Data Faces Podcast

Data Faces is a data, analytics, AI, and marketing podcast that brings the human stories behind the numbers to the forefront. Hosted by David Sweenor — author and founder of TinyTechGuides — each episode features engaging conversations with the industry's leading voices: the data leaders, analytics practitioners, AI innovators, and marketing leaders shaping the future of data-driven decision-making. We explore the culture, challenges, and real-life experiences of the people behind the numbers — enterprise AI adoption, data governance, generative AI, analytics, and B2B marketing.

Data Faces Podcast is a business podcast hosted by TinyTechMedia, with 45 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

TinyTechMedia hosts Data Faces Podcast, a business show with 45 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #46

  Run Your Data Platform Like a Product | Amin Venjara, ADP

  Aug 18, 202618 minS4

  Amin Venjara, chief data and product officer at ADP, joins Data Faces on location at the 20th annual CDOIQ Symposium in Cambridge, Massachusetts. He explains why a contractor going on about wood, nails, and concrete will never win over a homeowner who just wants to entertain in the backyard, and why data teams make the same mistake when they pitch the business. Amin walks through the framework from his session, value equals data plus capabilities, and how ADP treats its internal data platform like a product that builders across the company choose to use. He describes the annual Data and AI Day that drew 2,100 people, the hackathon that feeds it, and the metrics his team tracks to prove the platform is creating value. He closes with a concrete example of a data product that gives chat applications full customer context, so every team stops rebuilding the same stitching work. What you will learn: 1. Why data alone does not create value, and what capabilities like semantic layers and entity resolution add2. How treating the data platform as a product changes the relationship with internal customers3. How a hackathon and a 2,100-person Data and AI Day make foundational data work visible to executives4. What a real data product looks like, using normalized customer context to power chat and agent experiences Chapters: 0:00 Welcome from CDOIQ in Cambridge 0:24 Icebreaker: baseball on the radio and a love of math 2:09 Amin's role and what ADP does 3:43 Wood, nails, and the deck: value equals data plus capabilities 7:10 Treating the data platform like a product 9:13 Inside the hackathon and Data and AI Day 13:16 The metrics that prove the platform creates value 14:43 A data product in action: customer context for chat 17:25 Sign-off Watch more Data Faces on location: https://tinytechguides.com/data-faces-podcast/?utm_source=youtube&utm_medium=video&utm_campaign=cdoiq2026-amin-venjara&utm_content=descriptionConnect with Amin Venjara on LinkedIn: https://www.linkedin.com/in/venjara/ADP: https://www.adp.com #DataFacesPodcast #CDOIQ #DataProducts #DataStrategy

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  Episode #45

  SaaS Is Dead? Absolute BS | April Dunford

  Aug 11, 202638 minS4

  "Any software feature can be cloned with a prompt, overnight." A room full of data and AI leaders agreed on it. April Dunford's verdict: absolute BS. On this episode of the Data Faces podcast, David Sweenor talks with April Dunford, the positioning consultant behind Obviously Awesome and Sales Pitch who has worked with more than 300 B2B technology companies, about why the death-of-SaaS story falls apart inside real software companies: why "we have AI" is the new login screen, the CRM whose data model no competitor could copy, why even IBM couldn't spare three developers for one feature, and the real reason positioning projects fail. Key takeaways: - "We have AI" is a statement of parity, table stakes like a login screen- Real differentiation lives in a product's architecture, data model, and founding philosophy- Cloning an enterprise product means rebuilding your own and migrating the install base, so competitors won't and can't- Business cases kill copycat features before any code gets written- Positioning fails when marketing works alone; sales knows the shortlist and product knows the secret sauce Chapters: - 00:00 Intro- 01:10 What April does: positioning for B2B tech- 02:25 Small town, med school, and the switch to engineering- 03:54 When tech marketing was all engineers- 06:31 The topic nobody can avoid: AI- 07:15 The 25-page AI-generated positioning document- 11:13 "We have AI" is not differentiation- 12:11 The so-what chain: from capability to business value- 14:20 "Any feature can be cloned with a prompt": absolute BS- 15:00 A simple matter of programming- 16:03 Nobody vibe-codes the backend of Salesforce- 17:47 Three developers, six months, and the answer was no- 19:57 Every company has capabilities competitors can't build- 20:10 The CRM keyed on people, not companies- 26:06 Is legacy a competitive advantage? The opposite- 27:14 HubSpot, Salesforce, and founding philosophy- 30:18 "Rush is good": what positioning projects get wrong- 33:29 Sales knows the shortlist better than anyone- 36:56 Where to find April Read the full blog post:https://tinytechguides.com/blog/data-faces-april-dunford-ep45-death-of-saas-myth/?utm_source=youtube&utm_medium=video&utm_campaign=ep45-april-dunford&utm_content=description Connect with April Dunford:LinkedIn: https://www.linkedin.com/in/aprildunford/Website: https://www.aprildunford.com/ Listen to the Data Faces podcast:YouTube: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBoQ8qM1FMGBwYdxvw9BurRSpotify: https://open.spotify.com/show/6SmGkQGvZQSAT1O7g1l2yFApple Podcasts: https://podcasts.apple.com/us/podcast/data-faces-podcast/id1789416487Amazon Music: https://music.amazon.com/podcasts/8465f3b3-5d41-4c84-a561-bf8af09560e3/data-faces-podcast The only podcast that tells the human stories and brings you the faces behind the data. #DataFaces #ProductPositioning #SaaS #B2BMarketing

