Data Faces Podcast

TinyTechMedia
  • 5.0 (1)
  • Technology
  • Semimonthly

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 candid conversations with the data leaders, AI innovators, and marketing practitioners putting data and AI to work. Topics include why enterprise AI initiatives fail, AI-ready data and data quality, AI and agentic governance, AI agents, enterprise AI adoption, and B2B marketing. For CIOs, CDOs, analytics leaders, and product marketers.

  1. 3d ago ·  Bonus

    Is Your AI Lying to You? | Christopher Chen, Simba

    "Maybe it's not true that your AI is lying to you, but it could be giving you the wrong answer." That's Christopher Chen's answer when I pressed him on the Simba booth banner at CDAO Fall in Boston. The sign said, "Your AI is lying to you." Chris, Senior Product Manager for Simba Agentic Intelligence at insightsoftware, had a softer and more accurate version of the claim ready. The data isn't lying. The model isn't lying. Business context is often missing between them, and that's where the wrong answer comes from.Chris and I sit down on location at CDAO Fall to work through what makes AI answers wrong. We get into the three ways most organizations define revenue and why that spread is where hallucinations start. Chris names the three pillars he says most agentic AI programs drop for speed — data sovereignty, AI governance, and contextual grounding — and closes with practitioner advice on where a data leader should actually start.What you will learn:1. Why AI hallucinations usually aren't a data problem or a model problem on their own.2. How defining a metric once in a semantic layer stops three teams from arguing with three different numbers.3. Where to start on agentic AI without a six-month program plan, and what to prove in the first use case.Chapters:0:00 Intro0:16 Welcome from CDAO Fall Boston1:50 Built for Change: AI architecture that outlasts uncertainty2:38 The booth banner: Your AI is lying to you2:56 The reframe: not the data, not the model4:17 Revenue defined three ways5:29 Data sovereignty and the EU AI Act7:39 The pillars most programs drop for speed8:50 What a semantic layer does that a model can't11:19 Bring your own LLM and token cost13:27 Dark Matter aside14:42 Start small: the practitioner playbook15:15 Where to find Simba15:41 Wrap-upRead the full article: https://tinytechguides.com/blog/data-faces-christopher-chen-ep58-ai-semantic-layer/?utm_source=youtube&utm_medium=video&utm_campaign=ep58-christopher-chen&utm_content=descriptionLearn more about Simba Agentic Intelligence: https://www.insightsoftware.com/simba/solutions/agentic-intelligence/?utm_source=data-faces-podcast&utm_medium=youtube&utm_campaign=ep58-christopher-chen&utm_content=descriptionConnect with Christopher Chen: https://www.linkedin.com/in/christopherwchen/Connect with David Sweenor: https://www.linkedin.com/in/davidsweenor/#DataFacesPodcast #AgenticAI #AISemanticLayer

