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:Data quality was never fixed because there were no consequences for getting it wrong. AI agents changed that equation.Enterprise agentic adoption is in its earliest days. Customers are experimenting, but production-grade agents are rare.Qlik's Trust Score for AI gives decision-makers a quantifiable measure of data quality before it reaches an agent."Dashboards are dead" as a destination, but the data and decisions they inform are more important than ever.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 era6:08 Where agentic adoption actually stands 7:46 Future-proofing against LLM shifts 8:24 The analytics engine and unknown unknowns10:29 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 readyTimestamps: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 helpListen 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-podcastConnect 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 capability2- Vertical enterprise systems create horizontal friction for AI3- Metadata and human decision logic are often the missing layers4- “Boring” infrastructure work determines long-term AI success5- ROI comes from aligning AI to the metrics that actually drive your business⏳ Timestamps for Easy Navigation:00:00 – Welcome & episode overview02:00 – Redefining operationalizing AI04:15 – Why enterprise AI struggles across silos08:30 – Signals that AI is ready for production12:45 – Structured vs. unstructured data15:00 – The decisions leaders delay18:00 – Differentiation vs. distraction25:15 – Models vs. data: what matters more29:20 – Why infrastructure determines success32:30 – Finding real ROI in AI34:20 – 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 struggles2- How AI has reshaped executive attention on data foundations3- The difference between defensive and offensive data leadership4- Why culture and organizational readiness matter more than tools5- What a value-first data and AI strategy actually looks like⏳ Timestamps for Easy Navigation:00:00 – Welcome to Data Faces & guest introduction00:53 – Randy Bean’s career path into data and analytics03:24 – The origin and impact of the Data & AI Leadership Survey05:08 – What’s advanced—and what’s stalled—in CDO roles10:51 – Why culture, not technology, blocks AI adoption14:08 – GenAI adoption: hype vs. real progress17:23 – AI’s renewed focus on data quality and foundations21:43 – What separates effective data leaders from the rest27:59 – Business vs. technical leadership in data roles30:27 – What a value-first data strategy looks like33:51 – 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

📢 2025 was the year AI met the real world. No demos. No hype. Just results—and hard lessons.In this special Data Faces year-in-review episode, we synthesize insights from 27 conversations with leaders across data, analytics, and AI to surface what actually mattered in enterprise adoption.Rather than new models or bigger tools, the story of 2025 centered on strategy, operational maturity, agent management, and the human realities behind AI at scale. This episode distills a full year of dialogue into one clear narrative for data leaders who need signal, not noise. 🔍 Key Takeaways:1- Why most GenAI projects failed—and it wasn’t the technology2- How AI agents shifted from novelty to core infrastructure3- What governance looks like when speed still matters4- Where AI delivered value: narrow, unglamorous, high-impact work5- Why culture, alignment, and ethics became the real constraints⏳ Timestamps for Easy Navigation:00:00 – Opening & scope of the 2025 review01:10 – Strategy over technology: where projects broke down03:05 – AI agents move from tools to infrastructure04:35 – Real enterprise value in narrow workflows05:19 – Culture, alignment, and human failure modes06:09 – Ethics, fairness tradeoffs, and real-world consequences07:58 – Adoption shifts and governance as a value driver09:06 – Managing agents at scale10:02 – Automation that makes people better, not obsolete11:09 – What 2025 teaches us going forward📩 More insights & resources:👉 https://tinytechguides.com/data-faces-podcast/🔗 Connect with Data Faces:🌎 Website: https://tinytechguides.com/data-faces-podcast/🎧 Subscribe on Spotify, Apple Podcasts, and YouTube💬 What resonated most from 2025—strategy, agents, or people?👍 If this was useful, like, share, and subscribe for future episodes.#DataFaces #EnterpriseAI #DataLeadership

📢 Can open source, AI, and enterprise analytics really coexist? Absolutely—and Bruno Trimouille from Posit is here to explain how.  How are open source tools reshaping enterprise data science? What role does AI play in bridging business and technical teams? In this episode, Posit CMO Bruno Trimouille breaks down how his team supports millions of users—while staying true to an open source mission.🎯 Whether you’re a data leader, marketer, or innovator, you’ll learn practical approaches to balancing innovation with governance, productizing models into apps, and using AI for both technical and marketing acceleration.🔍 Key Takeaways:1- Why a code-first approach delivers trust, transparency, and reproducibility2- How AI bridges the gap between business users and data scientists3- What Posit’s B2B open source flywheel model looks like behind the scenes4- Why governance doesn’t have to kill speed—in fact, it can enable scale5- How marketing teams can harness Gen AI for content, segmentation & insights⏳ Timestamps for Easy Navigation:00:00 – Intro & guest welcome00:52 – What is Posit? (formerly RStudio)02:06 – Bruno’s journey: Engineer to CMO04:53 – Open source, code-first, and the future of data science07:32 – AI’s impact on productivity and risk in analytics09:13 – Solving the governance vs. speed tension11:29 – Using models & apps to make insights business-ready13:13 – Building a business on open source: Posit’s flywheel15:31 – Why organizations bet on open data tools18:09 – Community-building as a growth engine21:31 – How Posit marketing uses Gen AI every day24:04 – AI for personalization, segmentation & ABM27:51 – Multimedia & interactive learning with Gen AI30:31 – Using AI for data insights & campaign analysis33:14 – How AI is reshaping the marketing org chart34:04 – Future of data-driven marketing leaders35:49 – Final thoughts from Bruno📩 More insights & resources:  👉 https://tinytechguides.com/blog/category/data-faces-podcast/  🔗 Connect with Bruno Trimouille:  💼 LinkedIn: https://www.linkedin.com/in/brunotrimouille  🌎 Website: https://posit.co💬 What do you think? Drop your thoughts in the comments!  👍 Enjoyed this video? Like, share & subscribe for more AI insights!#OpenSourceAI #DataScienceLeadership #ResponsibleAI

