How Agentic Analytics Is Reshaping Data Teams

A few years ago, a good analyst wrote one query every ten minutes. Now they can run every five to ten seconds, before counting the queries an agent fires off on its own just to figure out what’s in a table. The easy conclusion is that agentic analytics means the executive types a question, gets an answer, and never talks to a data person again. This episode argues the reality is messier and more demanding of the humans in the loop, not less. Ross Katz sits down with James Winegar, CEO of CorrDyn, and Jacob Matson, Developer Advocate at MotherDuck, to unpack what agentic analytics actually entails. They cover the collapse of traditional data team roles, what a genuine maturity model looks like from early experimentation to automated decision-making, and why the companies getting real value aren’t the ones with the most AI seats but the ones who started with a specific decision they wanted to make faster and pointed a curious person at it. Key takeaways Data team roles are collapsing. Data engineer, analytics engineer, and analyst are becoming one role. Teams that find people who span that chain move ten times faster. Most analytics is ad hoc and disposable, and that’s fine. Governance is about identifying what needs to be kept, not managing everything equally. Context drift is the hidden risk. As businesses change, the context layer goes stale. Human review remains non-negotiable — automated self-improvement tends to descend rather than climb. Curiosity now outperforms skill set alone. The winners in this era keep asking why and can bridge technical and business understanding. If everyone uses the same agents, everyone makes the same decisions. Proprietary data and the judgment that shapes how you use it are the real moat. Chapter Markers 01:36 What agentic analytics actually means vs. what people picture 05:25 The gap between what people ask for and what they actually need 09:21 The shift from embedded analyst to data platform team 12:52 Collapsing data roles into one 19:43 Governance anti-patterns and context management 33:04 Curiosity as competitive advantage 36:21 The agentic analytics maturity model 46:43 Why human review of context is still non-negotiable 54:34 MotherDuck’s positioning vs. larger warehouse vendors 58:14 DuckDB 2.0 and what changes for agentic use cases 01:08:00 Where to start: use case first, infrastructure second 01:11:14 Hot takes: what the trio believes most people would push back on Connect with the show MotherDuck https://motherduck.comDuckDB https://duckdb.orgDbt https://www.getdbt.comModel Context Protocol (MCP) https://modelcontextprotocol.ioRoss Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/James Winegar on LinkedIn: https://www.linkedin.com/in/james-winegar/Jacob Matson on LinkedIn: https://www.linkedin.com/in/jacobmatson/

Seven cloud data warehouses, one open benchmark, and no single winner. Your warehouse choice probably came down to what someone used at their last job, plus a vendor bake-off with the outcome decided before it started. Then the bill arrived. James Winegar and Ross Katz from CorrDyn, who ran the benchmark, alongside Dave Yaffe, Co-Founder of Estuary break down what happened when they loaded the same data into Databricks, BigQuery, Redshift, Microsoft Fabric, ClickHouse, MotherDuck and Snowflake. You'll come away with a sharper way to shortlist engines, size compute and challenge vendor claims before you commit budget to your data stack. The conversation covers warehouse sizing, concurrency, idle time billing, open table formats like Iceberg, and how AI agents change query load on your data infrastructure. It's built for heads of data, CTOs and data engineering leads weighing a warehouse decision. If you want one tool crowned the winner, this benchmark won't give you that. Key Takeaways Under 10GB, pick the cheapest engine you can run. At 100GB and 1TB, the speed-versus-cost curve flips, and a bigger warehouse can cost you less."Useful concurrency" combines team size, data volume and a latency target into one sizing number. AI agents can make a ten-person team query like a thousand people.Redshift and Fabric beat their reputations in testing, with caveats that bite if you run 24/7 or juggle many workloads.A second vendor you've tested on your own workload is your best leverage when a three-year contract lands on your desk. Chapter Markers 00:00 Why build a warehouse benchmark at all 02:17 Why Estuary backed the project 03:33 Data size, concurrency and cost dimensions 04:44 How teams really pick warehouses today 06:02 Why vendor-run benchmarks fall short 10:13 Landing 2.2TB into seven engines 14:11 Query validation for apples-to-apples results 15:21 Sizing compute: speed versus cost curves 19:43 Scan aggregates, ClickHouse and MotherDuck 22:10 Defining useful concurrency 27:34 How idle time drives your bill 32:36 Reputations confirmed and contradicted 39:15 Home field advantage caveats 42:14 Open table formats and managed Iceberg 47:29 Agentic analytics and warehouse load 53:10 AI agents inside the data pipeline 58:06 No clear winner across the board 1:01:35 Why autoscaling was switched off 1:03:21 How to read any vendor benchmark 1:05:43 Before you sign a three-year contract 1:08:57 What could change in a rerun Useful Links & Resources Data warehouse buyer's guide and companion app (built on this benchmark)Benchmarks referenced: TPC-DS and ClickBenchApache Iceberg (open table format)CorrDyn: https://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/James Winegar on LinkedIn: https://www.linkedin.com/in/james-winegar/David Yaffe on LinkedIn: https://www.linkedin.com/in/davidyaffe/Website: https://corrdyn.com Which warehouse reputation does your own experience contradict? Tell us where the benchmark matches your workload, and where it doesn't. #EventualConsistency #DataEngineering #DataWarehouse #DataInfrastructure #Iceberg