    Is Your AI Lying to You? | Christopher Chen, Simba

  2. Nov 3

    Is BI Dead in the Age of AI Analysts? | Lucas Thelosen

    "Two years ago I was winning. But it really quickly changed." Lucas Thelosen used to race his own AI analyst to find the $10 million insight in a customer's data. He built DAS42, one of the largest Looker consultancies, then led Google's product org for data and AI. Now he is the founder and CEO of Gravity, the company behind Orion, an AI analyst.Lucas and David Sweenor get into whether BI is dead, why so few employees actually open the dashboards their company builds, and how an AI analyst can give every person a personalized answer while keeping the numbers the same across teams. Lucas explains why his team put deterministic SQL and Python wherever they could instead of trusting the model, what a "glass box" audit trail looks like for regulated industries, and why he thinks analysts should become context architects and data product managers.What you will learn:1. Why AI catches what people miss, starting with the green numbers nobody drills into.2. How to keep personalized AI answers from producing a new round of conflicting numbers.3. Why context needs ongoing maintenance, and how "ambient learning" keeps it current.Chapters: 0:00 Intro 1:02 From the 2008 crash to Looker and Google 2:35 The economist who wanted to be an ambassador 3:52 Is BI dead? 6:42 AI makes things up, so how do you trust it? 8:50 Same question, same answer across teams 10:47 Context architects and data product managers 13:19 Can you have too much context? 14:33 Why an AI analyst needs its own platform 17:53 Do you still need analytics skills? 19:14 Red blindness and the green numbers nobody checks 20:01 Audit trails and the glass box 21:31 The AI administrator nobody built 23:21 Ambient learning keeps context current 25:08 Is it listening to everything? 26:27 Unstructured data in analytics 28:02 Do you still need a semantic layer? 29:27 Warp drive 30:42 Repeatable workflows and the 7 a.m. podcast 32:08 What's next: governing the agent sprawl 33:21 When Breath Becomes Air 34:14 Wrap-upRead the full article: https://tinytechguides.com/blog/data-faces-lucas-thelosen-ep57-ai-analyst/?utm_source=youtube&utm_medium=video&utm_campaign=ep57-lucas-thelosen&utm_content=descriptionConnect with Lucas Thelosen: https://www.linkedin.com/in/thelosen/ Connect with David Sweenor: https://www.linkedin.com/in/davidsweenor/#DataFacesPodcast #AIAnalyst #BusinessIntelligence

    Is BI Dead in the Age of AI Analysts? | Lucas Thelosen

  3. Oct 27 ·  Bonus

    Why De-Biasing AI Is Impossible | Leigh Felton

    Leigh Felton, President of the AI for Job Security Foundation, joins Data Faces on location at the 20th annual CDOIQ Symposium in Cambridge, Massachusetts. Had AI been screening job candidates when she started out, she says it would have rejected her. She was a pregnant, homeless teenager and a college dropout who went on to spend 20 years at Microsoft, where she headed the company's first ethical AI enablement program.Leigh shows her two-volume book series, I Could Have Been CEO Yesterday, and explains why she believes you cannot de-bias a system that is built to recognize historical patterns. She makes the case for governing AI as an environment that shapes how people think rather than as a tool you pick up and put down. She also challenges the assumption that faster AI output equals productivity, pointing to research showing AI has reduced productivity for some engineers by about 19 percent.What you will learn:1. Why de-biasing an AI system trained on historical patterns may be impossible2. How environmental-scale governance, in the spirit of OSHA, differs from governing AI as a tool3. Why collaborating with AI and pushing back on it beats publishing its first draft, even when the task takes longer4. Why junior employees and Gen Zers will question the things 30-year experts stopped questioningChapters:0:00 Welcome and the presidential icebreaker1:06 Inside the AI for Job Security Foundation1:36 Two books on AI bias and who gets hired4:48 AI as an environment6:55 Speaking the CDO's language on productivity10:09 Don't let AI genericize you13:30 Will AI ever be smarter than people?15:45 Where to find Leigh's booksWatch more Data Faces on location: https://tinytechguides.com/data-faces-podcast/?utm_source=youtube&utm_medium=video&utm_campaign=cdoiq2026-leigh-felton&utm_content=descriptionConnect with Leigh Felton on LinkedIn: https://www.linkedin.com/in/leighfelton/#DataFacesPodcast #CDOIQ #AIEthics #FutureOfWork

