Experiencing Data w/ Brian T. O’Neill

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.

LinksPurgo.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.

We also got into: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.

LinksGetSolid.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.

Links

datalogz.io

Linkedin: https://www.linkedin.com/in/loganhavern/

What happens when your product team suddenly moves six times faster at delivering features, but your sales and business traction is unchanged? AI, and particularly AI for coding, has made it easier than ever to ship features, but faster development doesn’t automatically create better products or better outcomes. The real question is whether what you’re building changes anything for the people who buy, use, and benefit from your product. 

 

 

Today, I get into why AI evals and acceptance criteria measure the product, not its impact, and why your buyers are probably judging your POC on success metrics your team never wrote down. Toward the end, I make the concepts concrete by applying them to a fictional AI-powered pricing tool to show what defining quality looks like for both the fiscal buyer and the end user, and what to do with all that extra engineering velocity instead.

 

 

Your goal? Better; not just faster. 

 

 

Links

Episode 192: Analytics Product Usage Does Not Equal Value

Episode 202: Surface Tension

What makes an analytics or intelligence product indispensable when technical sophistication alone isn’t enough to drive adoption, renewals, or sales? Why do POCs stall, why does adoption stay flat, and why aren’t prospects nearly as excited about your (impressive) analytics tech as you are? 

 

 

It’s that the value never becomes obvious to the humans in the loop who do the buying, using, and justifying.

 

 

In this episode, I provide a framework for identifying the personas (roles) and experiences that can turn sophisticated analytical capability into perceived value. We’ll explore the five essential control surfaces you’ll need to address in your product and why founders and product leaders need to look beyond technical development (e.g., the delivery of AI models, dashboards, MCP connectors, agentic capabilities, and traditional interfaces). 

 

 

I also examine the tensions that can emerge when incentives and goals for end users are not aligned with those of management and executive/fiscal buyers. Why does the efficiency ROI you sell to a management champion get read as a job threat to the operator/user whose adoption you need to succeed? I’ll answer this, plus the reasons AI can complicate those dynamics, how to prevent your product from creating an Invisible Intelligence Gap for customers, and why one past podcast guest’s “shipping the meter” matters much more than another feature. (That will also explain why I don’t think dashboards are dead in the age of AI.) 

 

 

Highlights / Skip to:

Short-term stakeholders you shouldn’t forget to satisfy (particularly during POCs/Demos) such as legal, compliance, and the game of defense “value be damned” they are playing (4:14)

Not every mouth is equally hungry: deciding which personas deserve the most product attention (8:23)

End users of your product such as developers, analysts, business users (often ICs) operating your product daily—and why the real bar is whether they would complain to the C-suite if your product went away (9:12)

How managers of ICs (e.g., sales directors, customer service managers, VPs of data, etc.) might need something very different than end users, and why the manager’s needs might be a threat to those very IC users. The 3-part fix? Making all parties look successful to their superiors; improving the lives of those stakeholders; and arming champions with the evidence fiscal buyers need  (11:08)

The unique needs of executive and financial stakeholders who might have the most decision power while using your product the least (15:59)

Why “shipping the meter” and the Invisible Intelligence Gap decide whether your product survives the next budget cut (17:07)

The “AI Agent” as a “user” of your product: Why I think agents ride in a sidecar attached to humans and what you need to do to enable your customers and their AI agents to be successful (21:23)

Why figuring out what control surfaces/UIs/UXs you may need to improve is the easy part if you know what they are a response to (26:37)

 

Links

197 - Agentic AI Isn't a Moat for Analytics Products.This is. - My episode on viable moats for BI/Analytics products in the age of AI

The Invisible Intelligence Gap

Behavioral Signals CEO Rana Gujral on “Shipping the Meter” is in Episode 198

Need help identifying the 5 control surfaces in your product and satisfying the human users behind them? Schedule a free discovery call with me