📢 Most AI failures don’t come from the model—they come from the data feeding it.In this episode of the Data Faces Podcast, Tina Chace, VP of Product Management at Solidatus, explains why incomplete lineage, missing context, and silent upstream changes quietly undermine AI systems long before anyone notices.Tina shares lessons from deploying AI and machine learning in major banks, breaking down how column-level lineage and business context prevent cascading failures across systems, teams, and decisions.🔍 Key Takeaways:1- Why 90% of AI production issues trace back to data quality problems.2- How technical and business lineage work together to build trust.3- Why column-level tracking exposes the hidden transformations behind every metric.4- How visibility without control increases anxiety across data teams.5- Where organizations should start to get quick wins without “boiling the ocean.”⏳ Timestamps for Easy Navigation:00:00 – Intro: David Sweenor introduces Tina Chace00:54 – Tina’s early career and the origins of her data skepticism02:28 – The 90% data problem in AI and ML deployments03:24 – What data lineage actually captures06:47 – The rounding-error problem that compounds at scale07:59 – Bridging the language gap across data, reporting, and business teams09:46 – Who really owns data quality and lineage?12:43 – Technical vs. business lineage, with real examples16:24 – Managing complexity across systems, teams, and tech stacks18:16 – Why documenting “everything” never works23:28 – Data lineage in generative AI and RAG systems30:57 – Why AI makes complete lineage non-negotiable33:26 – The trust paradox: more visibility, more skepticism34:27 – How to get started without boiling the ocean35:35 – Closing remarks📩 More insights & resources:👉 Blog: https://tinytechguides.com/blog/data-lineage-for-ai-why-truth-beats-hope-in-banking/🎧 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/id1789416487🔗 Connect with Tina Chace:LinkedIn: https://www.linkedin.com/in/tina-chace-rho-5433133b/Solidatus: https://www.solidatus.com💬 What’s the biggest data trust challenge in your organization? Tell us in the comments.👍 Like, share, and subscribe for more conversations with leaders shaping AI, analytics, and data strategy.#DataLineage #DataQuality #AITrust

📢 What happens when culture and technology collide—do they compete, or do they enhance one another?In this episode of Data Faces, Gina von Esmarch, Founder and CEO of Adesso Associates, shares how cultural heritage, storytelling, and emerging technologies work together to shape stronger communities and brands.🔍 Key Takeaways:1- How AI and cultural identity can evolve together, not apart.2- Why authenticity and values are the foundation of innovation.3- How diversity of thought drives long-term business success.📩 Watch the full episode here: https://www.youtube.com/watch?v=F8-j7nqzsGk👉 More insights: https://prompts.tinytechguides.com/s/the-data-faces-podcast💬 How do you see culture influencing technology in your industry?👍 Like, share & subscribe for more insights.#DataFaces #AI #CultureAndTech #Leadership #Innovation

📢 What happens when AI becomes your sales coach? In this episode, we explore how artificial intelligence is reshaping how sales teams learn, ramp, and perform.Join David Sweenor, Founder of TinyTechGuides, as he talks with Matt Magne, Senior Enablement Manager of Revenue Enablement at LaunchDarkly, about the new frontier of AI-driven sales enablement — from AI role plays to digital coaching and the future of human + machine collaboration.🔍 Key Takeaways:-- Why AI is redefining how SEs and AEs practice and master sales conversations-- How LaunchDarkly integrates AI role plays to accelerate ramp time and confidence-- The balance between automation and authentic human coaching-- The data challenges still holding back innovation in enablement-- Why the future of enablement is about augmentation, not replacement⏳ Timestamps for Easy Navigation:00:00 – Welcome to Data Faces with David Sweenor01:00 – Meet Matt Magne and LaunchDarkly06:10 – What revenue enablement really means08:25 – How AI role plays improve ramp and coaching11:00 – Do reps prefer training with bots or humans?14:00 – Space repetition, practice, and performance17:10 – Building hybrid AI + human onboarding programs21:00 – Overcoming skepticism about AI training26:00 – The “skeptical buyer” prompt and realistic simulations27:15 – Human + machine collaboration: finding the right balance31:20 – Beyond scale: personalization and productivity with AI34:50 – The future of sales enablement: data, integration, and intelligence39:30 – Final takeaway: staying human in an AI-driven world📩 More insights & resources:👉 Read more at: https://open.substack.com/pub/davidsweenor/p/augmented-intelligence-the-future?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true🔗 Connect with Matt Magne:💼 LinkedIn: https://www.linkedin.com/in/exrocker/🌎 LaunchDarkly: https://launchdarkly.com💬 What do you think — can AI make us better sales coaches? Drop your thoughts in the comments!👍 If you enjoyed this episode, like, share, and subscribe for more insights from Data Faces.#AIinSales #SalesEnablement #DataFacesPodcast

📢 What’s the real end game of AI? Is it about smarter machines—or smarter organizations?In this episode of Data Faces, Eric Kavanagh, AI analyst and syndicated radio host of DM Radio, joins David Sweenor, founder of TinyTechGuides, to explore what success in AI truly means for business, technology, and society.Eric has spent decades helping enterprises navigate the data-driven world. He argues that AI isn’t a monolith—it’s a tool that works best when humans stay in control, governance evolves, and organizations learn to use intelligence responsibly.🔍 Key Takeaways:1- Why the “end goal” of AI is human productivity, not machine autonomy2- How corporate hierarchies will evolve as AI becomes embedded in workflows3- Why transparency, audit logs, and explainability will define governance success4- The risks of “big model” complexity and why small, focused models may win5- Why AI will change how we work more than what we do⏳ Timestamps for Easy Navigation:00:00 – Welcome and introduction to Eric Kavanagh01:00 – How Eric built a career at the intersection of data and media05:20 – Defining the “end goal” of AI: business, life, and technology08:45 – Where humans remain central—and why that won’t change12:10 – Transparency, bias, and the challenge of giant AI models19:00 – Real-world AI agents: when automation works and when it doesn’t22:50 – The rise of small language models and the age of execution29:45 – Governance, audit logs, and minimal viable oversight33:00 – The future of jobs, creativity, and collaboration39:00 – Where to find Eric Kavanagh and DM Radio📩 More insights & resources:👉 Blog recap: https://tinytechguides.com/blog/why-80-of-ai-projects-fail-and-the-three-boring-decisions-that-save-the-other-20/🔗 Connect with Eric Kavanagh:💼 LinkedIn: https://www.linkedin.com/in/erickavanagh/🌎 Website: https://dmradio.biz💬 What do you think? What should the end game of AI be? Drop your thoughts below!👍 Enjoyed this conversation? Like, share & subscribe for more real-world AI and data insights.#AI #DataFacesPodcast #EricKavanagh

