Two weeks after leaving his role as Chief Data and Analytics Officer at Regions Bank, Manav Misra came back on the show (he was first here on episode 97) to talk about the other side of the table: what it's like to be on the receiving end of data and AI software vendor pitches. Manav has been a long-time proponent of the data product management model of building, and so I imagined he had a pretty methodical way of doing buy-vs-build evaluations when vendors wanted to get their "wares" into the bank ;-)

Manav has never been shy about building internally, and one of his engineers built a competing solution on his own time during a side-by-side with a vendor. The result? Tune in to find out!

We also got into:

• What a startup has to show a buyer who is confident in their engineering capabilities internally
• Whether "the domain knowledge is baked into the product" actually mattered to Manav as a buyer
• What's still defensible for your product when Anthropic ships a skill that covers your industry (hint: Manav's answer wasn't domain expertise)
• Where POCs die inside a highly regulated buyer like a bank, and the one kind of internal ally he says a young vendor can't get through withou
• His aircraft carrier analogy for convincing internal bank leaders a startup is a safer bet than IBM, Salesforce or ServiceNow
• What happens when leadership loves a product and the end users won't touch it
• Manav's new advisory work and what he's been telling Purgo AI, a startup he now advises

If you sell a data or AI product to enterprise buyers, especially regulated ones like banks, this is a rare candid look at how one of them decides.

Purgo.ai:

Manav on LinkedIn

Full transcript and show notes: designingforanalytics.com/resources/episodes/206

Yoni Leitersdorf's company, Solid, sells a semantic layer: the plumbing that helps AI agents understand an enterprise's data well enough to do effective, valuable work that passes a customer's evals. As platform software, this is about as invisible as a product gets, and by Yoni's count there are probably 100 semantic layers a buyer can choose from right now, including the ones Snowflake and Databricks already ship. So I wanted to know how a startup like Solid can get market share in a crowded category, and how it decides which deals are worth chasing. One of his answers: the prospect who shows up saying "we know we need a semantic layer" is not the deal he's hoping for.

• What a POC has to prove before a data leader can take it through the finance/procurement gauntlet
• Why Yoni thinks startup speed stopped being a moat against Snowflake and Databricks given how fast bigger companies are now shipping features
• What he'd change about his customer discovery process if he started a third company

If your product is a horizontal platform or infrastructure your buyer can't see, I hope this episode sheds some light on the overlap between product, UX, and GTM for enterprise AI.

• GetSolid.ai
• Yoni on LinkedIn

Full transcript and show notes: designingforanalytics.com/resources/episodes/205

Logan Havern's company, Datalogz, sits on top of an enterprise's BI platforms (Power BI, Tableau, Qlik, Databricks Genie, and the SharePoint spreadsheets nobody admits to) and finds the duplicated, conflicting, and unused reports that AI is now generating faster than anyone can clean up. At one large pharma customer, executives "talking to their data" get wrong answers and then argue with the data team, because they believe Claude over the humans. The product that fixes that, which Logan calls semantic intelligence, is the one he thinks makes Datalogz a multibillion-dollar company. However, this is not what he sells first—and this is the topic we dug into today on this episode of Experiencing Data.

What Logan sells first is a 30-minute connection and a cost-savings business case finance can sign off on. That wedge took him a long time to find, and it sets him apart from other companies trying to sell “better AI answers” and “talk to your data” solutions. We also dove into the manual work [of 5-to-30-person admin teams] his product replaces, and my skepticism that usage data on a dashboard can tell you anything about whether it delivered value. If your analytics product's biggest value is also the hardest to explain, this one is for you.

• datalogz.io
• Linkedin: https://www.linkedin.com/in/loganhavern/