Data BS | A Podcast from CorrDyn

About Data BS | A Podcast from CorrDyn

The data leader's fortnightly reality check. No hype. No hot takes for engagement. Just honest conversation about what's actually happening in data and what it means for the work you're doing. Every two weeks, we pick the stories dominating your feed, the acquisitions, product launches, frameworks, and controversies and discuss them the way you would with your team: critically, honestly, and with one question in mind: "What does this actually mean for my world?" We're not here to sell you courses, predict the future, or tell you the sky is falling. We're here to cut through vendor claims that everything is "revolutionising" something, LinkedIn posts oscillating between doom and humble-brags, and tech journalism that treats every product launch like it's world-changing. This is for VPs of Data, Analytics Directors, Data Engineering Managers, and senior practitioners who need to stay informed but don't have time to wade through whitepapers and noise. People making real decisions: Should we migrate to that warehouse? Is this ML use case worth it, or just shiny object syndrome? Why is everyone talking about this framework when it doesn't solve our actual problem? In 20 minutes, you'll know what's worth your attention and what you can safely ignore. You'll get the perspective to make better decisions, ask vendors better questions, and avoid getting swept up in whatever trend is dominating feeds this week. You'll hear from practitioners and consultants who've been in the room when these decisions go right and when they go spectacularly wrong. We know what the press release says. We also know what actually happens six months later. Because in data, like in distributed systems, consistency is hard. But eventually, reality catches up with the hype.

Publisher

CorrDyn

Category

business · news

Language

en

Explicit

No

First episode

7 Oct 2024

Latest episode

7 Oct 2026

Latest episodes

31 episodes in the feed.

1. 

   7 Oct 2026

   Will Agentic Analytics Help You Shrink Your Data Team?

   Your CEO saw the agentic analytics demo. Now someone has to make it work. Leadership wants an AI agent answering every data question, and the CFO is already counting the analyst headcount it will save. You're the one who has to explain what the demo left out. Ross Katz, Principal and Data Science Lead at CorrDyn, argues that an AI agent does an analyst's job without knowing what actually matters to your business. So the real work moves into your semantic layer and context, plus the feedback loop that keeps them accurate. You'll leave with a way to scope a first prototype, the questions to put to any vendor, and a straight answer for the CFO. Jason Bradwell puts the questions to Ross on what agentic analytics is, how the vendors stack up, and where lock-in hides in your data stack. This one is for heads of data and analytics weighing build vs buy as AI disruption reaches BI. If you want a ranked vendor list, look elsewhere. Key Takeaways Your semantic and context layers decay over time. Anthropic hit this building its own agentic analytics system, and it changes the headcount math. Ross has one vendor category he'd steer clear of, and one type of company where he'd say go for it anyway. Keep your documentation in version control and you keep the option to migrate later, even inside a platform built to hold on to you. A $10,000-a-day AI bill is believable. The bigger question is how nobody caught it sooner. Chapter Markers 00:00 Why agentic analytics is everywhere right now 01:29 Defining agentic analytics in plain terms 04:03 From Excel exports to self-serve BI 08:09 Is the agent really an AI analyst? 10:46 Why humans still curate what matters 13:36 Pushing back on an all-in executive 16:23 The semantic layer and the feedback loop 22:36 Data as an argument about priorities 23:25 Vendor tour: hyperscalers and data warehouses 28:16 Keeping your options open on lock-in 29:42 BI tools with AI built in 30:37 The trouble with all-in-one platforms 34:32 The questions to ask any vendor 35:45 When all-in-one does make sense 39:02 What to tell the CFO about headcount 43:56 What We're Watching: the $10,000-a-day AI bill 47:26 Guardrails for token spend 49:51 Wrap-up Useful Links & Resources Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ Ross Katz on X: https://x.com/brosskatz Platforms mentioned: Snowflake Cortex, Databricks Genie, BigQuery, Amazon Bedrock, Microsoft Fabric, MotherDuck, Looker, Omni, Rill, dbt More from CorrDyn: https://corrdyn.com Related show: Data in Biotech, also from CorrDyn Connect With the Show CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ Website: https://corrdyn.com Listen on Apple Podcasts and Spotify Has your company put an agent in front of its data yet? Tell us what broke first, or which vendor question you wish you'd asked before signing. And if you think Ross is wrong about all-in-one platforms, make the case in the comments. If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com. #EventualConsistency #AgenticAnalytics #DataEngineering #SemanticLayer #CorrDyn

2. 