📢 AI agents are moving fast from hype to enterprise reality. But how do leaders ensure they deliver real ROI—without creating risk and chaos?In this episode of Data Faces, David Sweenor sits down with Catalina Herrera, Field Chief Data Officer at Dataiku, to explore how organizations can adopt AI agents responsibly. Catalina shares a practical framework for balancing speed, governance, and reliability—while keeping business impact front and center.🔍 Key Takeaways:1- Why “agent sprawl” is the biggest hidden risk for enterprises2- The guardrails every AI leader should put in place on day zero3- How bad data leads to bad agents—and what to do about it4- Practical patterns for scaling adoption with human-in-the-loop trust5- The next 12 months: where AI agents will make the biggest impact⏳ Timestamps for Easy Navigation:00:00 – Introduction and guest overview02:10 – Why AI agents matter now for business leaders07:25 – Governance guardrails every enterprise needs13:40 – Data foundations and making agents reliable19:15 – Avoiding agent sprawl and scaling adoption25:50 – Looking ahead: what’s next for AI agents30:20 – Final thoughts and closing📩 More insights & resources:👉 Blog recap: https://prompts.tinytechguides.com/s/the-data-faces-podcast🔗 Connect with Catalina Herrera:💼 LinkedIn: https://www.linkedin.com/in/herreracatalina/🌎 Dataiku: https://www.dataiku.com/💬 What do you think? How are you preparing for AI agents in your organization? Drop your thoughts in the comments!👍 If you enjoyed this conversation, remember to like, share, and subscribe for more episodes of Data Faces.#AIagents #AIGovernance #DataScience

📢 What if your marketing campaigns could adapt themselves in real time—without you lifting a finger?In this episode of Data Faces, David Sweenor talks with Rajeev Kozhikkattuthodi, Co-founder & CEO at Poexis, about how Agentic AI is reshaping growth marketing across events, ABM, and inbound.Rajeev shares practical lessons from the front lines: where Agentic AI actually delivers ROI, how to avoid the trap of “AI that doesn’t scale,” and why marketing leaders need a bias to action—not just analysis.🔍 Key Takeaways:1- What makes Agentic AI different from traditional AI in B2B marketing2- How to set the right level of autonomy and guardrails for AI systems3- Why many AI pilots stall and how to prove ROI at the P&L level4- The new role of in-person experiences in an AI-saturated digital world5- Leadership skills marketers need to thrive in an Agentic AI future⏳ Timestamps for Easy Navigation:00:00 – Intro & Rajeev’s background03:00 – What Agentic AI really means for marketers05:40 – Shifting from analysis to agency10:20 – Are humans ready to cede control to AI?14:40 – Why 95% of AI pilots stall20:20 – Agentic AI in events: boosting attendance & engagement27:30 – Personalization at events: pre, during & post strategies29:30 – Using AI for inbound content that still feels human34:50 – What marketing leaders must do to stay ahead37:00 – How to connect with Rajeev & Poexis📩 More insights & resources:👉 Blog recap: https://open.substack.com/pub/davidsweenor/p/the-ai-agent-mistake-90-of-marketing?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true🔗 Connect with Rajeev Kozhikkattuthodi:💼 LinkedIn: https://www.linkedin.com/in/rajeevtk/🌎 Website: https://www.poexis.com💬 What do you think—are marketers ready to move from analysis to action with AI? Drop your thoughts in the comments!👍 Enjoyed this conversation? Like, share & subscribe for more AI and data leadership insights.#AgenticAI #B2BMarketing #DataFacesPodcast

📢 AI is more than GenAI—and sustainable success depends on governance, teamwork, and evolution, not disruption.In this episode of Data Faces, David Sweenor sits down with Thomas Been, CMO at Domino Data Lab, to unpack how enterprises can move beyond hype and build real, lasting value with AI. From governance frameworks to the human factor, Thomas shares practical insights that every technology and business leader should hear.🔍 Key Takeaways:1- Why 95% of GenAI projects fail—and what successful enterprises do differently2- How to balance experimentation with governance to accelerate adoption3- Why teamwork and culture drive AI success more than technology alone4- The spectrum of AI: why GenAI is only part of the picture5- How leaders can prepare for continuous evolution instead of chasing disruption⏳ Timestamps for Easy Navigation:00:00 – Welcome & Guest Intro (Thomas Been, CMO, Domino Data Lab)01:05 – Thomas’s career journey & mission at Domino03:00 – The GenAI hype cycle: why most projects fail06:44 – AI vs. GenAI: spectrum, not a silver bullet10:39 – Evolution vs. disruption in enterprise AI14:17 – Rethinking governance: from compliance to value driver18:59 – Global perspectives on AI governance23:03 – What separates successful AI projects from failures27:41 – How AI is transforming marketing & marketing teams32:55 – The impact of AI on early-career professionals35:43 – Advice for business leaders on future-proofing AI strategy37:54 – Where to learn more about Domino Data Lab📩 More insights & resources:👉 Read the blog: https://prompts.tinytechguides.com/p/the-11-month-ai-validation-trap-thats🔗 Connect with Thomas Been:💼 LinkedIn: https://www.linkedin.com/in/tbeen/🌎 Website: https://domino.ai💬 What do you think? How is your company approaching AI governance and teamwork? Share in the comments!👍 Enjoyed this video? Like, share & subscribe for more insights on AI, data, and leadership.#EnterpriseAI #AIGovernance #DataFacesPodcast

📢 What happens when AI agents take on real tasks in marketing, sales, and operations? In this episode, Chelsea Wise of Relevance AI shares practical insights on how AI agents are transforming the future of work.Chelsea draws on her startup experience, academic background in consumer behavior, and role at Relevance AI to explain what leaders often underestimate about AI agents—and how to prepare teams for change.🔍 Key Takeaways:1- Why AI agents will shift work at the task level rather than replacing entire jobs.2- The overlooked role of peer-to-peer learning in building trust in AI.3- Practical, “unsexy” use cases where AI agents deliver real business value.4- How hackathons and hands-on learning can unlock organizational buy-in.5- Why critical thinking and ethics must guide AI adoption.⏳ Timestamps for Easy Navigation:00:00 – Introduction & Chelsea Wise background04:15 – The underestimated impact of AI agents in the workplace07:12 – Learning, trust, and human-to-human collaboration12:00 – What AI agents do well vs. where humans remain essential18:00 – Rethinking the marketing playbook with AI20:42 – Lessons from internal hackathons25:29 – Misconceptions about AI in enterprise marketing28:36 – Real-world “spreadsheet” use cases for AI agents32:21 – Preparing teams and leaders for AI agents34:17 – Teaching AI ethics and critical thinking38:11 – Final thoughts & resources📩 More insights & resources:👉 Blog recap: https://prompts.tinytechguides.com🔗 Connect with Chelsea Wise:💼 LinkedIn: https://www.linkedin.com/in/chelseawise/🌎 Website: https://relevance.ai💬 What do you think—are AI agents ready for your team? Drop your thoughts in the comments!👍 If you enjoyed this video, like, share & subscribe for more conversations on AI, data, and leadership.#AIAgents #FutureOfWork #DataFacesPodcast

