Data Faces Podcast

LinkedIn has a "rewrite with AI" button. Meanwhile, Kate Strachnyi is building an entire media company on authentic human voices. Is she right?In Episode 38 of the Data Faces Podcast, Kate Strachnyi (Founder, DATAcated) shares how she pivoted from finance to data visualization, built a 40+ creator influencer agency, and why she's betting on real humans over AI-generated content.Key Takeaways:1- How Kate followed the revenue data from courses and books to a focused media business2- The DATAcated Plus model: matching authentic creators to brand campaigns in data and AI3- Why Kate calls AI-rewritten content "non-GMO" and holds her creators to the same standard4- The shift from "Kate = DATAcated" to an agency brand that scales beyond one person5- The 20-year question: who fact-checks AI when today's subject matter experts retire?Timestamps:00:00 - Opening0:05 - Kate's background and what DATAcated does2:10 - Pre-finance Kate: what she wanted to be before data found her3:05 - The career pivot from risk management to data visualization5:03 - How DATAcated evolved from training to a media company7:27 - How the influencer model works behind the scenes9:33 - Automating business operations with Claude Code11:01 - Walking the line between brand amplification and spam14:11 - The fake tattoo story from Big Data London15:03 - DATAcated Plus vs. analyst firm engagements17:14 - Sold-out personal branding session at Gartner with Scott Taylor22:15 - Shifting from "Kate = DATAcated" to an agency brand24:02 - What works on LinkedIn now vs. five years ago27:01 - AI-generated content and the "non-GMO" philosophy29:04 - The 20-year question: who fact-checks AI when the experts retire?30:20 - Deep fake Dave and why Kate plans to remain authentic31:24 - Betting on AI for operations while keeping creative output human33:57 - Does AI make you more productive or just busier?36:19 - Where to find Kate and DATAcatedMore insights and resources:Blog: https://tinytechguides.com/blog/Connect with Kate Strachnyi:LinkedIn: https://www.linkedin.com/in/kate-strachnyi-data/DATAcated: https://datacated.com/Drop your thoughts in the comments!Like, share, and subscribe for more data and AI conversations.#AuthenticContent #AI #DataFacesPodcast

For 25 years, data quality was everyone'sproblem and nobody's priority. Brendan Grady, EVP and GM of Analytics & AIat Qlik, explains why the stakes just changed.

In this episode recorded on location at QlikConnect 2026, David Sweenor and Brendan discuss consequence management, whereenterprise agentic adoption really stands ("prior to stage zero"),Qlik's Trust Score for AI, the shift from dashboards to decision intelligence,and why open standards like MCP matter in an agentic world.

For 25 years, data quality was everyone's problem and nobody's priority. Brendan Grady, EVP and GM of Analytics & AI at Qlik, explains why the stakes just changed.

In this episode recorded on location at Qlik Connect 2026, David Sweenor and Brendan discuss consequence management, where enterprise agentic adoption really stands ("prior to stage zero"), Qlik's Trust Score for AI, the shift from dashboards to decision intelligence, and why open standards like MCP matter in an agentic world.

Key takeaways:

1. Data quality was never fixed because there were no consequences for getting it wrong. AI agents changed that equation.

2. Enterprise agentic adoption is in its earliest days. Customers are experimenting, but production-grade agents are rare.

3. Qlik's Trust Score for AI gives decision-makers a quantifiable measure of data quality before it reaches an agent.

4. "Dashboards are dead" as a destination, but the data and decisions they inform are more important than ever.

5. Data professionals should become data product owners and trusted guides as agents take on routine work.

Chapters:

0:00 Introduction at Qlik Connect 2026

1:14 Brendan's first job: Sound of Music tourguide

2:04 Lessons from the early analytics era

3:32 Why data quality has never been fixed

4:46 Consequence management in the agentic era

6:08 Where agentic adoption actually stands

7:46 Future-proofing against LLM shifts

8:24 The analytics engine and unknown unknowns

10:29 Structured vs. unstructured data

12:04 Hallucinations and trust scores

15:30 "Dashboards are dead"

18:05 Brain outsourcing and cognitive debt

21:57 MCP server and open standards

23:54 Qlik 2026 themes: trust, context,flexibility

26:12 Advice for data professionals

28:15 Does AI expand who can participate inanalytics?