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  Episode #44

  AI-Ready Data Is a Higher Bar Than Analytics | Matt Hayes

  Jul 28, 202637 minS4

  Getting data "ready" used to mean ready for a dashboard, where a person could catch a bad number before it did any harm. Once an AI agent acts on the data instead of a person, that safety net is gone. In this episode of the Data Faces Podcast, David Sweenor sits down with Matt Hayes, General Manager of the Data Business Unit at Qlik, to talk about why AI-ready data is a much higher bar than analytics-ready, how a customer-defined trust score keeps agents from acting on bad data, and why "data freedom" is what keeps enterprise AI both trustworthy and affordable. Key takeaways 1. AI-ready data clears a higher bar than analytics-ready data, because an agent acts on a bad number that a human would have paused on.2. A customer-defined data trust score pauses an agent when quality slips, but only works if it flags what genuinely matters.3. Matt's three principles are context, trust, and freedom, and he ranks freedom first.4. Data freedom is also an economics argument: open formats and not persisting your data hold down storage and compute cost.5. The discipline that keeps enterprise AI trustworthy is the same one that keeps it affordable. **Chapters** - 0:00 Introduction- 0:52 Matt's role and the Qlik data portfolio- 3:25 Lessons from the SAP world- 5:14 Context, trust, and freedom- 7:41 AI-ready versus analytics-ready- 11:05 What AI-ready means, and the trust score- 14:08 Data products as the finished good- 16:31 A supply-chain near-miss- 25:36 Where the data side meets the decision side- 31:51 A business case that survives the pilot- 36:17 Close **Links** Full blog: https://tinytechguides.com/blog/data-faces-matt-hayes-ep44-trust-enterprise-ai/?utm_source=youtube&utm_medium=video&utm_campaign=ep44-matt-hayes&utm_content=descriptionAlso on Substack: [SUBSTACK URL — pending publish]Matt Hayes on LinkedIn: https://www.linkedin.com/in/hayestech01/Qlik: https://www.qlik.com/ Data Faces PodcastYouTube: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBoQ8qM1FMGBwYdxvw9BurRSpotify: https://open.spotify.com/show/6SmGkQGvZQSAT1O7g1l2yFApple Podcasts: https://podcasts.apple.com/us/podcast/data-faces-podcast/id1789416487Amazon Music: https://music.amazon.com/podcasts/8465f3b3-5d41-4c84-a561-bf8af09560e3/data-faces-podcast #DataFacesPodcast #EnterpriseAI #Qlik