    Why De-Biasing AI Is Impossible | Leigh Felton

  4. Oct 20

    AI Won't Replace the Analyst | Gerry Van Zandt, Alteryx

    "AI can tell you what the market appears to believe, but an experienced analyst can tell you whether you should believe that or not." Gerry Van Zandt has spent close to 30 years in analyst relations at Intel, HP, Oracle, and now Alteryx, and he thinks AI makes analysts more important to technology buyers.Gerry and David Sweenor get into how the inputs behind a big technology purchase have multiplied, from podcasters and former analysts turned consultants to AI shortlists, and which of them matter at each stage of the buying cycle. Gerry answers the question everyone asks about analyst firms: whether the rankings are pay-to-play, and explains how a startup with no budget got into a Gartner Cool Vendors report. He also describes how AI now cuts his RFI work by about 70 percent, and how Gartner, Forrester, and IDC are taking very different paths with AI.What you will learn:1. Why analysts still carry institutional trust that an AI answer or an independent voice has a harder time earning.2. What paying an analyst firm actually buys you, and what it does not.3. How to brief the big firms with no paid relationship, and how an AR team can use AI to prepare executives in minutes.Chapters: 0:00 Intro 1:12 An Orioles aloha shirt 1:46 Thirty years in analyst relations 4:30 The Air Force pilot who became a technologist 5:41 What analyst relations is and why it matters 7:23 What influences a technology buyer in 2026 9:57 Are the veteran analysts aging out? 12:00 Do you still need Gartner when AI can do deep research? 14:06 AI filling out analyst RFIs on both sides 17:02 Relationships as the currency of analyst relations 18:48 Is it pay to play? 20:41 The NDA knowledge no AI can reach 22:08 Tier-two firms and startups on a budget 23:45 Briefing Gartner with no subscription 25:09 Gartner analysts vs. independents with 50,000 followers 26:52 What companies get wrong about analyst relations 29:16 How AI is changing the AR job 31:51 Genealogy and a World War II howitzer battalion 34:35 Where CMOs and CEOs should invest in AR 37:07 How Forrester, IDC, and Gartner are responding to AI 40:19 Wrap-upRead the full article: https://tinytechguides.com/blog/data-faces-gerry-van-zandt-ep55-analysts-ai/?utm_source=youtube&utm_medium=video&utm_campaign=ep55-gerry-van-zandt&utm_content=descriptionConnect with Gerry Van Zandt: https://www.linkedin.com/in/gerryv1/ Connect with David Sweenor: https://www.linkedin.com/in/davidsweenor/#DataFacesPodcast #AnalystRelations #Gartner

    AI Won't Replace the Analyst | Gerry Van Zandt, Alteryx

  5. Oct 13 ·  Bonus

    Industry Data Quality Gets a D Plus | Stacie Christensen, H-E-B

    Stacie Christensen, Senior Data Leader at H-E-B, joins Data Faces on location at the 20th annual CDOIQ Symposium in Cambridge, Massachusetts. After 22 years in the data industry, she grades our collective data quality somewhere between a D plus and a C minus, and she argues that data initiatives fail long before technology is ever the problem. Stacie explains why AI is a multiplier and a scaler that has nothing positive to scale when the foundation underneath it is missing. She makes the case that data governance belongs at data creation, since bad data becomes exponentially more expensive the further down the pipeline it travels. She also introduces her book series, including Built on Data and the quietly released Built on Simplicity, and shares why circular data flows are her favorite red flag for hidden complexity. What you will learn: 1. Why the foundation under the technology, including accountability, ownership, and governance, decides whether data initiatives succeed 2. Why hoarding data creates noise, backlog, and risk without helping critical business decisions 3. Why governing data at creation beats governing it downstream in analytics and reporting 4. How to spot circular data flows and use them as a starting point for simplification Chapters: 0:00 Welcome from day three at CDOIQ 0:50 The hardest class on campus 1:58 Why data initiatives fail before technology 3:35 AI is a multiplier and a scaler 5:16 Grading industry data quality 8:49 Governance starts at data creation 11:07 Built on Simplicity and circular data flows 14:26 Where to find Stacie's books Watch more Data Faces on location: https://tinytechast/?utm_source=youtube&utm_medium=video&utm_campaign=cdoiq2026-stacie-christensen&utm_content=description Connect with Stacie Christensen on LinkedIn: https:iechristensen/ Stacie's website: https://dataisnotpersonal.com #DataFacesPodcast #CDOIQ #DataGovernance #DataQuality