Links:

Blog post: https://tinytechguides.com/blog/why-bad-data-didnt-matter-until-now/

BrendanGrady on LinkedIn: https://www.linkedin.com/in/brgrady/

Qlik: https://www.qlik.com/

Data Faces Podcast: https://tinytechguides.com/data-faces-podcast/

Subscribe: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBoQ8qM1FMGBwYdxvw9BurR

#DataFacesPodcast #QlikConnect #AgenticAI#DataQuality #DecisionIntelligence

On the Data Faces Podcast, I usually interview someone with a whole face. For this bonus segment, I made an exception.

Scott Taylor's Data Puppets character "A-Eye" joins the show fresh from the Gartner Data & Analytics Summit. The puppet had thoughts about agentic AI, data governance, and vendors who couldn't spell AI two years ago.

Key moments:1- A-Eye reports from the Gartner show floor2- "Agents writing code, reviewing code, deploying code, and then apologizing for the code"3- "AI is the Ozempic for data governance, baby"4- The Old MacDonald data anthem (yes, I sang along)

Watch the full Episode 35 conversation with Scott Taylor:Blog: https://tinytechguides.com/blog/truth-before-meaning-the-three-word-fix-for-data-management/

Connect with Scott Taylor:LinkedIn: https://www.linkedin.com/in/scottdtaylor/MetaMeta Consulting: https://www.metametaconsulting.com/Data Puppets: https://www.linkedin.com/company/data-puppets/

Drop your thoughts in the comments!Like, share, and subscribe for more data and AI conversations.

#DataPuppets #DataFacesPodcast #DataManagement #AI

Data leaders have been pitching "data quality" to executives for decades, and the pitch keeps falling flat. Scott Taylor, the Data Whisperer, explains why — and what to do instead.In Episode 34 of the Data Faces Podcast, Scott Taylor (MetaMeta Consulting) shares his three-word data philosophy — truth before meaning — and the 3V framework (Vocabulary, Voice, Vision) that helps data leaders craft narratives executives actually respond to.Key Takeaways:1- "Truth before meaning" — why you must establish trust in your data before deriving any business insight from it2- The 3V framework for structuring executive conversations about data management3- Why data leaders lose the room by leading with "how" instead of "why"4- How vendor messaging at the Gartner D&A Summit created more confusion than clarity5- Why AI is not "the Ozempic for data governance"Timestamps:00:00 - Opening0:06 - Scott's background as the Data Whisperer3:59 - Truth before meaning: Scott's data philosophy in three words6:04 - The supermarket scanner example of truth in data7:56 - Why data practitioners aren't trained in storytelling10:27 - Has AI changed the data management conversation?13:08 - Vendor performance at the Gartner D&A Summit16:27 - "Context is the new oil" and the semantic pedantic cycle19:54 - Crafting a one-sentence data management story for a skeptical CFO22:59 - The 3V framework: Vocabulary, Voice, and Vision25:37 - Data Puppets and using satire to expose organizational dysfunction31:48 - Why humor helps executives hear hard truths34:24 - Where to find Scott Taylor and the Data PuppetsBONUS - Data Puppets segment: A-Eye attends the Gartner D&A SummitMore insights and resources:Blog: https://tinytechguides.com/blog/truth-before-meaning-the-three-word-fix-for-data-management/Connect with Scott Taylor:LinkedIn: https://www.linkedin.com/in/scottdtaylor/MetaMeta Consulting: https://www.metametaconsulting.com/Data Puppets: https://www.linkedin.com/company/data-puppets/Drop your thoughts in the comments!Like, share, and subscribe for more data and AI conversations.#DataManagement #DataGovernance #DataFacesPodcast