   23 Sept 2026

   Why Nvidia's $13B Deal for Hugging Face Was Never About Revenue

   Nvidia just paid almost $13 billion for a company with $150 million in revenue. That math only makes sense once you stop thinking about revenue. Every big AI acquisition headline feels urgent, but figuring out whether it changes anything about your data stack or your vendor risk is another matter entirely. Ross Katz, principal and data science lead at CorrDyn, works with data teams day to day on exactly the kind of infrastructure decisions this deal touches. He walks through the acquisition using the same framework he'd use with a client evaluating any major shift in the data industry. You'll walk away with a clear read on why Nvidia bought Hugging Face, what "aggregating demand" means in practice, and whether enterprises leaning on Hugging Face need to change anything right now. Ross also flags the one thing worth putting on your 3 to 5 year roadmap regardless of how this deal plays out. This episode covers Nvidia's smile curve strategy, the difference between supply aggregation and demand aggregation, Hugging Face's security breach and what Nvidia's involvement means for trust in the platform, and the regulatory review timeline ahead. It's built for data and analytics leaders who need a clear-eyed read on AI industry M&A, not hot takes. Key Takeaways - The $13 billion price tag is a rounding error for Nvidia, and that's the point: this was never a revenue play, it's about owning one of AI's key demand aggregation points. - Nvidia's real nightmare isn't losing this deal, it's a future where the model layer captures all the value and hardware becomes a commodity. Ross explains why that reshapes every move Jensen Huang makes. - If your team treats Hugging Face as load-bearing infrastructure, Ross has a specific take on what should change now versus what belongs on a 3 to 5 year roadmap instead. - Nvidia has a track record of vertical integration ambitions, and Ross lays out the exact scenario that would push them to acquire a frontier model company outright. Chapter Markers 00:00 Nvidia's $13 billion Hugging Face acquisition 00:58 What is Nvidia actually buying here 04:36 Aggregating supply versus aggregating demand 07:09 Hugging Face as AI's demand aggregation hub 11:44 Nvidia's nightmare scenario: hardware commoditization 14:10 Was Nvidia the only strategic buyer 20:27 Should enterprises rethink Hugging Face dependency 23:46 Regulatory review timeline and what to watch 24:58 Could Nvidia acquire a frontier model company 30:38 Nvidia's lead over AMD, Qualcomm, and Broadcom 33:23 Hugging Face's security breach and trust concerns 35:24 What We're Watching: data warehouse benchmarking findings Connect With the Show Follow Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ Website: corrdyn.com Do you think Nvidia's Hugging Face acquisition changes anything about how you evaluate vendor risk in your AI stack? Tell us in the comments, we read every one. If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com. #EventualConsistency #DataIndustry #Nvidia #AIInfrastructure #DataStrategy

3. 

   9 Sept 2026

   How to Know When Open-Weight AI Models Actually Save You Money

   AT&T runs 45 billion AI tokens a day, and open-weight models already handle a quarter of that load. If your business is burning through a frontier-model bill without knowing which tasks actually need frontier intelligence, you're leaving cash on the table every single day. Ross Katz, Principal and Data Science Lead at CorrDyn, works with data teams on exactly this problem: deciding what to build, what to buy, and what to route. He breaks down a Wall Street Journal piece on AT&T's shift to open-weight models and open-weight AI, and tells you where that logic does and doesn't apply to a normal-sized company. Listen and you'll walk away with a practical way to decide when open-weight models are worth the engineering cost, when routing beats self-hosting, and when Chinese-origin models are a risk versus just a headline. Ross puts a number on it: if the use case isn't worth more than a million dollars a year, don't bother self-hosting. Ross and Jason get into the real difference between open-source and open-weight models, when a routing layer beats hosting your own infrastructure, and why data exfiltration (not hidden backdoors) is the actual risk with Chinese models. They also cover build-cost estimates for self-hosting, and close with a candid take on the AI bubble debate. This one's for data leaders weighing their AI spend, not for anyone just using ChatGPT subscriptions day to day. ## Key Takeaways - AT&T's chief data and AI officer expects open models to handle up to 80% of their AI workload within a few years, but Ross explains why that math doesn't automatically transfer to a smaller company. - Self-hosting an open-weight model isn't just a GPU cost. Ross puts a real number on the engineering talent required, and where the break-even point actually sits. - The Chinese-model security story isn't what most headlines suggest. Ross separates the genuine risk (data exfiltration via APIs) from the one that hasn't been proven (hidden backdoors in the weights). - There's a specific volume threshold, tied to a proprietary dataset, where a small fine-tuned open-weight model can beat a frontier model outright. Ross names the number of examples it took. ## Chapter Markers 00:00 AT&T's token numbers and the episode setup 01:05 Are open models ready for large companies 02:38 Why routing captures value other companies leave behind 07:00 Open-source versus open-weight, explained 09:54 How AT&T's smart router actually works 12:11 Shared inference versus dedicated deployment 14:43 The real cost of self-hosting a model 17:19 Where open weights clearly win today 21:35 AT&T's use of Chinese models 22:33 Is the security risk real or overblown 27:32 What smaller businesses should actually worry about 30:57 Questions every data leader should be asking now 41:51 What We're Watching: is AI a bubble ## Useful Links & Resources - Wall Street Journal: AT&T's open-model strategy (the article that prompted this episode) - CorrDyn: corrdyn.com ## Connect With the Show - Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - Ross Katz on X: https://x.com/brosskatz - CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ Are you already routing workloads between open and closed models, or is your AI stack still one provider, one bill? Tell us how you're thinking about it. If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com. #EventualConsistency #DataIndustry #AIInfrastructure #OpenWeightAI #BuildVsBuy

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

YouTube views

6,396

Score snapshot 15 Sept 2025

Apple Podcasts (US)

5.0 / 5

4 ratings

Spotify

5.0 / 5

2 ratings

Podcast Authority Score: 39 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

38

Social presence

0

YouTube

68

Engagement

32