📢 Are you losing customers because your product messaging is too complex? In this episode of Data Faces, I sit down with Emma Stratton, founder of Punchy and expert in B2B SaaS storytelling, to talk about how to cut through the jargon, keep your message simple, and connect with your audience.Emma shares her proven framework for making marketing more “punchy,” and why simplicity is the secret weapon for winning in crowded markets. Whether you’re a founder, marketer, or product leader, this conversation is packed with insights you can put into action right away.🔍 Key Takeaways:1- Why complexity kills B2B SaaS messaging—and what to do instead2- How to find the right “altitude” in your messaging for different audiences3- Practical tips to make your writing more human without “dumbing it down”4- The role of emotion in B2B buying decisions5- How to adapt messaging in the age of AI while keeping your brand voice⏳ Timestamps for Easy Navigation:00:00 – Introduction & guest welcome00:48 – What Punchy does and why simple messaging matters02:22 – The curse of knowledge and overcomplication in B2B marketing06:37 – What “punchy” means in SaaS messaging08:38 – Overcoming the fear of sounding too simple10:52 – Finding the right “altitude” for your audience14:19 – Adapting messaging for different personas16:22 – AI’s impact on brand voice and sounding human21:28 – Why messaging by committee dilutes impact22:17 – How to test if your message resonates before launch24:36 – Common blind spots in product messaging27:11 – The myth of category creation for startups30:37 – Using emotion to connect with B2B buyers33:47 – Final advice for companies struggling with jargon📩 More insights & resources:👉 Read the blog recap: https://open.substack.com/pub/davidsweenor/p/how-to-write-punchy-b2b-messaging?r=1s6e48&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true🔗 Connect with Emma Stratton:💼 LinkedIn: https://www.linkedin.com/in/emma-stratton-punchy/🌎 Website: https://www.punchy.co💬 What’s the biggest challenge you face with your product messaging? Drop your thoughts in the comments!👍 Enjoyed this conversation? Like, share & subscribe for more insights on AI, analytics, and business leadership.#B2BMarketing #SaaSMarketing #MessagingStrategy

📢 Can AI agents really transform the enterprise—or is it just more buzz?In this episode of Data Faces, Rich Mendis, CMO at Bytemethod.ai, shares how agentic AI is (and isn’t) being used inside modern enterprises.Rich brings deep insight from real deployments and tackles the common myths about AI replacing human roles, the technical hurdles companies face, and how to assess true enterprise readiness for automation.Whether you're a tech strategist, B2B leader, or data professional trying to cut through the AI noise—this conversation delivers clarity, use cases, and practical wisdom.🔍 Key Takeaways:What AI agents actually are—and where they’re showing up in the enterpriseWhy trust, training data, and governance are make-or-break factorsCommon misconceptions about plug-and-play AI in B2BThe shift from AI replacing humans to AI complementing themHow to build a culture of experimentation with "risk-mitigated freedom"⏳ Timestamps for Easy Navigation:00:00 – Intro and welcome01:14 – Rich’s background and journey into AI02:21 – What is an AI agent, really?03:53 – Use cases for AI in marketing and operations05:57 – Misconceptions about AI capabilities in enterprise08:16 – Design-time vs. runtime AI use cases13:00 – Governance and validation of agent outputs16:14 – The hidden challenges with enterprise data quality19:18 – Cultural and technical readiness for AI adoption22:23 – How AI is changing roles and skills in business24:26 – Layoffs, automation myths, and the truth about efficiency29:55 – Why trust and data provenance matter more than ever31:46 – What remains uniquely human in a world of AI33:48 – Final thoughts and how to get started with Bytemethod.ai📩 More insights & resources:👉 Read the blog recap on prompts.tinytechguides.com🔗 Connect with Rich Mendis:💼 LinkedIn: https://www.linkedin.com/in/rmendis🌎 Website: https://bytemethod.ai💬 What do you think? Drop your thoughts in the comments!👍 Enjoyed this video? Like, share & subscribe for more AI and analytics insights!#AIinEnterprise #AIagents #DataFacesPodcast

📢 Is AI just adding noise—or unlocking real competitive advantage?In this episode of Data Faces, David Bryson, Principal Competitive Intelligence Manager at Splunk, breaks down how AI is changing the landscape of competitive intelligence and what it means for analysts, marketers, and business leaders alike.Whether you're building a CI function or trying to turn data into action, this conversation reveals how to move from information gathering to actual intelligence—with the help of AI.🔍 Key Takeaways:Why “so what?” is the question every CI analyst should askHow AI tools like deep research can supercharge strategic thinkingThe danger of over-trusting generic AI outputs in CI workflowsWhy prompt engineering is the new CI superpowerWhat the next generation of CI professionals need to succeed⏳ Timestamps for Easy Navigation:00:00 – Intro and welcome01:00 – Dave’s path from sales engineering to CI03:15 – How AI is changing intelligence gathering05:45 – Separating signal from noise in AI-generated research10:00 – Can AI replace the human side of CI?12:30 – Deep research, prompt strategy, and personal AI workflows16:00 – Sharing prompts and building internal CI knowledge17:10 – Rethinking feature-function comparisons in an AI-first world22:00 – Moving from reactive to proactive CI with AI27:10 – Turning information into true intelligence30:30 – What future CI professionals need to thrive34:30 – Advice for breaking into competitive intelligence📩 More insights & resources:👉 https://prompts.tinytechguides.com🔗 Connect with David Bryson:💼 LinkedIn: https://www.linkedin.com/in/david-bryson🌎 Splunk: https://www.splunk.com💬 What was your biggest insight from this episode? Drop it in the comments!👍 If you enjoyed the conversation, don’t forget to like, subscribe, and share for more practical AI and data insights.#CompetitiveIntelligence #AIinBusiness #DataFacesPodcast

