Data BS | A Podcast from CorrDyn
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Podcast Overview
<p>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.</p> <p>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?"</p> <p>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.</p> <p>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?</p> <p>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.</p> <p>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.</p> <p>Because in data, like in distributed systems, consistency is hard. But eventually, reality catches up with the hype.</p> <p><br /></p>
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Publishing Since
10/7/2024
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Recent Episodes
June 26, 2026
Similar Keynote, Different Platforms: What Snowflake and Databricks Are Really Competing For
<p>Snowflake and Databricks held their flagship conferences within a fortnight of each other and both independently built their entire keynotes around the same claim: the bottleneck for enterprise AI is not the model, it is the context.</p> <p>When two direct competitors land on identical messaging at the same moment, the right response is to check the working. That's exactly what this episode of Eventual Consistency does.</p> <p>Ross Katz joins Jason Bradwell to separate signal from positioning across both conferences, from Databricks' LDAP and the one copy of data promise, to the ontology race, to what Genie One actually tells us about how mature these agentic platforms really are. The through line is bigger than any single announcement: data gravity is no longer the moat it once was, and both platforms know it. The race now is to become the structured intelligence layer of your business and that changes how platform decisions should be made.</p> <p>The real risk for data leaders right now is not backing the wrong preview feature. It is not experimenting at all.</p> <p><b>Key topics covered</b></p> <p>>> Why the "context not model" consensus is real and manufactured at the same time </p> <p>>> What LDAP means for your data architecture </p> <p>>> Why the ontology race matters more than the feature announcements </p> <p>>> How to make a Snowflake vs. Databricks platform decision in 2026</p> <p>>> What a mature agentic AI system actually looks like in practice </p> <p><b>About the hosts</b></p> <p><b>Ross Katz </b>brings a background in analytics and data strategy, working with companies to cut through the noise and focus on what actually drives business value. With experience spanning industries such as e-commerce, education, biotech, and finance, as well as the evolving landscape of AI-enabled work, he focuses on the intersection of data capabilities and business outcomes. He's particularly interested in how shifts in technology change not just what's possible but also how people think about and use data in their daily work.</p> <p><b>Jason Bradwell</b> is a seasoned B2B marketing leader, founder of <i>B2B Better</i> and hosts Pipe Dream, where he explores how modern B2B companies can build media and marketing strategies that drive real revenue and audience growth. </p> <p><b>Connect with us: </b></p> <ul><li>Sponsor:<b> </b><a href="https://www.corrdyn.com/" target="_blank">CorrDyn,</a> a data consultancy</li><li>Connect with Ross Katz on <a href="https://www.linkedin.com/in/b-ross-katz/" target="_blank">LinkedIn</a></li><li>Connect with Jason Bradwell <a href="https://www.linkedin.com/in/jasonbradwell/" target="_blank">LinkedIn </a></li></ul>
June 10, 2026
Credence Goods, Junior Cuts, and the Value Chain Audit Firms Don't Want to Talk About
<p>The Big Four accounting firms are posting more job ads for AI specialists than for auditors. Graduate intake is down 30% at KPMG and 22% at Deloitte. Equity partners are being quietly demoted. The global chairman of PwC is telling the BBC he can't find the engineers he needs.</p> <p>The natural read is that AI is eating audit from the inside. In Episode 10 of Eventual Consistency, Jason Bradwell and Ross Katz spend the episode pulling that narrative apart and find that the more interesting story isn't about audits going away. It's about a value chain being restructured in ways that most of the coverage is missing entirely.</p> <p>Ross introduces a four-phase model of the audit value chain: origination, analysis and production, judgment and synthesis, and relationship and sign-off. AI is compressing the middle (the analysis and production phases) but that compression doesn't reduce the judgment layer. As AI produces more, someone has to verify more. The nature of junior roles isn't being eliminated; it's being transformed from procedural execution toward synthesis and verification. And that transformation requires a different kind of training, a different kind of hire, and a fundamentally different expectation of what new entrants to the profession will do on day one.</p> <p>The conversation gets into why the talent problem the Big Four are describing (not enough AI specialists, stagnant junior salaries, and a competitive market they're not equipped to win) is partly a signalling problem as much as a hiring one. </p> <p><b>Key topics covered</b></p> <p>>> What the AI hiring numbers at the big four actually tell us and what they're being used to signal vs. what they mean operationally</p> <p>>> The four-phase audit value chain: origination, analysis and production, judgment and synthesis, relationship and sign-off</p> <p>>> Graduate intake cuts at KPMG, Deloitte, and PwC: one-time recalibration or permanent contraction?</p> <p>>> Whether AI is better for experts (multiplying senior leverage) or better at lifting the floor (enabling juniors to do more) and why the answer determines what the org chart looks like in five years</p> <p>>> The talent market reality: why big four firms are losing the AI hiring competition and what that window means for smaller firms</p> <p><b>About the hosts</b></p> <p><b>Ross Katz </b>brings a background in analytics and data strategy, working with companies to cut through the noise and focus on what actually drives business value. With experience spanning industries such as e-commerce, education, biotech, and finance, as well as the evolving landscape of AI-enabled work, he focuses on the intersection of data capabilities and business outcomes. He's particularly interested in how shifts in technology change not just what's possible but also how people think about and use data in their daily work.</p> <p><b>Jason Bradwell</b> is a seasoned B2B marketing leader, founder of <i>B2B Better</i> and hosts Pipe Dream, where he explores how modern B2B companies can build media and marketing strategies that drive real revenue and audience growth. </p> <p><b>Connect with us: </b></p> <ul><li>Sponsor:<b> </b><a href="https://www.corrdyn.com/" target="_blank">CorrDyn,</a> a data consultancy</li><li>Connect with Ross Katz on <a href="https://www.linkedin.com/in/b-ross-katz/" target="_blank">LinkedIn</a></li><li>Connect with Jason Bradwell <a href="https://www.linkedin.com/in/jasonbradwell/" target="_blank">LinkedIn </a></li></ul>
May 21, 2026
If AI Can Do the Work, What Are Clients Actually Paying For?
