Experiencing Data w/ Brian T. O’Neill

Hosted by Brian T. O’Neill from Designing for Analytics

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206

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Oct 2026

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About the show

Your B2B analytics product does the hard technical work. So why aren't sales and adoption keeping up? I'm Brian T. O'Neill, and my podcast, Experiencing Data, is a Listen Notes top 2% global podcast for founders, CEOs, and product leaders at B2B analytics companies: the ones whose product should be winning deals on technical sophistication alone, but isn't. Maybe competitors have caught up on features and prospects say you all look the same, so you end up competing on price. Maybe customers never discover your best features and use you "like Excel." Maybe your value is algorithmic, and buyers can't see what they'd be paying for—something I call the Invisible Intelligence Gap . Through solo episodes and interviews with CEOs, founders, and the enterprise data leaders who buy these products, I draw on my work as a product designer and strategy consultant to help you translate technical complexity into commercial clarity: connecting what your data product does to the outcomes, value, and human factors that still matter, even in the age of AI. Experiencing Data is the only non-technical show about why technically impressive products don't sell or get used, and what to do about it. Subscribe today on all major platforms or browse the episode archive. Get 1-Page Episode Summaries In your Inbox: https://designingforanalytics.com/ed About Brian: https://designingforanalytics.com/bio/

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60 recent

November 4, 2026Episode 20637 min

206 - Regions Bank’s Former CDAO On Buy vs. Build and What It Takes to Get Your Analytics or AI Product Into a Bank

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. Links Purgo.ai : Manav on LinkedIn Full transcript and show notes: designingforanalytics.com/resources/episodes/206

October 20, 2026Episode 20545 min

205 - Everyone Says They’re the Missing Layer in Enterprise AI, So I Asked Solid’s CEO How He Proves That in a POC

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. Links GetSolid.ai Yoni on LinkedIn Full transcript and show notes: designingforanalytics.com/resources/episodes/205

October 6, 2026Episode 20443 min

204 - Showing ROI in 30 Minutes, Not Your AI: How Datalogz’s CEO Built a Sticky Enterprise Analytics Product

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/

September 17, 2026Episode 20340 min

203 - Engineering Is 6x Faster. Sales Isn't. Here's What Your Team Never Defined.

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

September 2, 2026Episode 20231 min

202 - Surface Tension: The 5 Essential Control Surfaces to Make Your Analytics Product Sell Itself

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

August 18, 2026Episode 20129 min

201 - What Enterprise Buyers Really Want from Analytics Software Companies with David Krauza

Recently, I met David Krauza, VP of Enterprise Data Strategy and Products & Governance at Comcast, at the 2026 CDOIQ symposium, and after chatting for a bit, he agreed to come on the show to talk about how he, as an enterprise buyer, thinks about B2B software purchases in the age of AI. As vibe coding makes internal development more accessible, David explains why the buy-versus-build decision isn’t simply about whether a company can build a solution itself. Leaders need to weigh long-term roadmaps, maintenance, integrations, and whether they want to take on the responsibility of becoming a software company. The real question is not just what can be built, but what makes the most strategic sense to own. David also highlights an important consideration vendors frequently overlook: the data their own products create and the possible importance of that to an enterprise data leader. This is particularly true when the product’s primary intended purpose is not analytics itself. In a complex sales environment, which may include a senior data leader as a champion or decision maker, product metadata, or what you might currently be thinking of as a byproduct, might actually be a primary decision point in an enterprise purchasing conversation. David then outlines the pre-implementation work required before any of this can be evaluated: establishing shared definitions, explicit success metrics, and a documented “before-picture” you can run at renewal time to understand the ROI of the product. We also explored the hidden costs that can undermine an otherwise compelling product. A polished UI may still create UX friction if users have to constantly move between systems, while complex data integrations can introduce additional labor and operational burdens. This friction should be considered during the evaluation rather than discovered after development. David says vendors can stand out by demonstrating that they understand where a customer’s business is headed and how their roadmap supports that direction. He also dropped some real gold about the difference he sees as a buyer when being pitched by a founder vs. a B2B salesperson—and what the latter is missing when they pitch him. Highlights / Skip to: Why buy any products when AI allows you to build them yourself? (2:03) Allowing use cases and goals to dictate the adoption of internal solutions (4:03) Making data capture, accessibility, and ecosystem fit part of the buying decision (5:54) Is data missing in sales conversations due to a lack of marketing or of results? (8:36) Comcast’s method for deciding to renew when the intelligence is invisible (11:22) The importance of pre-post analysis when making a renewal argument (14:02) Where David sees the most time being wasted in the pitch process and why vendors who did _____ win more often (16:36) The hidden costs that often go undiscussed during the sales process (20:07) How Comcast evaluates UX friction (back-and-forth of switching between applications to accomplish work) (22:22) Other hidden factors that can prevent your sale from closing (25:13) Differences David sees between founder-led sales and sales-led sales (26:41) David’s advice for founders selling in the analytics and data space right now (28:14) Links David Krauza’s LinkedIn David Krauza’s Substack

