Data Faces Podcast

TinyTechMedia
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  • Technology
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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. 5d ago ·  Bonus

    Data Quality for 140 Million Members | Intuit Credit Karma

    Veenit Shah, Senior Manager of Data Engineering, and Puneet Singh, Senior Data Engineer, both at Intuit Credit Karma, join Data Faces on location at the 20th annual CDOIQ Symposium in Cambridge, Massachusetts. Their team keeps credit scores current for about 140 million members, and an AI remediation agent now compresses root cause investigations that once took 30 to 40 minutes into just a few.Veenit and Puneet walk through the data quality program they presented at the conference, from startup-era firefighting with nightly calls and manual checks to five pillars monitored across more than 100 tables and 40,000 columns. They explain why data quality is everybody's job when a single alert can touch millions of members, how agents now generate production-ready pipeline code in minutes, and why the next levels of their agentic maturity model remain under security review.What you will learn:1. How Credit Karma moved from reactive firefighting to five data quality pillars measured across 40,000 columns2. Why every alert gets logged, measured, and analyzed weekly, with brutal prioritization of the noisiest issues3. How an AI remediation agent runs and refines queries until it lands on root cause and blast radius4. Where guardrails, anonymized data, and selective agent access fit as the team climbs the maturity curveChapters:0:00 Welcome and introductions2:21 The firefighting years4:58 Five pillars across 40,000 columns6:48 Whose job is data quality?8:14 The AI remediation agent10:10 Level two on the agentic maturity curve12:22 Guardrails and security for agents14:11 Wrap-up with Veenit and PuneetWatch more Data Faces on location: https://tinytechguides.com/data-faces-podcast/?utm_source=youtube&utm_medium=video&utm_campaign=cdoiq2026-intuit-credit-karma&utm_content=descriptionConnect with Veenit Shah on LinkedIn: https://www.linkedin.com/in/veenitshah/Connect with Puneet Singh on LinkedIn: https://www.linkedin.com/in/puneet-singh-5614b065/Intuit Credit Karma: https://www.creditkarma.com#DataFacesPodcast #CDOIQ #DataQuality #AIAgents

    Data Quality for 140 Million Members | Intuit Credit Karma

  2. Sep 8

    Why "AI-Ready Data" Means Almost Nothing | Malcolm Hawker, Profisee

    Every vendor says your data needs to be "AI-ready." Almost none of them define it the same way.Malcolm Hawker, Chief Data Officer at Profisee, former Gartner analyst, and author of The Data Hero Playbook, joins David Sweenor to take the term apart. His definition is short: data is AI-ready when it supports the use case in front of it. That makes readiness a spectrum, not a state, and it means the same customer data can clear the bar for a marketing campaign and fall far short for a regulated decision.We also get into why a semantic layer cannot solve identity, why "garbage in, garbage out" is a career-limiting thing to say to your CEO, and how the industry manufactures new names for problems that already had them.Key takeaways:1. Data is AI-ready when it fits the purpose. There is no universal threshold, and treating readiness as binary is why the term is ambiguous and nearly meaningless.2. The cost of being wrong, not a quality score, decides whether a use case can go into production.3. Semantic layers define what a customer means. They cannot tell you which of fifteen customer records is the real person.4. Most failed proofs of concept put a probabilistic system into a process that always ran on deterministic rules.5. New vocabulary in data management is often an old idea with a new label, and the loop that produces it is predictable.Chapters:0:00 Intro1:18 From Dun & Bradstreet to Gartner to Profisee4:22 The record label Malcolm never started7:02 What does AI-ready data even mean?7:46 Fitness for purpose, and why ChatGPT works anyway11:56 Context, semantic layers, and the fifteen David Sweenors14:51 Why "garbage in, garbage out" gives Malcolm hives15:44 Data quality for unstructured data19:26 The semantic pedantic feedback loop23:30 How data literacy became a top-three problem overnight25:17 Does MDM apply to unstructured data?29:54 The Data Hero Playbook and the growth mindset34:43 What Malcolm is reading37:13 Where to find MalcolmRead the full article: https://tinytechguides.com/blog/data-faces-malcolm-hawker-ep49-ai-ready-data/?utm_source=youtube&utm_medium=video&utm_campaign=ep49-malcolm-hawker&utm_content=descriptionMentioned in this episode:Bridging Knowledge, Data, and AI by Joseph Hilger, Lulit Tesfaye, and Zachary WahlSoftware Wasteland by Dave McCombConnect with Malcolm Hawker: https://www.linkedin.com/in/malhawker/Connect with David Sweenor: https://www.linkedin.com/in/davidsweenor/#DataFacesPodcast #MasterDataManagement #AIReadyData