📢 Is AI just a tool—or a mirror forcing us to rethink how we lead, decide, and communicate?In this episode, Judit Szabo, Global Head of Demand Generation and Operations at Endava, shares how AI is not just changing workflows—it’s reshaping the human side of business.From marketing operations to leadership mindset, Judit dives into how AI is pushing B2B organizations to confront assumptions, strengthen critical thinking, and rediscover what makes us uniquely human.🔍 Key Takeaways:1– Why AI is a wake-up call for leadership and decision-making2– The human skills becoming more valuable in an AI-driven world3– How demand gen and marketing ops must evolve with intelligent automation4– Where AI falls short—and why human context still matters5– How to lead AI adoption without fear or over-reliance⏳ Timestamps for Easy Navigation:00:00 – Intro & welcome01:25 – Why AI is more than just a productivity tool06:40 – Redefining demand generation in the AI era12:10 – The new value of human judgment and soft skills19:30 – How AI changes the role of operations leaders27:45 – Leading teams through transformation34:00 – Final thoughts and where to go from here📩 More insights & resources:👉 [Blog post or resource link here]🔗 Connect with Judit Szabo:💼 LinkedIn: https://www.linkedin.com/in/juditszabocloud/🌎 Website: https://www.endava.com💬 What do you think? Drop your thoughts in the comments!👍 Enjoyed this conversation? Like, share & subscribe for more expert takes on AI, analytics, and business leadership.#AILeadership #B2BSaaS #DataDrivenDecisions

📢 Are AI “hallucinations” just bad framing? Discover why agentic quality—not accuracy alone—will decide which AI products win.Generative and agent-based AI are changing how every team researches, plans, and executes. In this fast-paced talk, Hyoun Park, CEO & Principal Analyst at Amalgam Insights, joins David Sweenor to break down what agentic quality means, why “hallucinations” is the wrong metaphor, and how to keep thousands of enterprise agents from running wild. Perfect for data leaders and curious execs who want pragmatic, hype-free answers.🔍 Key Takeaways:1- Agentic quality measures goal-oriented reasoning and timeliness—metrics most benchmarks ignore.2- “Hallucination” anthropomorphizes AI; call it model mismatch and fix the data or prompt instead.3- Synthetic data and vector stores are table stakes; trust and governance will decide adoption.4- Expect agent sprawl: thousands of mini-workflows that demand new ownership and monitoring.5- Within five years, every white-collar role will spend ~25 % of its time orchestrating AI.⏳ Timestamps for Easy Navigation:00:00 – Intro & show setup01:00 – Hyoun’s analyst journey and Amalgam Insights mission03:30 – What AI agents actually do beyond meeting scheduling07:00 – Will agents make us mentally lazy? Human learning vs. automation11:30 – Defining agentic quality and why accuracy isn’t enough18:00 – Retiring the term “hallucination” and reframing model errors24:30 – Managing agent sprawl and ownership at scale29:00 – Synthetic data, vector DBs & the real impact on quality33:00 – Creative AI: music, art, and the future of human craft36:00 – The missing capability—establishing cross-agent trust37:30 – Final thoughts & episode wrap-up📩 More insights & resources:👉 Read Hyoun’s latest posts: https://amalgaminsights.com/🔗 Connect with Hyoun Park:💼 LinkedIn: https://www.linkedin.com/in/hyounpark/🌎 Website: https://amalgaminsights.com/💬 What resonated most? Tell us in the comments!👍 If you learned something useful, give us a like, share with a colleague, and subscribe for more weekly AI strategy talks.#AgenticAI #DataAnalytics #ArtificialIntelligenceSee more at https://prompts.tinytechguides.com/s/the-tinytechguides-chronicle

📢 Can we really trust AI without trustworthy data?Field CTO Shane Murray of Monte Carlo Data shares what “AI-ready” actually means, and why most data teams are underprepared for the shift to generative AI.In this episode, we explore the practical and philosophical challenges behind building data products that can power AI applications — from defining quality in unstructured data to the ripple effects of small changes in AI systems. Shane draws on his experience leading data at The New York Times and now helping organizations scale observability and governance at Monte Carlo Data.🔍 Key Takeaways:Why the term “AI-ready” is often misunderstood — and what it really takesHow unstructured data quality and observability differ from traditional structured approachesThe hidden risks of hallucinations, model drift, and multi-agent errorsWhy governance can’t be “pumped in” after the fact — it must be designed in from the startA pragmatic path for data teams: start small, keep humans in the loop, and build what matters⏳ Timestamps for Easy Navigation:00:00 – Intro & Shane Murray’s background03:23 – What does “AI-ready” actually mean?07:54 – Measuring quality in unstructured data12:43 – The hidden causes of AI hallucinations18:23 – Multi-agent systems and compounding errors20:31 – Rethinking AI governance in enterprise environments25:35 – Can we ever truly trust AI?30:45 – The future of trustworthy AI systems34:38 – Shane’s advice to data teams and where to start📩 More insights & resources:👉 [Link to blog post or Substack recap here]🔗 Connect with Shane Murray:💼 LinkedIn: https://www.linkedin.com/in/shanemurray5/🌎 Website: https://www.montecarlodata.com💬 What stood out to you most? Let us know in the comments.👍 Like this episode? Subscribe and share for more conversations on data, AI, and analytics leadership.#AIReadyData #DataGovernance #TrustworthyAI

📢 What if the biggest reason your AI projects struggle isn’t the tech—but the people?In this episode of Data Faces, Danny Stout, Product Lead at EY and seasoned AI strategist, shares why alignment, communication, and soft skills matter more than ever in data and AI teams.From building the right team structure to navigating internal politics, Danny brings practical insights from his decades-long career leading data and analytics transformation efforts across major enterprises.🔍 Key Takeaways:1- Bigger AI models aren’t always better—context and alignment win.2- Organizational misalignment kills great tech before it starts.3- Communication and soft skills are now essential for AI success.4- AI teams need more than tech talent—they need translators and connectors.5- Simple solutions often outperform flashy tools in real-world AI use.⏳ Timestamps for Easy Navigation:00:00 – Intro & guest welcome01:14 – Danny’s career path: from education to AI leader03:47 – Why tech obsession blinds us to the human element06:00 – The critical role of executive alignment10:22 – Who should you hire first on an AI team?14:52 – How GenAI is changing the skills landscape17:56 – The growing importance of soft skills20:59 – Why diversity supercharges team effectiveness21:54 – Real-world story: Predicting human behavior with data25:05 – Common blind spots in AI projects27:22 – Understanding and applying guardrails in GenAI28:33 – What GenAI tools Danny is building now30:19 – Advice for aspiring data & AI leaders33:57 – Final tips + where to follow Danny📩 More insights & resources:👉 [Insert blog/resource link here]🔗 Connect with Danny Stout:💼 LinkedIn: https://www.linkedin.com/in/dannystout/💬 What resonated most with you? Drop a comment below!👍 Like, share, and subscribe for more conversations on the people shaping AI and data!#AILeadership #AnalyticsCulture #DataStrategy #GenerativeAI #SoftSkillsInTech #AIteams #TechLeadership

