PODCAST · technology
Experiencing Data w/ Brian T. O’Neill
by Brian T. O’Neill from Designing for Analytics
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
Episodes
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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…
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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...
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196 - The Unique Challenges and Solutions to Selling API-based Analytics and Intelligence Products
I've been seeing a recurring pattern with companies selling APIs, MCPs, data feeds, and other developer-focused AI products. While the technology is often sound if not impressive, sales momentum sometimes slows when prospects have to imagine how the product will create value in their own...
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195 - Buyers Block: Why Your B2B Analytics or AI Product's POC Didn't Close
It’s a common pattern for teams building B2B analytics and AI products: the proof-of-concept goes well, the buyers sound excited, and everyone assumes the deal is about to close—until it quietly stalls out. The assumption is usually that sales needs to follow up harder or marketing needs more...
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194 - AI for BI: Juan Sequeda on Preparing Your Analytics to Work With LLMs
If you’re hoping that adding AI to your analytics product or capabilities is going to unlock new revenue, sales, and greater user adoption, but you’re not sure what’s involved in this transformation, this episode is for you! Today, I’m talking with Juan Sequeda today, an expert in knowledge…
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193 - Faster…or Better? Creating Value with Blue Ocean Thinking and AI-Powered Product Development
Speed is often confused with good product thinking. The idea is that if teams can ship prototypes, dashboards, and models faster, they will automatically learn faster. But execution speed alone doesn’t ensure a clearer understanding of what’s actually worth building. Instead, teams often fall…
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192 – Product Usage Does Not = Value: Why “Adoption” Metrics Are Misleading You
I’ve seen this challenge again and again with teams building analytics and AI products: nobody can define what quality to the end user means or how to measure. The answer? “Adoption.” The problem is that “amount of usage” tells you nothing useful about your customer’s experience with your product...
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191 - Turning Agents into Software that Sells [Smarter!] with Zig.ai CEO Steve Ancheta
I'm talking with Steve Ancheta, CEO of Zig, a platform designed to free sales teams from repetitive, non-revenue-generating tasks. CRM and logistical tasks can consume up to 72% of the week of a sales team, but Zig’s AI agents handle them so reps can focus on closing deals. Unlike tools built for...
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190 - Why Discovering Valuable Analytics Use Cases for Your Product Is So Hard (Even with AI)
I’ve seen this pattern repeatedly with teams building analytics and AI products: the issue usually isn’t the quality of the models or the sophistication of the data. The technology often works just fine. The real breakdown happens earlier—when teams begin with the data they already have and try…
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189 - The Invisible Intelligence Gap
I’ve worked with a lot of teams building analytics and insights products and decision-support systems. The pattern I keep seeing isn’t that the math is wrong or the ML / AI models are weak. Much of the time, the technology is fine. The challenge is that all that [not always artificial!]...
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188 - Can’t Close the Sale? Why Your Product’s UX and Workflow Misalignment Are Killing Sales (Part 2)
I’m continuing my exploration of a hard truth many leaders of analytics software companies run into: deals don’t stall because the tech is weak. Instead, they stall because prospects can’t see the value soon enough or the risk of changing the status quo is too high. This is often a product…
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187 - Can’t Close the Sale? The Invisible Reasons Prospects Aren’t Buying Your Technically Superior Analytics or AI Product (Part 1)
I’m digging into a frustrating reality many teams face: even technically superior analytics and AI products routinely lose deals—not because the KPIs or models aren’t good enough, but because buyers and users can’t clearly see how the product fits into their day-to-day work. Your demos and POCs…
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186 - Why Powerful AI & Analytics Products Feel Useless to Buyers
I’m back! After about 7 years (or more) of bi-weekly publishing, I gave myself a break (to have the flu, in part), but now it’s back to business! In 2026, I’ll be focusing the podcast more on the commercial side of data products. This means more founders, CEOs, and product leader guests at small...
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185 - Driving Healthcare Impact by Aligning Teams Around Outcomes with Bill Saltmarsh
Bill Saltmarsh joins me to discuss where a modern CDO gets the inspiration to “operate in the producty way” in his domain, which is healthcare. Now Vice President of Enterprise Data and Transformation and the Chief Data Officer at Children’s Mercy Kansas City, his early days as an analyst…
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184 - Part III: Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change
In this final part of my three-episode series on accelerating sales and adoption in B2B analytics and AI products, I unpack a growing challenge in the age of generative AI: what to do when your product automates a major chunk of a user’s workflow only to reveal an entirely new problem right…
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183 - Part II: Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change
In this second part of my three-part series (catch Part I via episode 182), I dig deeper into the key idea that sales in commercial data products can be accelerated by designing for actual user workflows—vs. going wide with a “many-purpose” AI and analytics solution that “does more,” but is...