    Why "AI-Ready Data" Means Almost Nothing | Malcolm Hawker, Profisee

  3. Sep 1 ·  Bonus

    3x Faster Time to Market With Data Products | Capital One

    Amy Lenander, Chief Data Officer at Capital One, and Christina Egea, SVP of Enterprise Data, join Data Faces on location at the 20th annual CDOIQ Symposium in Cambridge, Massachusetts. Their data product strategy is producing measurable results, with use cases launching three times faster and standardized data costing 30% less to maintain.Amy and Christina walk through the talk they gave at the symposium, doubling down on data products to drive business value. They explain how a usage analysis revealed that nine categories of data covered most of the company's needs, how every data product gets a single accountable owner, and why Amy's background running Capital One businesses gives her the empathy and credibility to push partners to eat their vegetables. Christina also gets candid about the hardest part, which is getting started, and the tension between building fast for one use case and building what scales to a hundred.What you will learn:1. How a usage analysis revealed nine core data categories to build products around2. The lifecycle of a data product, from scoping and ownership to ontology modeling and data assets3. Why a business-first background helps a CDO prioritize the data that matters most4. How listening to customers drives adoption, from easier migration to closing the missing 30%Chapters:0:00 Welcome and introductions1:37 Doubling down on data products3:40 Curating the data that matters most5:44 How a data product comes to life6:58 A business-first path to the CDO role9:17 The hardest part is getting started10:19 Driving adoption by listening12:39 Results and sign-offWatch more Data Faces on location: https://tinytechguides.com/data-faces-podcast/?utm_source=youtube&utm_medium=video&utm_campaign=cdoiq2026-capital-one&utm_content=descriptionConnect with Amy Lenander on LinkedIn: https://www.linkedin.com/in/amylenander/Connect with Christina Egea on LinkedIn: https://www.linkedin.com/in/christinaegea/Capital One: https://www.capitalone.com#DataFacesPodcast #CDOIQ #DataProducts #DataStrategy

    3x Faster Time to Market With Data Products | Capital One

  4. Aug 25

    The Real Cost of Enterprise AI | Sam Pierson, CTO of Qlik

    Every AI architecture decision made today comes with an expiration date. Models leapfrog each other every few months, patterns change, and the conversation itself will look different in a year. In this episode of the Data Faces Podcast, David Sweenor sits down with Sam Pierson, Chief Technology Officer at Qlik, to talk about building an AI stack you can change your mind about: open formats like Apache Iceberg, a model router that sends each task to the model that is good enough but 10 times cheaper, and why most of your AI token bill is decided in the architecture before a model ever runs.Key takeaways-Enterprises are rebuilding data architectures on open formats like -Apache Iceberg so components can be swapped as AI patterns change.-Qlik's model router matches each task to the best cost-performance model, including models that are good enough but 10 times cheaper.-Token costs pile up before a model answers: agents interrogating a cloud data warehouse burn tokens for minutes per question, while a pre-calculated in-memory engine answers at zero inference cost.On the claim that AI can clone any software feature with a prompt, Sam's verdict is "largely it's BS." The durable advantage is the engine, the data fabric, and governance.97 percent of enterprises have budgeted agentic AI but only 18 percent have fully deployed it. We are in the first inning.Chapters0:00 Introduction1:16 Sam's role as CTO at Qlik2:00 First job: mowing lawns2:34 Lessons from Talend3:55 How AI changes data integration10:13 Open formats and Apache Iceberg13:43 The model router16:02 Token costs, ROI, and sovereignty18:43 Is freedom from lock-in real?22:58 The zero-inference-cost engine25:43 Freedom versus governance28:05 "Largely it's BS"32:33 97 percent budgeted, 18 percent deployed35:30 CloseFull blog: https://tinytechguides.com/blog/data-faces-sam-pierson-ep46-adaptable-ai-architecture/?utm_source=youtube&utm_medium=video&utm_campaign=ep46-sam-pierson&utm_content=description Also on Substack: https://open.substack.com/pub/davidsweenor/p/your-ai-stack-will-be-wrong-in-12?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true Sam Pierson on LinkedIn: https://www.linkedin.com/in/samuelpierson/ Qlik: https://www.qlik.com/ Data Faces Podcast YouTube: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBoQ8qM1FMGBwYdxvw9BurR Spotify: https://open.spotify.com/show/6SmGkQGvZQSAT1O7g1l2yF Apple 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-podcast#DataFacesPodcast #EnterpriseAI #Qlik

    The Real Cost of Enterprise AI | Sam Pierson, CTO of Qlik

  5. Aug 18 ·  Bonus

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

    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

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

  6. Aug 11

    SaaS Is Dead? Absolute BS | April Dunford

    "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 AprilRead 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=descriptionConnect 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

    SaaS Is Dead? Absolute BS | April Dunford

  7. Jul 28

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

    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 takeaways1. 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

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

  8. Jul 14

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

    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

    When AI copies every feature, what's left to sell? | Donald Farmer
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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
    50
  • Rating
    Clean
  • Copyright
    © TinyTechMedia
  • Show Website
    Data Faces Podcast