<p>When AI can produce a ten-page analytics report, spin up a data pipeline, or generate a plausible infrastructure assessment in minutes, a question starts nagging at everyone running a professional services business: what exactly are clients still paying for?</p> <p>In Episode 9 of Eventual Consistency, Jason Bradwell and Ross Katz tackle that question from two different angles, Ross from the data services side, Jason from the marketing agency world. They find that the answer has almost nothing to do with AI capability, but has everything to do with three things: whether a client can tell if the work is right, whether you can recover when it goes wrong, and whether the work compounds on a foundation that actually holds.</p> <p>Ross introduces a framework for thinking about where AI genuinely displaces expertise and where it doesn't, with the verifiability test, the recoverability test, and the compounding test. </p> <p>They also dig into the trust problem that's quietly gotten harder for service providers. When a plausible-looking document can be prompted into existence in seconds, the signals clients used to rely on to assess trustworthiness such as a well-produced deliverable or a polished proposal have depreciated fast. </p> <p>The episode closes on a popular discussion on the build vs. buy equation in an AI-accelerated world. Why "I can build it now" is not the same as "I should build it", and whether the heavily subsidised pricing of today's AI tools represents a genuine future risk or an overblown concern. </p> <p><b>Key topics covered</b></p> <p>> What the collapsing agency pyramid means for the future of professional services </p> <p>> Why boutique expertise becomes more valuable, not less, as AI handles the general case</p> <p>> The specification cost problem: the more specific your need, the more deeply you have to engage to get AI to meet it, which is exactly where domain expertise lives</p> <p>> The iron triangle illusion: why AI is conditioning clients to expect fast, cheap, and good simultaneously and why the cost just shifts to the future</p> <p>> Value-based vs. time-and-materials pricing in an AI era: what the research says, and why business model stickiness is a real constraint</p> <p>> Whether subsidised AI pricing is a ticking clock or an overblown concern </p> <p><b>About the hosts</b></p> <p><b>Ross Katz </b>brings a background in analytics and data strategy, working with companies to cut through the noise and focus on what actually drives business value. With experience spanning industries such as e-commerce, education, biotech, and finance, as well as the evolving landscape of AI-enabled work, he focuses on the intersection of data capabilities and business outcomes. He's particularly interested in how shifts in technology change not just what's possible, but how people think about and use data in their daily work.</p> <p><b>Jason Bradwell</b> is a seasoned B2B marketing leader, founder of <i>B2B Better</i> and hosts Pipe Dream, where he explores how modern B2B companies can build media and marketing strategies that drive real revenue and audience growth. </p> <p><b>Connect with us: </b></p> <ul><li>Sponsor:<b> </b><a href="https://www.corrdyn.com/" target="_blank">CorrDyn,</a> a data consultancy</li><li>Connect with Ross Katz on <a href="https://www.linkedin.com/in/b-ross-katz/" target="_blank">LinkedIn</a></li><li>Connect with Jason Bradwell <a href="https://www.linkedin.com/in/jasonbradwell/" target="_blank">LinkedIn </a></li></ul>
24 total episodes available
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- What is Data BS | A Podcast from CorrDyn?
<p>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.</p> <p>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?"</p> <p>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.</p> <p>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?</p> <p>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.</p> <p>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.</p> <p>Because in data, like in distributed systems, consistency is hard. But eventually, reality catches up with the hype.</p> <p><br /></p> - How often does this podcast release new episodes?
This podcast updates weekly.
- Where can I listen to this podcast?
This podcast is available on 6 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Information about guest appearances is not available.
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