Data Faces Podcast

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

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

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

"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

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

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

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

Everyone is racing to do something with agentic AI. Almost nobody is asking whether they should. Andreas Welsch spent close to two decades building enterprise AI at SAP, and he is now one of the most direct voices on what agentic AI demands from business leaders.In this episode of the Data Faces Podcast, David Sweenor and Andreas Welsch talk about the contagion driving AI layoffs, the revenue question leaders keep skipping, the truth about "SaaS is dead," and why agent risk compounds as you add more agents. It is a candid conversation about keeping human judgment in the loop while the hype runs ahead of reality.Key takeaways1. AI layoffs spread like a contagion, one announcement at a time, even when the value case is unproven.2. The question that saves money and sanity is "should we?" not "can we?"3. Rebuilding SaaS yourself is possible, but you pay subscriptions for convenience, maintenance, and peace of mind.4. The bigger prize is revenue and growth, not another round of cost cuts.5. Agentic AI is a probabilistic system that can be confidently wrong, and risk compounds as agents multiply.Chapters (estimated from the transcript, verify against the recording)- 00:00 Intro- 01:01 What Intelligence Briefing does- 01:48 From pediatrician dreams to taking apart RC cars- 03:45 Leaving SAP and becoming the CXO of everything- 10:40 The layoffs vicious cycle and the revenue question- 13:58 Pilots versus production- 21:37 "SaaS is dead" and vibe-coding clones of DocuSign and Mentimeter- 25:00 When to defer risk to a vendor- 27:57 Just because you can does not mean you should- 32:47 Editing a book with three custom GPTs- 36:12 The conventional wisdom that is wrongLinksRead the full blog: https://tinytechguides.com/blog/andreas-welsch-agentic-ai-human-edge/?utm_source=youtube&utm_medium=video&utm_campaign=ep41-andreas-welsch&utm_content=description Connect with Andreas Welsch: https://www.linkedin.com/in/andreasmwelsch Intelligence Briefing: https://intelligence-briefing.com

"Without context, your agents are dumb." That's how Josh Howard, Senior Director of Product Marketing for Executive Audiences at Databricks, closed Episode 40 of the Data Faces Podcast.Frontier models are some of the most advanced technology of our lifetime. They are also dumb in the way that matters for your business, because they were trained on the public internet and have never seen your customer records, your forecast methodology, or your sales policies. In this episode, host David Sweenor and Josh Howard unpack new findings from the Databricks and Economist Enterprise *Making AI Deliver* survey of 1,221 senior technology leaders, including the 84/43 measurement gap, why data infrastructure costs more than the GPU bill, where AI agents are already working in the enterprise, and why the real race over the next five years isn't to AGI.**Key Takeaways:**1. Today's models are "dumb" not because they lack capability, but because they lack enterprise context2. 59% of senior tech leaders say data storage and movement is the biggest AI cost, only 25% say compute3. 84% of executives say AI is beating expectations, but only 43% require teams to measure the impact4. AI agents now create 80% of new databases on Databricks' Neon serverless Postgres layer, up from 0.1% in 20235. The next five years will reward boring work: cleaning data, fixing semantics, and tying agent projects to measurable outcomes**Timestamps:**00:00 - Opening and introduction01:17 - Josh's role leading PMM for executive audiences at Databricks02:21 - If not PMM: full-time fly-fishing guide in Colorado03:23 - "Your AI is dumb" — what the phrase actually means05:25 - Structured vs. unstructured data and the industry's row-and-column trap06:20 - Where Josh and Dave first met at Dell Technologies08:13 - Metadata, context, and the 20-year-old enterprise architect fight09:37 - The November 2022 ChatGPT moment in the C-suite11:07 - Trying to pry Excel from a financial analyst's hands at Alteryx12:08 - Human-in-the-loop and the Replit agent that wiped a production database12:53 - Conversational analytics, Databricks Genie, and internal semantics19:11 - Inside the Databricks and Economist *Making AI Deliver* survey20:54 - The 84/43 measurement gap23:21 - The 59/25 cost split — data infrastructure vs. compute28:30 - Upskilling, prompt engineer hype, and behavior change30:17 - AI washing on the 101 corridor and Allbirds' pivot to NewBird AI33:26 - What will look obvious in 202735:39 - Closing thought: "Without context, your agents are dumb"**More insights and resources:**Blog: https://tinytechguides.com/blog/forget-agi-your-ai-is-dumb-without-your-data/?utm_source=youtube&utm_medium=video&utm_campaign=ep40-josh-howard&utm_content=description Survey: https://www.databricks.com/resources/analyst-research/making-ai-deliver**Connect with Josh Howard:**LinkedIn: https://www.linkedin.com/in/joshoward/Databricks: https://www.databricks.com/Drop your thoughts in the comments!Like, share, and subscribe for more insights.#AgenticAI #EnterpriseAI #DataLeadership #Databricks #DataFacesPodcast

The difference between an AI that "hallucinates" and one that acts intelligently lies in context.

In Episode 39 of the Data Faces Podcast, Steve Wooledge (CMO at Collate) joins David Sweenor to discuss why metadata, once a technical "card catalog," is now the foundational layer for the agentic era. Steve traces his journey from chemical engineering to building categories at Alteryx and Alation, and now leads the charge for open-source semantic intelligence at Collate.

Key takeaways:

1. Metadata vs. semantics. Technical descriptions aren't enough for AI, and Semantic Intelligence Graphs provide the "gut feel" AI lacks.

2. The Switzerland approach. Organizations need a neutral metadata layer that spans silos such as Databricks and Snowflake.

3. Marketing velocity. AI is compressing production workflows and "Taste Squared" is the new metric for human marketing leaders.

4. The category creation playbook. Steve shares lessons learned from defining "Agentic Data Intelligence" at Alation.

Chapters

● 0:00 – Introduction

● 1:08 – From Chemical Engineering to Data Sales

● 3:45 – Guitar Shredding and "Melodic" Hard Rock

● 5:01 – Marketing Lessons. Dave Kellogg and the power of first principles

● 7:40 – The hard truth about partner marketing and global SIs

● 10:18 – Why open source out-innovates the enterprise

● 15:56 – Metadata for AI agents. The semantic intelligence shift

● 20:45 – The "neutral layer" strategy

● 24:03 – How AI is changing the CMO role

● 27:23 – "Taste squared." Why you can't be a lazy marketer

● 32:19 – Career advice for the next generation of data professionals

● 36:28 – Final advice. Peer review and quality controlConnect with Steve

● LinkedIn: https://www.linkedin.com/in/stevewooledge/

● Collate: https://getcollate.io/Follow TinyTechGuides

-- Blog: https://tinytechguides.com/blog/why-ai-agents-require-a-switzerland-approach-to-metadata/?utm_source=spotify&utm_medium=video&utm_campaign=ep39-steve-wooledge&utm_content=description

--Substack: https://open.substack.com/pub/davidsweenor/p/why-ai-agents-require-a-switzerland?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

#datafacespodcast #AI #Metadata #DataGovernance #B2BMarketing