📢 Tired of B2B messaging that sounds like everyone else’s? You’re not alone.Product marketing consultant Gabriela Contreras joins Data Faces to unpack why most B2B messaging falls flat—and how to actually make yours resonate.If you're a product marketer, GTM leader, or startup founder trying to sharpen your story and cut through the noise, this episode is packed with real-world strategies and frameworks you can use immediately.🔍 Key Takeaways:The #1 messaging mistake B2B SaaS companies make (hint: trying to talk to everyone).How to align messaging across product, marketing, and sales without chaos.A simple framework (VBF: Value–Benefit–Feature) that transforms how you write copy.What product marketers can do to capture the voice of the customer—without massive VOC programs.How to evolve your messaging as your product matures and the market shifts.⏳ Timestamps for Easy Navigation:00:00 – Intro & welcome01:00 – Gabriela's background and approach02:15 – The biggest messaging mistake in B2B SaaS04:40 – Navigating multiple personas and tailoring messaging07:15 – Standing out in a saturated SaaS landscape10:45 – Getting PMMs closer to the customer13:40 – Cross-team alignment on messaging19:20 – Case study: Translating tech into clarity24:00 – Value > Benefit > Feature explained28:30 – Emotional resonance vs. business speak31:00 – Evolving messaging with the market35:30 – Final advice: Your product is NOT the hero37:00 – How to connect with Gabriela📩 More insights & resources:👉 https://www.skyline.marketing🔗 Connect with Gabriela Contreras:💼 LinkedIn: https://www.linkedin.com/in/mgcontreras/💬 What resonated with you most? Drop your thoughts in the comments!👍 Enjoyed this episode? Like, share & subscribe for more stories behind the data.#B2BMarketing #ProductMarketing #SaaSStrategy #MessagingFrameworks #VoiceOfCustomer #StartupMarketing

📢 Can you really trust AI—or are we putting too much faith in a system we barely understand?In this episode of Data Faces, we sit down with executive advisor Robert Lake to explore what it truly means to trust AI in business—and why it starts with people, not just the tech.Robert draws on 30+ years of experience in data science and strategic advising to break down the hidden challenges of building AI that organizations (and humans) can confidently rely on. From governance to explainability to emotional dependency on machines—this is the human side of AI you don’t want to miss.🔍 Key Takeaways:1- Why trust in AI is often misplaced—and how to fix that2- The myth of AI "reasoning" and the dangers of anthropomorphizing tech3- How business leaders can evaluate the intentions behind AI systems4- Why core values—not compliance checklists—drive ethical AI adoption5- How to build long-term “AI habits” that align with real business goals⏳ Timestamps for Easy Navigation:00:00 – Introduction & what trust in AI really means02:23 – Robert’s background in data science & business exits05:14 – Probabilistic AI vs. executive desire for certainty07:55 – Humanizing machines: the real risk behind emotional AI11:53 – Evaluating intentions: what the “trust module” really checks17:33 – Explainability, accountability, and who owns the system21:12 – Building AI habits: Lessons from Lean Six Sigma23:31 – Do we need new trust frameworks in AI?29:52 – AI maturity in executive teams & why less tech is more34:38 – Organizational change: Why “start with why” still matters37:17 – Final advice: “What would your grandma say?”38:36 – How to connect with Robert📩 More insights & resources:👉 https://treboradvisors.com🔗 Connect with Robert Lake:💼 LinkedIn: https://www.linkedin.com/in/rjlake🌎 Website: https://treboradvisors.com💬 What’s your take on AI trust? Drop a comment below!👍 If this sparked your thinking, like, share & subscribe for more expert insights on AI, data, and business strategy.#ArtificialIntelligence #TrustInAI #DataEthics #AILeadership #BusinessStrategy

📢 Is Gen AI revolutionizing product marketing—or just flooding the market with generic content?In this episode of Data Faces, David Sweenor sits down with Melissa Burroughs, Director of Product Marketing at Alteryx, to unpack how generative AI is transforming the PMM role. From boosting productivity to raising ethical questions, Melissa shares real-world insights, personal lessons, and future-forward advice for marketers navigating the Gen AI shift.Whether you're a seasoned PMM or just getting started with AI, this episode is packed with valuable takeaways on strategy, trust, and thriving in an AI-driven landscape.🔍 Key Takeaways:1-Gen AI is supercharging PMM productivity—but it raises the bar for quality and strategy.2-The human touch—collaboration, expertise, and judgment—is more valuable than ever.3-Ethical use of AI requires transparency, accountability, and a BS filter.4-PMMs must balance AI efficiency with brand voice and customer trust.The PMM role is evolving—fewer junior roles, greater strategic expectations.⏳ Timestamps for Easy Navigation:00:00 – Intro & Melissa’s journey from physics to product marketing03:45 – How Gen AI is transforming the PMM role07:15 – The rise of collaboration and human-centric skills10:10 – When AI makes PMMs less effective12:00 – Why subject-matter expertise matters more than ever18:30 – Balancing AI productivity with brand authenticity23:00 – Ethical concerns: transparency, privacy & bias28:50 – The future of PMM roles in a Gen AI world33:40 – Should AI help with strategy instead of just tasks?34:50 – Advice for PMMs starting their Gen AI journey36:05 – Why protecting data matters more than ever38:00 – Final thoughts: AI as a partner, not a replacement📩 More insights & resources:👉 Explore helpful AI prompts & tools: https://prompts.tinytechguides.com🔗 Connect with Melissa Burroughs:💼 LinkedIn:   / melissaburroughs  🌎 Learn more about Alteryx: https://www.alteryx.com#genai #ProductMarketing #AIForBusiness#tinytechguides #DataFacesPodcast