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/brosskatzPlatforms mentioned: Snowflake Cortex, Databricks Genie, BigQuery, Amazon Bedrock, Microsoft Fabric, MotherDuck, Looker, Omni, Rill, dbtMore from CorrDyn: https://corrdyn.comRelated show: Data in Biotech, also from CorrDyn Connect With the Show CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/Website: https://corrdyn.comListen 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

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

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

Your data platform used to serve people. Now it has to serve agents too, and that changes what you build and who you hire. Two people ask an AI agent the same question and get two different numbers back, and neither can explain how their agent got there. That's the moment a data leader realises the platform wasn't built for this world. Ross Katz, principal and data science lead at CorrDyn, works with data teams every day as they rebuild their platforms for a world where agents, not dashboards, are the main consumer of data. You'll get a clear framework for what actually changes on a data team when agents become primary consumers, the warning signs that tell you it's time to invest in your data foundation, and a practical way to think through build versus buy versus hire. Ross also makes the case for why judgment, not production, is now the scarce resource on any data team. Ross covers the new org chart split between platform and distribution work for humans and for agents, why judgment has become the bottleneck, and how tacit knowledge gets written into semantic layers and tools like MotherDuck Guides. This is for data and analytics leaders deciding how to structure their teams around agentic AI, not for anyone after a hype pitch on AI replacing analysts. Key Takeaways - The data team's job hasn't changed: curating knowledge and adding context, but who's consuming it has, and that reshapes the platform you need to build. - One specific failure mode is coming for every data team running agents at scale, and it involves two conflicting numbers that nobody can explain. - There are three real reasons to bring in an external team instead of building in-house, and only one of them is about missing skills. - AI should raise your standard for "done", not lower it, but only if you're using it the way Ross argues you should. Chapter Markers 00:00 - Introduction: agents as the new data consumer 00:49 - How the data team's role has evolved with AI 05:11 - Why context now flows to agents, not dashboards 09:43 - Warning signs it's time to invest in your data foundation 16:11 - Build vs buy vs hire: the real decision drivers 21:38 - Designing the org chart for platform and distribution 29:22 - Why judgment is the bottleneck, not production 34:01 - Which data roles disappear, and which get created 36:29 - Turning tacit knowledge into documented context 43:22 - What We're Watching: who AI actually helps 48:09 - Recap and key takeaways Useful Links & Resources - CorrDyn: corrdyn.com - Katie Bauer's platform vs distribution framework, referenced from her work at Hex: https://hex.tech/blog/data-teams-in-ai-era/ - Otis et al. research on AI and Kenyan business owners, discussed in the What We're Watching segment: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4671369 - MotherDuck Guides, for organisation-level data documentation: https://motherduck.com/docs/category/guides/ - Previous Eventual Consistency episode with Dave Yaffe on AI governance: https://www.corrdyn.com/podcasts/eventual-consistency/ep-27-prefect-dagster-orchestration/ 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/ - Website: corrdyn.com Where does your team stand right now: are you still building platforms purely for human consumers, or have agents already started showing up in your query logs? Tell us what you're seeing. If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com.