    Industry Data Quality Gets a D Plus | Stacie Christensen, H-E-B

  6. Oct 6

    The AI Context Layer Nobody Names | Ken Sanford, Clarifeye

    Most of what makes an expert good at the job was never written down. Ken Sanford, who leads go-to-market and commercial strategy at Clarifeye, argues that this tribal knowledge is the part of the AI context layer nobody talks about, because document retrieval is close to solved and the judgment to interpret those documents still lives in people's heads. Ken and David Sweenor go back to their SAS days. They get into how Clarifeye's AI interviewer, Clara, pulls expertise out of people by hunting for edge cases and exceptions, and why the most tech-forward people in a company are the wrong ones to write its AI skills. Ken also explains why the memories your AI harness builds amount to your judgment, why your company should own them, and where he thinks the next generation of experts comes from. What you will learn:1. Why near-perfect document retrieval still leaves AI without the judgment to interpret what it finds. 2. How interviewing experts about exceptions, five or ten minutes a day, captures knowledge a wiki never will. 3. Why the people with 25 or 30 years of intuition should shape your AI skills, and how to make that capture a pull. 4. Why owning the artifacts your AI harness builds lets a company move between models and tools. Chapters: 0:00 Intro 1:17 Ken Sanford and Clarifeye 2:29 A surfboard at the trade show booth 3:42 Iron Man 3 extras and a first job in Naples 5:58 Tribal knowledge and the unstructured data problem 8:20 How Clara runs an expert interview 10:51 Kids, chatbots, and sycophantic AI 12:02 Do experts resist? Harness memories as your judgment 13:29 Where customers start: onboarding and software migrations 15:20 Data, context, and the reasoning layer 16:17 Keeping knowledge current, and banning the word "ship" 18:58 Would leaders use AI to replace themselves? 19:50 The wrong people are writing your AI skills 22:16 Where the next generation of experts comes from 24:41 Hand integration and the skills we lose26:16 Hiring for agency27:32 "Just quit your company" and a team of 12 29:22 Why process blueprints beat a vector store 30:46 Build your own career knowledge base 31:40 End of days? A techno-optimist view 34:58 West of Jesus and the science of flow 37:05 Where to find Ken Read the full article: https://tinytechguides.com/blog/data-faces-ken-sanford-ep53-tribal-knowledge-ai/?utm_source=youtube&utm_medium=video&utm_campaign=ep53-ken-sanford&utm_content=description Connect with Ken Sanford: https://www.linkedin.com/in/ekenomics/Clarifeye: https://www.clarifeye.ai/Connect with David Sweenor: https://www.linkedin.com/in/davidsweenor/ #DataFacesPodcast #TribalKnowledge #AISkills

    The AI Context Layer Nobody Names | Ken Sanford, Clarifeye

  7. Sep 29 ·  Bonus

    No Business Value From AI? Shut It Down | Randy Bean

    Randy Bean, founder and CEO of the Data & AI Leadership Exchange, joins Data Faces on location at the 20th annual CDOIQ Symposium in Cambridge, Massachusetts. His test for data leaders is blunt. If you're not getting measurable business value from your data and AI investments, or a clear path to it, go back to the office this afternoon and shut them down.Randy has organized the CDOIQ Chief Data Officer panel for 12 years, and this year's edition featured data leaders from Lowe's, Ford, and Humana. He traces how the role has moved from defensive risk and compliance work to an offensive, business-focused mandate, shares fresh survey numbers on where CDOs report, and explains why every chief data officer from last year's panel will be out of that role by the end of this year. He also gives his mid-year read on the AI bubble.What you will learn:1. How the CDO role moved from risk and compliance to a business-value mandate2. Where CDOs report today, with 42% under technology and 33% under business leadership3. Why churn at the top of the panel signals a fight to control AI's future in the enterprise4. Randy's bubble call, AI is overestimated in the short term and underestimated in the long termChapters:0:00 Welcome back to Data Faces0:38 Poet or rock star1:55 Twelve years of CDO panels3:10 Shut down what doesn't deliver value7:26 Where should the CDO report10:44 A role changing in real time11:55 The AI bubble, short term and long term14:24 Sign-off from CambridgeWatch more Data Faces on location: https://tinytechguides.com/data-faces-podcast/?utm_source=youtube&utm_medium=video&utm_campaign=cdoiq2026-randy-bean&utm_content=descriptionConnect with Randy Bean on LinkedIn: https://www.linkedin.com/in/randybeannvp/#DataFacesPodcast #CDOIQ #ChiefDataOfficer #DataLeadership