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182 - Designing with the Flow of Work: Accelerating Sales in B2B Analytics and AI Products by Minimizing Behavior Change
Building B2B analytics and AI tools that people will actually pay for and use is hard. The reality is, your product won’t deliver ROI if no one’s using it. That’s why first principles thinking says you have to solve the usage problem first. In this episode, I’ll explain why the key to user…
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181 - Lessons Learned Designing Orion, Gravity’s AI, AI Analyst Product with CEO Lucas Thelosen (former Head of Product @ Google Data & AI Cloud)
On today's Promoted Episode of Experiencing Data, I’m talking with Lucas Thelosen, CEO of Gravity and creator of Orion, an AI analyst transforming how data teams work. Lucas was head of PS for Looker, and eventually became Head of Product for Google’s Data and AI Cloud prior to starting his own...
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180 - From Data Professional to Data Product Manager: Mindset Shifts To Make
In this episode, I’m exploring the mindset shift data professionals need to make when moving into analytics and AI data product management. From how to ask the right questions to designing for meaningful adoption, I share four key ways to think more like a product manager, and less like a...
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179 - Foundational UX principles for data and AI product managers
Content coming soon.
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178 - Designing Human-Friendly AI Tech in a World Moving Too Fast with Author and Speaker Kate O’Neill
In this episode, I sat down with tech humanist Kate O’Neill to explore how organizations can balance human-centered design in a time when everyone is racing to find ways to leverage AI in their businesses. Kate introduced her “Now–Next Continuum,” a framework that distinguishes digital...
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177 - Designing Effective Commercial AI Data Products for the Cold Chain with the CEO of Paxafe
In this episode, I talk with Ilya Preston, co-founder and CEO of PAXAFE, a logistics orchestration and decision intelligence platform for temperature-controlled supply chains (aka “cold chain”). Ilya explains how PAXAFE helps companies shipping sensitive products, like pharmaceuticals, vaccines,...
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176 - (Part 2) The MIRRR UX Framework for Designing Trustworthy Agentic AI Applications
This is part two of the framework; if you missed part one, head to episode 175 and start there so you're all caught up. In this episode of Experiencing Data, I continue my deep dive into the MIRRR UX Framework for designing trustworthy agentic AI applications. Building on Part 1’s “Monitor” and...
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175 - The MIRRR UX Framework for Designing Trustworthy Agentic AI Applications (Part 1)
In this episode of Experiencing Data, I introduce part 1 of my new MIRRR UX framework for designing trustworthy agentic AI applications—you know, the kind that might actually get used and have the opportunity to create the desired business value everyone seeks! One of the biggest challenges with...
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174 - Why AI Adoption Moves at the Speed of User Trust Irina Malkova on Lessons Learned Building Data Products at Salesforce
In this episode of Experiencing Data, I chat with Irina Malkova who is the VP of AI Engineering and VP of Data and Analytics for Tech and Product at Salesforce. Irina shares how her teams are reinventing internal analytics, combining classic product data work with cutting-edge AI engineering—and...
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173 - Pendo’s CEO on Monetizing an Analytics SAAS Product, Avoiding Dashboard Fatigue, and How AI is Changing Product Work
Todd Olson joins me to talk about making analytics worth paying for and relevant in the age of AI. The CEO of Pendo, an analytics SAAS company, Todd shares how the company evolved to support a wider audience by simplifying dashboards, removing user roadblocks, and leveraging AI to both generate…
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172 - Building AI Assistants, Not Autopilots: What Tony Zhang’s Research Shows About Automation Blindness
Today on the podcast, I interview AI researcher Tony Zhang about some of his recent findings about the effects that fully automated AI has on user decision-making. Tony shares lessons from his recent research study comparing typical recommendation AIs with a “forward-reasoning” approach that…
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171 - Who Can Succeed in a Data or AI Product Management Role?
Today, I’m responding to a listener's question about what it takes to succeed as a data or AI product manager, especially if you’re coming from roles like design/BI/data visualization, data science/engineering, or traditional software product management. This reader correctly observed that most…
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170 - Turning Data into Impactful AI Products at Experian: Lessons from North American Chief AI Officer Shri Santhnam (Promoted Episode)
Today, I'm chatting with Shri Santhanam, the EVP of Software Platforms and Chief AI Officer of Experian North America. Over the course of this promoted episode, you’re going to hear us talk about what it takes to build useful consumer and B2B AI products. Shri explains how Experian structures…
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169 - AI Product Management and UX: What’s New (If Anything) About Making Valuable LLM-Powered Products with Stuart Winter-Tear
Today, I'm chatting with Stuart Winter-Tear about AI product management. We're getting into the nitty-gritty of what it takes to build and launch LLM-powered products for the commercial market that actually produce value. Among other things in this rich conversation, Stuart surprised me with the...
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168 - 10 Challenges Internal Data Teams May Face Building Their First Revenue-Generating Data Product
Today, I am going to share some of the biggest challenges internal enterprise data leaders may face when creating their first revenue-generating data product. If your team is thinking about directly monetizing a data product and bringing a piece of software to life as something customers actually...