Stewart Bond coined the term "data intelligence" in 2016. Now it's a market category. Here's how it happened — and why it matters more than ever for AI.
Stewart Bond, Research VP at IDC, joins David Sweenor on the Data Faces Podcast to trace the origins of "data intelligence" from a single research note to a full-blown market category adopted by Collibra, Alation, Informatica, Databricks, and IBM. They dig into what data intelligence actually means, why it's distinct from data governance, and why the rise of agentic AI makes getting it right non-negotiable.
Key takeaways:1- Data intelligence (intelligence *about* data) is not the same as data governance — governance is organizational discipline; intelligence is the technology that enables it2- GDPR was the catalyst that accelerated enterprise interest in data intelligence and metadata management3- Databricks redefined the term to mean intelligence *from* data, triggering a debate that's still playing out4- Agentic AI demands high-quality, trustworthy data at the source — "shift left" for data quality is no longer optional5- Unstructured data intelligence is the next frontier, and most organizations are not ready
Timestamps:0:00 Opening and introductions1:06 Stewart's background — 30+ years in IT, IBM certified architect, IDC analyst since 20112:31 Personal interests: fishing, road biking, and competitive curling5:00 The origin of "data intelligence" — 2016, ASG Technologies, and one research note6:44 GDPR as the catalyst — data governance vs. data intelligence8:11 Market adoption: Collibra, Erwin, Alation, Informatica, and more11:05 Databricks makes a splash — and Dave Kellogg weighs in13:39 IBM rebrands its portfolio to WatsonX Data Intelligence15:00 What it takes to successfully define a market category16:02 How data intelligence is evolving: semantics, active metadata, unstructured data19:13 Buy vs. build: how organizations assemble data intelligence capabilities23:32 Agentic AI and why data intelligence matters more than ever27:27 "Shift left" — data quality must happen at the source for real-time AI29:14 Cracking the unstructured data quality problem31:21 What CDOs are actually complaining about35:07 Where organizations are under-investing37:46 Data catalog adoption challenges — and how agentic AI can help
Listen on your preferred platform:YouTube playlist: https://www.youtube.com/playlist?list=PLzrDACjTQ4OBfdBJQiHax4oR1bXzs8JYYSpotify: https://open.spotify.com/show/3tFMqBPGioiMPxVJOmDPLjApple Podcasts: https://podcasts.apple.com/us/podcast/data-faces-podcast/id1779505301Amazon Music: https://music.amazon.com/podcasts/8465f3b3-5d41-4c84-a561-bf8af09560e3/data-faces-podcast
Connect with Stewart Bond:LinkedIn: https://www.linkedin.com/in/stewartlbond/
Connect with David Sweenor:Website: https://tinytechguides.comLinkedIn: https://www.linkedin.com/in/davidsweenor/
#DataIntelligence #DataGovernance #AgenticAI #DataManagement #DataFacesPodcast

"All the computer programs that have ever needed to be written have already been written." That's what Michael Meyer's guidance counselor told him in the late 1980s. 35 years later, he's still proving that advice wrong.In this episode of the Data Faces Podcast, host David Sweenor sits down with Michael Meyer, Solutions Engineer at Snowflake, to talk about the skill that carried him through every industry shift: storytelling. From creating a fictional character named "Walt the data janitor" to explain data governance, to building ML pipelines with vibe coding tools, Michael shares why the ability to make complex things understandable matters more than any single technology.Key Takeaways:1. Storytelling is the connective thread across every data role, from architecture to marketing to solutions engineering2. The semantic layer is a storytelling problem, and building a good one is still about 70% human work3. AI-assisted coding accelerates proof of concepts, but judgment about what the numbers mean is what separates useful work from dangerous work4. Early career data professionals should start with data modeling and fundamentals before chasing AI tools5. Getting out from behind the screen and learning from people matters as much as learning from platformsTimestamps: 00:00 - Opening and introduction 02:00 - Michael's background at Snowflake 04:00 - Joe's Brew Reviews and the storytelling instinct 06:30 - Walt the data janitor and internal marketing 11:00 - The mindset shock of product marketing 14:00 - Customer language and storytelling on a B2B web page 17:00 - Coming back to the technical side at Snowflake 19:00 - What is the semantic layer and why does it matter now? 23:00 - Facts, dimensions, metrics, and verified queries 25:30 - Building a semantic model: how much is human vs. AI? 28:30 - Vibe coding with Snowflake Cortex Code 32:00 - Career advice: fundamentals early career professionals need 34:30 - Find what energizes you and get out from behind the screen 35:30 - Craft beer recommendations and closing More insights and resources: Blog: [BLOG LINK] Connect with Michael Meyer: LinkedIn: https://www.linkedin.com/in/michael-meyer/ Drop your thoughts in the comments! Like, share, and subscribe for more insights. #DataCareers #SemanticLayer #DataFacesPodcast