August 5, 2026Episode 20047 min

200 - VC Lessons on GTM, Product, and Moats for Data and Security Startups with Nishkam Prabodh

I'm talking to Nishkam Prabodh of Venture Guides, an early-stage venture capital firm focused on infrastructure software, cybersecurity, and data. Nishkam explains why strong technology alone rarely determines whether a startup succeeds. Many technical founders struggle when transitioning from founder-led sales to a repeatable go-to-market motion because the founder's deep customer understanding often does not translate into a scalable sales process. The challenge isn't just building better technology, but connecting technical capabilities to clear business outcomes. Nishkam breaks down the communication gap that often appears between technical products and enterprise buyers. Rather than focusing only on technical outputs, products need to demonstrate business value through experiences designed for different stakeholders, including end users, executives, compliance teams, and budget owners. Whatever ROI the product shows has to relate back to improving revenue, reducing cost, or reducing risk. Nishkam also discusses how AI is changing product organizations, with execution-focused tasks becoming increasingly automated while judgment, domain expertise, and strategic decision-making become more valuable. On buy versus build, he notes that two-thirds of the failures in the MIT study on enterprise AI deployments were internal builds, while the successes skewed toward buying. While traditional advantages like proprietary technology and architecture still matter, he believes the strongest defensibility increasingly comes after deployment. Products that learn customer context, retain institutional knowledge, improve workflows, and generate organization-specific intelligence can create compounding value over time. AI models may become commoditized, but the surrounding product layer of memory, retrieval, context, tooling, and governance will determine long-term differentiation. He closes with the discipline that makes all of this possible: pick one customer, one industry, one revenue band, and build repeatability there before worrying about coverage. Highlights / Skip to: The pattern Nishkam Prabodh sees in B2B and data companies that stall at founder-led sales handoff (4:46) Challenges in translating technical complexity into commercial clarity (7:58) The two fall-off points in a deal, and why the cheaper one is the bigger one (12:19) The importance of communicating value to the executive buyer, not just the user, as early as you can (13:24) Why a stalled deal might be a product design problem, not a sales problem (17:22) The only three things ROI is allowed to reduce to: revenue, cost, risk (19:41) What skill sets Nishkam thinks are essential for product teams (20:11) The three shifts in product hiring: later, more judgment, domain over generalist (25:37) The importance of judgment in preventing unmet needs from derailing sales (26:32) What Nishkam believes are strong moats for data products (31:13) Why a buyer's failed in-house build still costs you sales cycle and ACV (33:37) Showing the value of a product on day thirty, not just day one (36:45) Why the buyer never sees the work that makes the simplicity possible (41:20) Nishkam Prabodh’s closing advice for technical founders of analytics and data products (44:46) Links Nishkam Prabodh’s LinkedIn Venture Guides website

July 21, 2026Episode 19950 min

199 - Why Your Analytical AI Product Might Need 2 Pitches, Not 1, with Bhaskar Sunkara, CEO (bicycle.ai)

I’m talking to Bhaskar Sunkara, CEO of bicycle.AI, which provides an AI analyst product designed to monitor revenue-critical KPIs, investigate the business and technical drivers behind KPI changes, and take a “governed next step.” Bhaskar explains why analytics products often fail when they overwhelm users with telemetry instead of focusing on the signals that matter. Drawing from his experience as founding CTO of AppDynamics, he shares how his team moved from low-level technical monitoring to business transactions like logins, checkouts, and bookings. The key lesson? Start with the right metric at the right level of granularity, then use deeper technical analysis to explain why something changed. Bhaskar also breaks down how bicycle.AI serves multiple audiences inside an enterprise. Business leaders want measurable outcomes, KPI owners need answers about what changed and what to do next, and data teams require trust, governance, and traceability. He explains how, in order to support these different users, Bicycle separates product experience into four core surfaces: pull features like dashboards and chat, and push features like alerts and data stories. Alerts further help operational users respond quickly to KPI changes and data stories provide executives with strategic narratives around trends, causes, and business impact. During our chat, Bhaskar also draws a line most AI products blur: be explicit about which findings are deterministic and which are only a theory. He connects this directly to my CED framework , separating the conclusion from the evidence from the underlying data, and argues that how much you automate should be governed by one question: how costly is being wrong? I also probed Bhaskar about their moat. He’s learned that enterprise adoption requires winning over both executives who care about revenue impact and analytics teams that need confidence in the system’s recommendations. Bhaskar also explains why their long-term advantage comes from the DEAL framework: Detect, Explain, Act, and Learn. By continuously incorporating validated decisions, business context, and customer-specific knowledge, the platform becomes more useful over time. We finish up with his advice for fellow analytical AI product founders, including why AI makes user experience more important, not less: it is the connection between agents, decisions, humans, and accountability. Highlights / Skip to: Making the invisible feel urgent enough for customers to buy products (2:41) How to avoid creating the ‘metrics toilet’ when the system can do so much (6:56) Designing for the end-user versus the buyer, especially during the POC phase (12:20) Thinking about the product’s design in a way that ensures Bicycle’s business value is obvious (15:38) How bicycle.AI’s “push” and “pull” features help stakeholders see value (20:54) Getting their first 20 customers (25:19) What Bhaskar got wrong: over-rotating on the business buyer vs. the analytics team (32:23) The homework a build-anything horizontal platform imposes on customers (and Bicycle’s vertical antidote) (34:30) Bicycle.AI’s moat: compounding institutional knowledge (36:08) DEAL: Detect, Explain, Act, and Learn (40:46) How they designed the UX to reduce time-to-value during onboarding/setup (44:51) Bhaskar Sunkara’s advice for other analytical AI founders (and why AI makes UX even more important to address) (47:47) Links bicycle.ai Bhaskar Sunkara’s LinkedIn My CED framework for advanced analytics products that Bhaskar references in this episode