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  Episode #43

  When AI copies every feature, what's left to sell? | Donald Farmer

  Jul 14, 202636 minS4

  For decades, buying a data platform meant buying into a practice, a methodology, a community, a whole way of seeing the work. Now a general-purpose AI model can copy almost any feature from a prompt. So what does a software vendor sell?On this episode of the Data Faces podcast, David Sweenor talks with Donald Farmer, Principal of TreeHive Strategy and a veteran data product leader from Microsoft and Qlik, about practice versus process, why a Tableau analyst stays a Tableau analyst, why "a human in the loop" is so often a cop-out, and the four human attitudes a system can model but never feel.Key takeaways:-Why the technical feature moat has collapsed, and what replaces it-Practice versus process, and why a practice is sticky when features are not-Why community and identity outlast any single feature-"A human in the loop is a cop-out," and how to design the human attitudes in on purpose-Trust, doubt, ambition, and care, the four things AI can simulate but not feelChapters:00:00 Intro01:16 Meeting at the BARC Data and Analytics Retreat03:07 The shoe shop and an analytic mindset at sixteen05:01 First week at Microsoft, briefing a bank08:32 Why companies still run on spreadsheets10:13 Practice versus process12:52 Tableau versus Qlik, and two different practices16:50 Can vendors still compete on features?20:38 Open source, community, and AI22:01 Do businesses have an ethical stance?25:13 "A human in the loop is a cop-out"27:47 The four human attitudes30:50 Intelligence without purpose33:46 Marvin the Paranoid AndroidRead the full blog post: https://tinytechguides.com/blog/data-faces-donald-farmer-ep43-practice-vs-process/?utm_source=youtube&utm_medium=video&utm_campaign=ep43-donald-farmer&utm_content=descriptionConnect with Donald Farmer: LinkedIn: https://www.linkedin.com/in/donalddotfarmerListen to the Data Faces podcast: YouTube: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBoQ8qM1FMGBwYdxvw9BurR Spotify: https://open.spotify.com/show/6SmGkQGvZQSAT1O7g1l2yFApple Podcasts: https://podcasts.apple.com/us/podcast/data-faces-podcast/id1789416487 Amazon Music: https://music.amazon.com/podcasts/8465f3b3-5d41-4c84-a561-bf8af09560e3/data-faces-podcastThe only podcast that tells the human stories and brings you the faces behind the data.#datafacespodcast #practiceVsProcess #AIstrategy #businessIntelligence #agenticAI

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  Episode #42

  The three V's of agentic AI | Doug Laney on the self-driving business

  Jun 30, 202635 minS4

  Doug Laney coined the three V's of big data in 2001. On this episode of the Data Faces podcast, he names the next three for the agentic AI era: volition, visibility, and viscosity. David Sweenor and Doug Laney get into the seven levels of agentic autonomy, from a basic chatbot to a business that runs itself, why labor savings are the least imaginative way to value an AI agent, who really owns your data, and what it would take for a billion-dollar company to run on a handful of people. Doug Laney is the Innovation Fellow for Data and Analytics Strategy at West Monroe, author of Infonomics and Data Juice, and a former Gartner Distinguished Analyst. Key takeaways: - The three new V's of agentic AI: volition, visibility, and viscosity- The seven levels of agentic AI autonomy, and where most companies actually sit- Substitution, amplification, and invention: a better way to value agents- Warranted delegation over blind trust, the brakes before the accelerator- Why data is the strangest and most renewable asset a company owns Chapters: - 00:00 Intro- 01:55 The Risky Business icebreaker- 04:05 Do the original 3 V's still hold up?- 04:30 The new three V's: volition, visibility, viscosity- 07:06 Why labor savings are the least imaginative measure- 09:43 The hospital example: substitution, amplification, invention- 11:23 Cost versus revenue, and the numerator with no ceiling- 13:44 What MBA students grasp that executives miss- 17:27 Who really owns your data- 20:18 The seven levels of autonomy- 25:47 The billion-dollar company with almost no employees- 29:58 Warranted delegation and the brakes- 33:04 Parting wisdom from Marvin Minsky Read the full blog post:https://tinytechguides.com/blog/data-faces-douglas-laney-ep42-three-vs-agentic-ai/?utm_source=youtube&utm_medium=video&utm_campaign=ep42-douglas-laney&utm_content=description Connect with Doug Laney:LinkedIn: https://www.linkedin.com/in/douglaney Listen to the Data Faces podcast:YouTube: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBoQ8qM1FMGBwYdxvw9BurRSpotify: https://open.spotify.com/show/6SmGkQGvZQSAT1O7g1l2yFApple Podcasts: https://podcasts.apple.com/us/podcast/data-faces-podcast/id1789416487Amazon Music: https://music.amazon.com/podcasts/8465f3b3-5d41-4c84-a561-bf8af09560e3/data-faces-podcast #agenticAI #AIagents #dataStrategy #infonomics #DataFaces

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