📢 Most AI initiatives stall not because of weak models, but because of weak execution.

In this episode of the Data Faces Podcast, David Sweenor sits down with Asa Whillock, CEO of Euphonic AI, to unpack what it really takes to operationalize AI inside the enterprise.

With experience spanning Adobe, Alteryx, and now a growth-focused AI startup, Asa explains why production AI depends less on model hype and more on data access, system alignment, and disciplined leadership. If you’re responsible for turning AI experiments into measurable business outcomes, this conversation will sharpen your thinking.

🔍 Key Takeaways:

1- Production AI is about context — not just model capability

2- Vertical enterprise systems create horizontal friction for AI

3- Metadata and human decision logic are often the missing layers

4- “Boring” infrastructure work determines long-term AI success

5- ROI comes from aligning AI to the metrics that actually drive your business

⏳ Timestamps for Easy Navigation:

00:00 – Welcome & episode overview

02:00 – Redefining operationalizing AI

04:15 – Why enterprise AI struggles across silos

08:30 – Signals that AI is ready for production

12:45 – Structured vs. unstructured data

15:00 – The decisions leaders delay

18:00 – Differentiation vs. distraction

25:15 – Models vs. data: what matters more

29:20 – Why infrastructure determines success

32:30 – Finding real ROI in AI

34:20 – Final advice for AI leaders

📩 More insights & resources:

👉 https://www.tinytechguides.com

🔗 Connect with Asa Whillock:

💼 LinkedIn: https://www.linkedin.com/in/asawhillock/

🌎 Website: https://www.euphonic-ai.com/

💬 What’s the biggest barrier to operationalizing AI in your organization? Share your perspective in the comments.

👍 If this was valuable, like the video and subscribe for more conversations with leaders shaping data and AI.

#OperationalizingAI #EnterpriseAI #AILeadership

📢 AI governance is moving faster than most companies can control—and that gap is where risk shows up.In this episode of the Data Faces Podcast, Gene Arnold, Partner Sales Engineer at Atlan, breaks down what AI governance actually looks like in real organizations—not policy decks or theory, but decisions, tradeoffs, and failures teams face every day.David Sweenor and Gene explore how AI governance differs from data governance, why most AI projects never reach production, and how metadata, accountability, and testing determine whether AI becomes an asset or a liability.This conversation is for leaders who want AI to scale without surprises.🔍 Key Takeaways:1- Why AI governance is not just an extension of data governance2- How biased outcomes emerge even when models “work as designed”3- The hidden risks of moving fast without ownership or traceability4- Why metadata and semantic context matter more than models5- A practical starting point for governing AI without slowing teams down⏳ Timestamps for Easy Navigation:00:00 – Podcast intro & Gene Arnold background02:10 – From data catalogs to AI governance07:05 – Data governance vs AI governance explained11:56 – The overlooked role of unstructured data16:31 – Why most AI projects fail in production19:18 – Real-world AI governance failures (Amazon, facial recognition)26:45 – How to detect and manage bias in AI systems27:02 – Practical advice for getting started with AI governance31:06 – Accountability, metadata, and the semantic layer36:10 – Final thoughts on adopting AI responsibly📩 More insights & resources:👉 Blog recap and show notes:https://tinytechguides.com/blog/why-the-biggest-ai-enthusiasts-care-most-about-governance/🔗 Connect with Gene Arnold:💼 LinkedIn: https://www.linkedin.com/in/genearnold/💬 What governance challenges are you seeing with AI in your organization? Share your perspective in the comments.👍 If this was useful, like the video, subscribe, and share it with someone leading AI or data initiatives.#AIGovernance #DataLeadership #EnterpriseAI

📢 Most companies invest heavily in data and AI—yet few see real business impact. Why?
In this episode of Data Faces, David Sweenor sits down with Randy Bean to unpack four decades of lessons from the front lines of data, analytics, and AI leadership.