How are autonomous AI agents reshaping enterprise operations? In this episode of The Data Faces Podcast, John Thompson, Global AI Leader at EY, cuts through the hyperbole to provide a strategic assessment of opportunities, risks, and governance requirements based on his decades of implementation experience.🔍 Key Topics Covered:1- The fundamental shift from analytical AI to operational agents2- Why the capabilities that make agents powerful also make them risky3- The inevitable consolidation of today's fragmented agent framework landscape4- The critical importance of appropriate agent governance mechanisms5- Realistic value expectations versus "utopian automation scenarios"⏳ Timestamps for Easy Navigation:00:33 - John's 38-year journey through data, analytics and AI02:40 - The dual-edged nature of AI agent capabilities04:58 - The current state of AI agent maturity05:35 - Why 150 agent frameworks will consolidate like the early auto industry06:22 - Competing visions: Microsoft's closed platform vs. Google's open ecosystem10:13 - From RPA to intelligent process automation12:07 - The human element as the primary risk factor14:28 - The need for an agent management platform16:54 - Operational risks of inadequate agent governance18:46 - Wrapping agents with organizational context21:20 - Why AGI remains decades or centuries away23:44 - The importance of transparency in customer interactions25:48 - The fallacy of "utopian automation scenarios"30:53 - Using AI to monitor AI-generated content34:13 - Why there is no "post-AI world"34:33 - Resources for building organizational AI competency💡 More AI & Data Insights:🌐 Explore our analysis: https://prompts.tinytechguides.com/p/there-is-no-post-ai-world-preparing📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com🔗 Connect with John Thompson:💼 LinkedIn: https://www.linkedin.com/in/johnkthompson/🌎 Learn about EY's AI initiatives: https://www.ey.com/en_us/artificial-intelligence👍 Enjoyed this strategic discussion? Like, share, and subscribe for more executive insights on enterprise AI!#AIAgents #EnterpriseAI #AIGovernance

📌 AI & Analytics in Finance: The Future of CFOs | Jawwad RasheedHow are AI and analytics reshaping finance? In this episode of The Data Faces Podcast, Jawwad Rasheed, Financial Services & Transportation Lead at Alteryx, explores how finance leaders can leverage AI, analytics, and automation to drive transformation.🔍 Key Topics Covered:1-How AI is changing the finance function2-The shift from finance controllers to strategic business partners3-The impact of automation & self-service analytics on finance teams4-Why CFOs must embrace AI-driven decision-making5-The rise of continuous accounting & finance as a service⏳ Timestamps for Easy Navigation:00:00 – Introduction & Meet Jawwad Rasheed02:00 – How AI & Analytics Are Transforming Finance07:45 – The Evolution of CFOs: From Controllers to Strategists11:50 – Self-Service Analytics & Automation in Finance18:00 – Balancing Governance & Risk in Financial AI25:30 – The Role of Leadership in Finance Transformation31:00 – The Future of Finance: AI, Continuous Accounting & More39:00 – Final Thoughts & Advice for Finance Leaders💡 More AI & Data Insights:🌐 Explore TinyTechGuides: www.tinytechguides.com📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com🔗 Connect with Jawwad Rasheed:💼 LinkedIn: https://www.linkedin.com/in/jawwad-rasheed/🌎 Alteryx Website: https://www.alteryx.com👍 Enjoyed this discussion? Like, share, and subscribe for more AI insights!#FinanceAI #CFOTrends #JawwadRasheed #AIinFinance #AnalyticsforCFOs

📌 AI Agents & Data Strategy | Sanjeev MohanAI agents are transforming business and data strategy, but are they ready for real-world deployment? In this episode of The Data Faces Podcast, Sanjeev Mohan , former Gartner VP and AI expert, joins us to discuss the rise of AI agents, their role in enterprise AI, and key challenges in AI governance.🔍 Topics Covered in This Episode:What are AI agents, and how do they differ from traditional AI models?The current state of AI governance, enterprise AI, and automationHow businesses can leverage AI for competitive intelligenceThe challenges of AI hallucination, reliability, and ethical concernsThe future of AI in decision-making and its impact on leadership⏳ Timestamps for Easy Navigation:00:00 – Introduction & Meet Sanjeev Mohan02:15 – AI Agents: Definition & Business Use Cases07:00 – Are AI Agents Overhyped? Real vs. Fiction09:50 – AI in Competitive Intelligence & Business Strategy14:30 – The Role of Data Strategy in AI Agents18:00 – AI Hallucination & Reliability Challenges22:30 – Human vs. AI Decision-Making in the Enterprise27:00 – AI Regulation & Governance: What Leaders Need to Know35:00 – Expert Advice for Tech & Business Leaders]s💡 More AI & Data Insights:🌐 Explore TinyTechGuides: www.tinytechguides.com📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com🔗 Connect with Sanjeev Mohan:💼 LinkedIn: https://www.linkedin.com/in/sanjmo/📝 Medium: https://medium.com/@sanjmo🌎 Website: https://www.sanjmo.com/👍 If you found this insightful, hit like, share, and subscribe for more expert AI discussions!#AIAgents #DataStrategy #AIInBusiness

📌 Beyond AI Hype: What 20% of Companies Get Right | Shawn RogersThe AI revolution is here—but only 20% of companies are getting it right. What are they doing differently? In this episode of The Data Faces Podcast, Shawn Rogers, CEO of BARC and AI thought leader, shares research-backed insights on AI adoption, data strategy, and enterprise innovation.🔍 Key Topics Covered:1-The AI hype vs. reality—what’s actually driving business success2-How top companies align AI with business strategy for real ROI3-Why data quality is the #1 challenge (and how to fix it)4-The rise of domain-specific AI models & unstructured dataA5-I’s impact on jobs & workforce upskilling—what leaders need to knowTh6-e role of AI ethics, governance, and responsible AI development⏳ Timestamps for Easy Navigation:00:00 – Introduction & Meet Shawn Rogers02:00 – The AI Hype Cycle: Is It Hysteria or Innovation?07:45 – AI Adoption & What Leaders Are Doing Differently11:50 – Data Strategy & AI: Why Data Quality Matters18:00 – The Rise of Small & Domain-Specific AI Models25:30 – AI’s Impact on Jobs & Workforce Upskilling31:00 – Ethics, Bias & Responsible AI Development39:00 – Future Trends: What’s Next in AI & Data Strategy💡 More AI & Data Insights:🌐 Explore TinyTechGuides: www.tinytechguides.com📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com🔗 Connect with Shawn Rogers:💼 LinkedIn: https://www.linkedin.com/in/shawnrogers/🌎 BARC Website: https://barc.com👍 If you found this insightful, hit like, share, and subscribe for more expert AI discussions!#AITrends #DataStrategy #ShawnRogers #EnterpriseAI #AIGovernance #FutureOfWork #AIInBusiness #DataQuality #ArtificialIntelligence #MachineLearning