Prefect just bought Dagster. Two of the biggest names in orchestration are now one company, and the reasons go well beyond a slow grower getting folded into a faster one. If you're trying to work out whether your orchestration tool is about to get sidelined, or whether "agent governance" is really just a new label on an old data stack problem, this one's for you. David Yaffe is co-founder and CEO of Estuary, a real-time data integration platform, and previously ran product at Invite Media (acquired by Google) and co-founded Arbor (acquired by LiveRamp). He's spent his career building the pipelines and infrastructure that data teams actually run on. Ross and David break down what the Prefect-Dagster deal really signals: a shift in buyer from data engineers to IT and the CIO, and a race to own agent governance before anyone else does. You'll walk away with a sharper read on where orchestration is heading, why documentation might beat MCP servers for agent-ready systems, and where the next round of data stack consolidation is likely to land. Expect a candid take on M&A positioning versus reality, the mechanics of agent permissions and observability, and a debate on MCP servers versus CLI documentation for building agent-ready infrastructure. This is for data and analytics leaders trying to separate genuine strategy from press-release spin, not for anyone after a puff piece on the deal. Key Takeaways - The Prefect-Dagster deal has less to do with orchestration and more to do with who owns agent governance and permissions once agentic workflows go mainstream. - The buyer for agent governance tooling isn't the data engineer scheduling jobs. It's the IT department, and getting those two buyers speaking the same language won't be simple. - Well-documented CLIs can outperform MCP servers for agent use, cutting token spend and doing double duty for human users too. - Treat every agent like a new graduate on day one. If your documentation, schemas, and data model aren't clear enough for a novice to follow, an agent will struggle just as much, only more expensively. Chapter Markers 00:00 Prefect's acquisition of Dagster explained 01:39 Welcome David Yaffe, Estuary co-founder 02:02 First reactions to the Prefect-Dagster deal 03:46 Why orchestration alone isn't a sticky business 07:19 Fast MCP and the rise of agent governance 09:47 MCP servers versus CLI documentation debate 12:52 Treating agents like new hires on day one 14:41 Who actually buys agent governance tools 17:40 The "aqua-pivot": why Prefect really bought Dagster 20:07 What happens to existing Dagster users 24:17 Where data industry consolidation happens next 27:27 Governance, liability and the human parallel 29:50 Time-boxed access and the future of agent permissions 31:20 What success looks like a year from now 35:07 What We're Watching: John von Neumann and the death of reading 40:14 Wrap up and final thoughts Useful Links & Resources - David Yaffe on LinkedIn: https://www.linkedin.com/in/davidyaffe - Estuary (David's company): estuary.dev - The Maniac by Benjamin Labatut, referenced in the John von Neumann discussion 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/ - corrdyn.com Got a take on the Prefect-Dagster deal, or a horror story about agent permissions gone wrong? Tell us in the comments. We're curious whether your team sees this as consolidation, or the opening move in the fight for agent governance. If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com. #EventualConsistency #DataInfrastructure #AIagents #DataStack #Orchestration

What happens when an AI agent's mistake can cost you the client, not just the demo. You've proven your AI agent works in a sandbox. Getting the business to trust it in production, with real customers watching, is a different problem entirely. Matthew Sekac is Head of Data & Analytics at Welocalize, where he has spent the past 18 months building an AI agent framework to handle the project management complexity behind thousands of translation and localisation projects. He joined Welocalize in 2020 and has over a decade of senior sales strategy experience in IP and life sciences translation before moving into data leadership. Matt walks through exactly how his team proved an AI agent framework could handle over a million manual tasks a year without a mountain of rules-based technical debt. You'll get a concrete method for testing agent reliability against human benchmarks before you ever touch production and a way of thinking about human-in-the-loop verification that scales instead of adding new work. This one covers ethnographic research into how project managers actually work, shadow testing agents against human output, and the evals-based thinking needed to get leadership buy-in for AI at scale. It's built for data and analytics leaders trying to move AI agents from pilot to production, not for anyone looking for a quick AI hype fix. Key Takeaways - Welocalize ran a 30-day shadow test where an AI agent mirrored every human decision on live tasks before a single customer saw it, and that proof, not confidence, is what got leadership to say yes. - The team found their "quality check" agent was inversely correlated with actual accuracy: the more confident it was that a human wouldn't override it, the more likely a human did. The reason why says a lot about where reasoning agents still fail. - Nobody asks if a human process is error-free before automating it, but everyone expects zero errors from AI. Matt argues that a mismatch is the real reason AI trust conversations go sideways. - Before writing a single rule, the team spent weeks just watching project managers work, uncovering a 56-page SOP that turned into a 70-row spreadsheet for one customer alone. Chapter Markers 00:00 Introduction: capability and trust as one problem 01:07 The agentic AI initiative at Welocalize 03:07 Why rules-based automation hit a technical debt wall 06:45 Decomposing project complexity: common cause vs special cause 10:04 The ethnographic study: watching before automating 12:32 Building the shadow test: 30 days, human vs agent 16:04 Real hiccups: parsing failures and UI friction 19:00 Where human-in-the-loop verification goes next 22:52 Benchmarking against human error, not perfection 26:07 Trust, blame, and why systems feel different to people 29:22 Gathering evidence leadership will actually believe 38:42 Building a repeatable agentic engineering practice 40:50 How AI is changing who can build automation 49:00 Evals-based development as a precondition, not an afterthought Useful Links & Resources - Matthew Sekac on LinkedIn: https://www.linkedin.com/in/matthew-sekac-8884894 - Welocalize: https://www.welocalize.com Connect With the Show - Host LinkedIn: https://www.linkedin.com/in/b-ross-katz/ - Host on X: https://x.com/brosskatz - CorrDyn on LinkedIn: https://www.linkedin.com/company/corrdyn/ - Website: https://corrdyn.com If you're wrestling with the same problem, proving an AI system's reliability before anyone will trust it with production work, we want to hear how you're approaching it. Drop a comment with what your organisation's baseline for "good enough" actually is. If you want to talk about your data challenges, or you think we got something wrong, find us at corrdyn.com.

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/brosskatzPrevious episode referenced on Snowflake and Databricks context layersCorrDyn: 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

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 consultancyConnect with Ross Katz on LinkedInConnect with Jason Bradwell LinkedIn