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167 - AI Product Management and Design: How Natalia Andreyeva and Team at Infor Nexus Create B2B Data Products that Customers Value
Today, I’m talking with Natalia Andreyeva from Infor about AI / ML product management and its application to supply chain software. Natalia is a Senior Director of Product Management for the Nexus AI / ML Solution Portfolio, and she walks us through what is new, and what is not, about designing…
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166 - Can UX Quality Metrics Increase Your Data Product's Business Value and Adoption?
Today I am going to try to answer a fundamental question: how should you actually measure user experience, especially with data products—and tie this to business value? It's easy to get lost in analytics and think we're seeing the whole picture, but I argue that this is far from the truth.…
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165 - How to Accommodate Multiple User Types and Needs in B2B Analytics and AI Products When You Lack UX Resources
A challenge I frequently hear about from subscribers to my insights mailing list is how to design B2B data products for multiple user types with differing needs. From dashboards to custom apps and commercial analytics / AI products, data product teams often struggle to create a single solution…
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164 - The Hidden UX Taxes that AI and LLM Features Impose on B2B Customers Without Your Knowledge
Are you prepared for the hidden UX taxes that AI and LLM features might be imposing on your B2B customers—without your knowledge? Are you certain that your AI product or features are truly delivering value, or are there unseen taxes that are working against your users and your product / business?...
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163 - It’s Not a Math Problem: How to Quantify the Value of Your Enterprise Data Products or Your Data Product Management Function
I keep hearing data product, data strategy, and UX teams often struggle to quantify the value of their work. Whether it’s as a team as a whole or on a specific data product initiative, the underlying problem is the same – your contribution is indirect, so it’s harder to measure. Even worse, your...
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162 - Beyond UI: Designing User Experiences for LLM and GenAI-Based Products
I’m doing things a bit differently for this episode of Experiencing Data. For the first time on the show, I’m hosting a panel discussion. I’m joined by Thomson Reuters’s Simon Landry, Sumo Logic’s Greg Nudelman, and Google’s Paz Perez to chat about how we design user experiences that improve...
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161 - Designing and Selling Enterprise AI Products [Worth Paying For]
With GenAI and LLMs comes great potential to delight and damage customer relationships—both during the sale, and in the UI/UX. However, are B2B AI product teams actually producing real outcomes, on the business side and the UX side, such that customers find these products easy to buy, trustworthy...
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160 - Leading Product Through a Merger/Acquisition: Lessons from The Predictive Index’s CPO Adam Berke
Today, I’m chatting with Adam Berke, the Chief Product Officer at The Predictive Index. For 70 years, The Predictive Index has helped customers hire the right employees, and after the merger with Charma, their products now nurture the employee/manager relationship. This is something right up…
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159 - Uncorking Customer Insights: How Data Products Revealed Hidden Gems in Liquor & Hospitality Retail
Today, I’m talking to Andy Sutton, GM of Data and AI at Endeavour Group, Australia's largest liquor and hospitality company. In this episode, Andy—who is also a member of the Data Product Leadership Community (DPLC)—shares his journey from traditional, functional analytics to a product-led…
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158 - From Resistance to Reliance: Designing Data Products for Non-Believers with Anna Jacobson of Operator Collective
After getting started in construction management, Anna Jacobson traded in the hard hat for the world of data products and operations at a VC company. Anna, who has a structural engineering undergrad and a masters in data science, is also a Founding Member of the Data Product Leadership Community...
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157 - How this materials science SAAS company brings PM+UX+data science together to help materials scientists accelerate R&D
R&D for materials-based products can be expensive, because improving a product’s materials takes a lot of experimentation that historically has been slow to execute. In traditional labs, you might change one variable, re-run your experiment, and see if the data shows improvements in your desired...
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156-The Challenges of Bringing UX Design and Data Science Together to Make Successful Pharma Data Products with Jeremy Forman
Jeremy Forman joins us to open up about the hurdles– and successes that come with building data products for pharmaceutical companies. Although he’s new to Pfizer, Jeremy has years of experience leading data teams at organizations like Seagen and the Bill and Melinda Gates Foundation. He…
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ABOUT THIS 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
HOSTED BY
Brian T. O’Neill from Designing for Analytics
CATEGORIES
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Frequently Asked Questions
How many episodes does Experiencing Data w/ Brian T. O’Neill have?
Experiencing Data w/ Brian T. O’Neill currently has 205 episodes available on PodParley. New episodes are automatically indexed when they're published to the podcast feed.
What is Experiencing Data w/ Brian T. O’Neill about?
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...
How often does Experiencing Data w/ Brian T. O’Neill release new episodes?
Experiencing Data w/ Brian T. O’Neill publishes regularly with 205 episodes available. Check the episode list for the typical release schedule.
Where can I listen to Experiencing Data w/ Brian T. O’Neill?
You can listen to Experiencing Data w/ Brian T. O’Neill on PodParley by clicking any episode. We provide an embedded audio player for direct listening, and you can also subscribe via your preferred podcast app using the RSS feed.
Who hosts Experiencing Data w/ Brian T. O’Neill?
Experiencing Data w/ Brian T. O’Neill is created and hosted by Brian T. O’Neill from Designing for Analytics.
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