📌 The Role of Data Trust in AI Success | Kamal MaheshwariAI is only as good as the data it learns from—so how can businesses build AI on trusted, high-quality data? In this episode of The Data Faces Podcast, Kamal Maheshwari, Co-Founder of @decube_data, shares insights on why data trust is essential for AI success, how businesses can improve data integrity, and the role of leadership in fostering a culture of trust.🔍 Key Topics Covered:1-What data trust really means & why it’s critical for AI2-How businesses can prevent bad data from corrupting AI models3-The biggest challenges companies face in ensuring data integrity4-How leaders can foster a data-driven culture of trust5-The future of AI governance, automation, and trusted data⏳ Timestamps for Easy Navigation:00:00 – Introduction & Meet Kamal Maheshwari02:00 – Why AI Needs Trusted Data to Succeed07:45 – The Business Impact of Poor Data Quality11:50 – How Companies Can Ensure AI is Built on Reliable Data18:00 – Leadership’s Role in Creating Data Trust25:30 – AI’s Impact on Data Governance & Compliance31:00 – Challenges & Solutions in Scaling Trusted Data for AI39:00 – Final Thoughts & Advice for Business Leaders💡 More AI & Data Insights:🌐 Explore TinyTechGuides: www.tinytechguides.com📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com🔗 Connect with Kamal Maheshwari:💼 LinkedIn: https://www.linkedin.com/in/kamal-maheshwari/🌎 D Cube Website: https://www.dcube.ai👍 Enjoyed this discussion? Like, share, and subscribe for more AI insights!#DataTrust #AIandData #KamalMaheshwari #AIIntegrity #DataStrategy

📌 Gen AI in 2025: Why 90% Fail & How to Succeed | Kjell CarlssonHow can organizations separate AI hype from reality and build AI responsibly? In this episode of The Data Faces Podcast, Kjell Carlsson, Head of AI Strategy at Domino Data Lab, shares insights on AI governance, data trust, and the challenges of deploying AI agents.🔍 Key Topics Covered:1-Why 90% of Gen AI projects fail to deliver real business value2-AI governance and why organizations struggle with deployment3-The importance of trusted data for AI success4-The risks of AI hallucination and bias in enterprise AI5-The reality of AI agents: Hype vs. real-world applications⏳ Timestamps for Easy Navigation:00:00 – Introduction & Meet Kjell Carlsson02:00 – The Reality of AI in 2025: Hype vs. Reality07:45 – Why Most AI Projects Fail to Deliver Value11:50 – AI Governance: Challenges & Best Practices18:00 – Why Trusted Data is Critical for AI Success25:30 – AI’s Impact on Jobs & The Workforce31:00 – AI Agents: Are They the Future or Just Hype?39:00 – Final Thoughts & Advice for Business Leaders💡 More AI & Data Insights:🌐 Explore TinyTechGuides: www.tinytechguides.com📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com🔗 Connect with Kjell Carlsson:💼 LinkedIn: https://www.linkedin.com/in/kjellcarlssonphd/🌎 Domino Data Lab Website: https://www.dominodatalab.com👍 Enjoyed this discussion? Like, share, and subscribe for more AI insights!#AIGovernance #TrustedData #KjellCarlsson #AITrends #EnterpriseAI

📌 The Future of AI: Lessons from History | Kevin PetrieWhat can history teach us about AI's future? In this episode of The Data Faces Podcast, Kevin Petrie, VP of Research at BARC US, shares insights on how past technological revolutions mirror today’s AI boom, the risks of hype, and what businesses can learn to stay ahead.🔍 Key Topics Covered:1-Why AI Hype & Fear Are Nothing New – What history tells us2-The Industrial Revolution & AI: Are There Parallels?3-What Happens When Jobs Are Disrupted? AI vs. Past Tech Shifts4-AI Governance & Regulation: Lessons from Other Technologies5-The Future of AI in 2025: Will It Reshape Work & Society?⏳ Timestamps for Easy Navigation:00:00 – Introduction & Meet Kevin Petrie02:00 – AI Hype vs. Reality: What We Can Learn from History07:45 – The Printing Press, Industrial Revolution & AI Parallels11:50 – What Happens When Jobs Are Disrupted by AI?18:00 – AI & Cognitive Automation: Is This Time Different?25:30 – AI Governance & Ethics: Historical Lessons for Today31:00 – The Future of AI in 2025 & Its Business Impact39:00 – Final Thoughts: How to Stay Ahead in the AI Era💡 More AI & Data Insights:🌐 Explore TinyTechGuides: www.tinytechguides.com📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com🔗 Connect with Kevin Petrie:💼 LinkedIn: https://www.linkedin.com/in/kevinpetrie/🌎 BARC US Website: https://www.barc.com👍 Enjoyed this discussion? Like, share, and subscribe for more AI insights!#AIin2025 #AIHype #KevinPetrie #AIFuture #DataStrategy #AIandHistory

📌 The Ethics of AI: Fairness, Bias & Responsibility | Monica CisnerosAI is shaping our world—but is it truly fair and responsible? In this episode of The Data Faces Podcast, Monica Cisneros, AI and Data Analytics expert, explores the challenges of AI fairness, bias, and governance. We discuss how AI is shaping business, ethics, and society, and why understanding fairness isn’t as simple as it seems.🔍 Key Topics Covered:1-Why fairness in AI isn’t black and white2-How AI bias impacts real-world decisions3-21 mathematical definitions of fairness—why it’s complicated4-AI literacy & public understanding—who’s responsible?5-The Turing Trap: Is AI competing with humans or enhancing us?⏳ Timestamps for Easy Navigation:00:00 – Introduction & Meet Monica Cisneros02:00 – Why AI Fairness is More Complex Than You Think07:45 – The 21 Mathematical Definitions of AI Fairness11:50 – Real-World Bias in AI: The COMPAS Case Study18:00 – AI Ethics, Governance & Public Responsibility25:30 – AI Literacy & How the Public Can Better Understand AI31:00 – The Turing Trap: When AI Mimics Human Intelligence39:00 – Final Thoughts & The Future of Responsible AI💡 More AI & Data Insights:🌐 Explore TinyTechGuides: www.tinytechguides.com📩 Get exclusive AI workflows & insights: prompts.tinytechguides.com🔗 Connect with Monica Cisneros:💼 LinkedIn: https://www.linkedin.com/in/monica-cisneros/👍 Enjoyed this discussion? Like, share, and subscribe for more AI insights!#AIFairness #AIEthics #ResponsibleAI #AIandBias #MonicaCisneros #AIinSociety