Randy shares insights from his long-running Fortune 1000 benchmark surveys, explains why culture—not technology—remains the biggest blocker, and outlines what separates effective data leaders from those who struggle to deliver value.

This conversation is practical, candid, and aimed squarely at executives responsible for turning AI ambition into operational results.

🔍 Key Takeaways:
1- Why the Chief Data Officer role has expanded—but still struggles
2- How AI has reshaped executive attention on data foundations
3- The difference between defensive and offensive data leadership
4- Why culture and organizational readiness matter more than tools
5- What a value-first data and AI strategy actually looks like

⏳ Timestamps for Easy Navigation:
00:00 – Welcome to Data Faces & guest introduction
00:53 – Randy Bean’s career path into data and analytics
03:24 – The origin and impact of the Data & AI Leadership Survey
05:08 – What’s advanced—and what’s stalled—in CDO roles
10:51 – Why culture, not technology, blocks AI adoption
14:08 – GenAI adoption: hype vs. real progress
17:23 – AI’s renewed focus on data quality and foundations
21:43 – What separates effective data leaders from the rest
27:59 – Business vs. technical leadership in data roles
30:27 – What a value-first data strategy looks like
33:51 – Where to find Randy’s research and writing

📩 More insights & resources:
👉 Blog & episode recap: https://tinytechguides.com/blog/culture-eats-ai-for-breakfast/

🔗 Connect with Randy Bean:
💼 LinkedIn: https://www.linkedin.com/in/randybeannvp/
🌎 Website & research: https://randybeandata.com

💬 What resonated most with you from this conversation? Share your take in the comments.
👍 If this was useful, like the video, subscribe, and follow Data Faces for more leadership conversations.

#DataLeadership #AILeadership #ChiefDataOfficer

📢 AI is everywhere—but what’s real, what’s hype, and where is the business value actually coming from?In this episode of the Data Faces Podcast, David Sweenor sits down with Tom Davenport, Distinguished Professor at Babson College and one of the most trusted voices in analytics and AI. They unpack where AI is delivering durable value today, why generative AI may be overvalued, and what leaders should realistically expect as we move through 2025 and into 2026.This is a grounded conversation for executives and practitioners who want clarity—not speculation—on how AI is reshaping work, decision-making, and enterprise strategy.🔍 Key Takeaways:1- Why generative AI is overhyped—and where real value still exists2- What most organizations misunderstand about agentic AI today3- The shift from individual AI use to enterprise-level impact4- Why disciplined experimentation matters more than pilots5- How AI is quietly changing workflows, not just tools⏳ Timestamps for Easy Navigation:00:00 – Welcome & introduction to Tom Davenport02:10 – What’s real vs hype in AI today04:20 – Are we in an AI bubble?05:50 – Agentic AI: real use cases vs experimentation07:00 – Is generative AI analytics “on steroids”?08:55 – One underestimated AI shift coming by 202610:59 – Where enterprise AI value will show up first13:20 – Why generative AI requires new disciplines17:15 – Jobs, education, and the limits of AI predictions28:10 – Governance vs enablement in AI30:55 – The positive case: AI, workflows, and business change32:35 – Final thoughts📩 More insights & resources:👉 Blog & episode write-up: https://tinytechguides.com/blog/why-boring-ai-use-cases-will-win-in-2026/🔗 Connect with Tom Davenport:💼 LinkedIn: https://www.linkedin.com/in/davenporttom/💬 What’s your take—where do you see real AI value today? Drop your thoughts in the comments.👍 If this conversation was useful, like, share, and subscribe for more practical insights on AI, data, and analytics leadership.#AILeadership #Analytics #BusinessValue