    No Business Value From AI? Shut It Down | Randy Bean

  8. Sep 22

    AI Didn't Break the Tech Job Market | Jim Jinright, JRJ Search Group

    Everyone blames AI for the tech job market. An executive recruiter who talks to the hiring decision-makers every day says the story started two years before the AI headlines did.Jim Jinright, Managing Partner at JRJ Search Group in Dallas Fort Worth, joins David Sweenor to explain the perfect storm behind the layoffs: pandemic overhiring, the 2022 interest rate increases, and labor arbitrage, with AI arriving last and taking the blame. He describes the move from a no-hire, no-fire economy to a low-hire, no-fire one, and why his phone is ringing more than it was a year ago.We also get into where the junior work went, what a recruiter does when 500 applications arrive in an hour, ghost jobs, age screens, why Jim calls the AI interview a slap in the face, and his advice for early-career engineers who are watching coding become a commodity.Key takeaways:1. The tech job market slowdown traces to overhiring, interest rates, and cost cutting. AI added the "10 engineers or none" idea, and that idea is starting to correct.2. AI is powerful and has no context. Senior people with judgment are more in demand, and junior people lost the small problems they used to learn on.3. Every resume is perfect now, so the conversation decides. Bring the receipts: problems you owned and outcomes you produced.4. Ghost jobs are real. If you are not getting responses, it is not always you. Sometimes it is the moment.5. Move up the stack. Stop competing on a language and become the person who builds and runs the software factory.Chapters:0:00 Intro1:11 JRJ Search Group and twelve years in executive search2:16 A would-be pilot and a demo tape3:33 The perfect storm behind the layoffs5:45 From no-hire, no-fire to low-hire, no-fire6:16 Where the junior work went8:23 Every resume is perfect now9:04 500 applicants in an hour11:19 Ghost jobs13:35 Surviving a layoff and staying sane16:03 Age discrimination and showing the last ten years18:24 What "AI literate" really means to clients21:31 Demonstrating emotional intelligence24:43 Fractional work26:31 The AI interview28:44 Job hopping and the resume that explains itself32:57 Advice for early-career engineers36:57 What Jim is watching38:11 Where to find JimRead the full article: https://tinytechguides.com/blog/data-faces-jim-jinright-ep51-perfect-storm-tech-job-market/?utm_source=youtube&utm_medium=video&utm_campaign=ep51-jim-jinright&utm_content=descriptionConnect with Jim Jinright: https://www.linkedin.com/in/jimjinright/JRJ Search Group: https://jrjtx.comConnect with David Sweenor: https://www.linkedin.com/in/davidsweenor/#DataFacesPodcast #AIJobs #TechCareers

    AI Didn't Break the Tech Job Market | Jim Jinright, JRJ Search Group
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About

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 candid conversations with the data leaders, AI innovators, and marketing practitioners putting data and AI to work. Topics include why enterprise AI initiatives fail, AI-ready data and data quality, AI and agentic governance, AI agents, enterprise AI adoption, and B2B marketing. For CIOs, CDOs, analytics leaders, and product marketers.

Information

  • Creator
    TinyTechMedia
  • Years Active
    2025 - 2026
  • Episodes
    58
  • Rating
    Clean
  • Copyright
    © TinyTechMedia
  • Show Website
    Data Faces Podcast

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