thing people do with the data. And the really exciting thing is that we can finally do something with text data. Fivetran has been delivering lots of text data since the very first connector that I wrote in 2015 and Salesforce. We've been syncing notes since then. In the past, there's not a lot you can do with unstructured text. Now, unstructured text is machine readable, and that's a really profound evolution. It's a profound evolution for a lot of things, but one way it's a profound evolution is for businesses trying to make use of the data they have. They can actually do something with unstructured text data. And, for example, at Fivetran, we have an internal knowledge-based search bot. Because as a Fivetran sales engineer or customer service rep, you have to be familiar with this huge array of systems that we connect to, because we're data movers. We have all these sources and destinations we talk to. There's more detail than any human being can actually be familiar with. And so we have this tool that indexes all of the internal documentation and past support tickets and Slack conversations and things of that nature. And it's like the big brain that you can ask any question of, how the hell do I configure an Oracle database, this version, this whatever, in order to connect Fivetran to it? And it's a super useful tool. It's powered by the exact same data warehouse, the exact same tables as everything else, as all the rest of our analytics stack.