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.
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
29 Sept 2026
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
22 Sept 2026
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
Who has been a guest on Data Faces Podcast
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Judit Szabo
Hyoun Park
Shane Murray
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