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
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What is Eventual Consistency | Your Reality Check on What's Actually Happening in Data?
Eventual Consistency | Your Reality Check on What's Actually Happening in Data is a news podcast hosted by CorrDyn, with 27 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.
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CorrDyn hosts Eventual Consistency | Your Reality Check on What's Actually Happening in Data, a news show with 27 episodes published.
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Episode #12
The Consulting-Lab Land Grab: What Sits Between a Model and an Outcome
Jul 9, 202634 minS2
Every major AI lab has just bought itself a consulting arm. Ross Katz explains why that is not a flex, it is an admission that the model alone does not create value. You have been told to "do something with AI" by year end, and now every systems integrator in the market wants the work. How do you tell a partner doing real engineering from one reselling a model licence with a nice deck on top? Ross Katz is principal and data science lead at CorrDyn, where he works daily with enterprise data teams trying to get real value from AI deployments. He reads these lab-consultancy tie-ups as a practitioner who actually does the integration work, not as someone selling the deal. Ross walks through why OpenAI, Anthropic and the big four consultancies are suddenly partnering, and what each side actually gets from the arrangement. You will come away with a clear framework for the layers of work that sit between signing an AI deal and getting anything useful out of it, plus a simple filter for evaluating any implementation partner pitching you. This episode covers the OpenAI and Anthropic consulting and private equity deals, the six layers between a model and real business value, and where money actually accrues in the AI stack. It is built for data and analytics leaders under pressure to show AI results, not for anyone looking for lab hype or a quick fix. Key Takeaways The labs, consultancies and PE firms should not be natural partners, but each is trading something specific: reach, integration knowledge, or portfolio intelligence. Ross breaks down exactly what each side walks away with. Getting a model into production means working through six layers beyond the model itself, and the one nobody wants to talk about is the hardest. Anthropic's own numbers suggest six dollars of services spend for every dollar of software, which tells you where the real work (and the real value) is sitting right now. Ross gives you three questions to ask any AI consultant before you sign, built around exactly how they claim to deliver value. Chapter Markers 00:00 AI labs buying into consultancies and PE 01:25 Why labs need consultancies at all 09:19 Strategic alliance or forced marriage 11:12 The six layers between deal and deployment 17:11 Where the AI money actually lands 20:13 Are consultancies funding their own disruption 22:48 The 1970s mainframe rollout parallel 26:43 Spotting real engineering vs a reseller 29:09 What We're Watching: AI labs and IPO pricing 31:56 Recap and takeaways Useful Links & Resources Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ Ross Katz on X: https://x.com/brosskatz Previous episode referenced on Snowflake and Databricks context layers CorrDyn: corrdyn.com Connect With the Show CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ Ross Katz on X: https://x.com/brosskatz Which layer is eating your AI budget right now: the data substrate, the governance, or the process and change nobody budgeted for? Tell us where your own AI rollout is stuck, and whether your consultant could actually answer Ross's three questions. If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com. #EventualConsistency #DataIndustry #AIDisruption #BuildVsBuy #DataInfrastructure
Similar Keynote, Different Platforms: What Snowflake and Databricks Are Really Competing For
Jun 26, 202643 minS2
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. 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. 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. The real risk for data leaders right now is not backing the wrong preview feature. It is not experimenting at all. Key topics covered >> Why the "context not model" consensus is real and manufactured at the same time >> What LDAP means for your data architecture >> Why the ontology race matters more than the feature announcements >> How to make a Snowflake vs. Databricks platform decision in 2026 >> What a mature agentic AI system actually looks like in practice About the hosts Ross Katz 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. Jason Bradwell is a seasoned B2B marketing leader, founder of B2B Better and hosts Pipe Dream, where he explores how modern B2B companies can build media and marketing strategies that drive real revenue and audience growth. Connect with us: Sponsor: CorrDyn, a data consultancy Connect with Ross Katz on LinkedIn Connect with Jason Bradwell LinkedIn
Credence Goods, Junior Cuts, and the Value Chain Audit Firms Don't Want to Talk About
Jun 10, 202644 minS2
<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...
If AI Can Do the Work, What Are Clients Actually Paying For?
May 21, 202643 minS2
<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...
Acceleration Without Stabilization: what AI is doing to data teams, according to the dbt Lab State of Analytics Engineering report
May 8, 202639 minS2
<p>The 2026 dbt Labs State of Analytics Engineering report surveyed 363 data practitioners (not vendors, not analysts, but the people building and maintaining data systems). The headline finding is a tension that most people working in or alongside data teams will recognise immediately: AI is now embedded in daily data work, teams are shipping more and faster, and yet trust as a stated priority jumped from 66% to 83% in a single year. </p>
<p>At the same time, 41% of respondents still report ambiguous data ownership, 53% still cite poor data quality, and compute costs are up 50% while only 36% of teams report rising budgets.</p...
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