July 7, 2026Episode 19849 min

198 - Ship the Meter: Making Invisible AI Legible to Buyers with Rana Gujral

Today, I'm talking to Rana Gujral, CEO of Behavioral Signals, which provides AI that interprets human behavioral cues in speech to help route call center conversations more effectively, improve customer service performance, and detect voice-based fraud. Their moat is a decade of voice data tied to real business outcomes, not the model itself, as Rana explains. During our conversation, Rana shares his practical framework for making the value of their AI obvious to the various humans in the loop that the product needs to “touch,” and he argues that a one size [UI] doesn’t fit all. In Rana’s product, they discovered that customer service reps need ambient assistance, supervisors need aggregate patterns, compliance teams need audit trails, and executives need outcome metrics tied to business results. He also explains why having measurable ROI isn't enough. Early renewals for Behavioral Signals suffered because the people signing the checks couldn't actually see the product's impact. Rana's solution? “Ship the meter” alongside the intelligence. If your AI works quietly in the background, you still need reporting UIs that clearly communicate the product’s value. For founders struggling with stalled POCs, Rana breaks down the three-stage evaluation journey his team developed after repeatedly seeing deals fail at predictable moments. By designing the customer experience around those milestones, his team transformed how buyers gained confidence throughout the evaluation process. Finally, we explored why great B2B AI products don't succeed by becoming another dashboard. Rather, they succeed by closing the loop between decisions, outcomes, and learning. Rana also fills me in on his upcoming book, The AI Instinct, which focuses on how AI changes human judgment rather than simply advancing model capabilities. And his parting advice? Listen to find out! Highlights / Skip to: Making “invisible AI” value clear (3:57) The four surfaces of visibility the product team dials into to ensure Behavioral Signals is indispensable to customers(6:26) Behavioral Signals’ intentionality behind their three-phase model to address deals not closing (15:35) How Rana’s team deals with AI moving downstream problems further upstream (19:56) Determining their product’s boundaries: when do you stop building? (22:55) Why proprietary data makes for such a good moat (24:57) What Rana would do the same and differently if he were starting over (28:45) Rana’s book: The AI Instinct: The Future of AI and Human Decision-Making (39:29) Rana Gujral’s closing advice (44:35) Links Behavioral Signals The AI Instinct: The Future of AI and Human Decision-Making Rana Gujral’s website Rana Gujral’s LinkedIn

June 24, 2026Episode 19731 min

197 - Agentic AI Isn’t a Moat for Analytics Products. This is

Everyone is racing to the same place chasing a limited set of buyers—how will your “AI for BI” product stand out? I've been seeing teams heavily invest in copilots, agents, semantic layers, governance frameworks, and increasingly sophisticated models, yet many still hear the same feedback from sales prospects: “We may just build this ourselves?" Or they don’t hear it, but suspect the customer is doing just that. Whether they actually can DIY the solution is the wrong question. The bigger question is *why they believe they can.* Your product may have a genuine competitive advantage, but your real challenge is that this advantage isn't obvious to buyers. The moat exists, but it is invisible. What makes this relevant is that many capabilities once considered differentiators are rapidly becoming normalized. AI copilots, agentic analytics, governed data, semantic layers, and broad integrations now appear across nearly every platform in the category. As AI accelerates development, sophisticated engineering alone becomes harder to defend as a lasting advantage. So what actually creates a durable moat if the engineering and product seems easy to copy? I explore four areas: proprietary data, trusted relationships, and products that accumulate institutional knowledge remain difficult to replicate. And finally, user experience itself as a strategy. As users increasingly access your intelligence through AI agents rather than dashboards, their experience may become the moat that competitors can't copy. Highlights / Skip to: AI for BI and analytics products is facing a race to commoditization (2:09) Common moats that everyone is using right now and why they fail (3:28) Proprietary data as a moat (9:29) Being embedded in your community as a moat (11:14) Compounding institutional knowledge as a moat (15:22) UX design asa moat even when there is little/no UI to see (18:36) Find the baseline for customer experience to build into later strategies (25:11) Actionable questions to ask your team to move forward on finding your competitive differentiation as a B2B analytics product (28:02) Links CED: A UX Framework for Designing Analytics Tools That Drive Decision Making