Eventual Consistency | Your Reality Check on What's Actually Happening in Data

CorrDyn
  • 5.0 (4)
  • Tech News
  • Updated Biweekly

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.

  1. Credence Goods, Junior Cuts, and the Value Chain Audit Firms Don't Want to Talk About

    5d ago

    Credence Goods, Junior Cuts, and the Value Chain Audit Firms Don't Want to Talk About

    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. 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. 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. 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.  Key topics covered >> 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 >> The four-phase audit value chain: origination, analysis and production, judgment and synthesis, relationship and sign-off >> Graduate intake cuts at KPMG, Deloitte, and PwC: one-time recalibration or permanent contraction? >> 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 >> The talent market reality: why big four firms are losing the AI hiring competition and what that window means for smaller firms 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

    44 min
  2. If AI Can Do the Work, What Are Clients Actually Paying For?

    May 21

    If AI Can Do the Work, What Are Clients Actually Paying For?

    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? 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. 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.  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.  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.  Key topics covered > What the collapsing agency pyramid means for the future of professional services  > Why boutique expertise becomes more valuable, not less, as AI handles the general case > 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 > 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 > Value-based vs. time-and-materials pricing in an AI era: what the research says, and why business model stickiness is a real constraint > Whether subsidised AI pricing is a ticking clock or an overblown concern  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 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

    43 min
  3. Acceleration Without Stabilization: what AI is doing to data teams, according to the dbt Lab State of Analytics Engineering report

    May 8

    Acceleration Without Stabilization: what AI is doing to data teams, according to the dbt Lab State of Analytics Engineering report

    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.  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. The report calls it "acceleration without stabilization." In this episode of Eventual Consistency, we use the report as a lens on a bigger question: what is AI actually doing to the relationship between data teams and the rest of the business? Ross Katz from CorrDyn, works with organizations across multiple industries as part of their external data team, offering a vantage point that most internal practitioners don't have. He unpacks why the bottleneck in most data organisations has shifted from infrastructure to accountability, and why the real challenge isn't technical. It's political. Key topics covered >> "Acceleration without stabilization": what it looks like inside organizations and why data teams bear the cost of the gap >> Why the biggest constraint in the AI era isn't time or money, it's attention >> Stakeholder management as a data team survival skill: how to navigate upward in environments that prioritise speed over foundations >> The four layers of data work; generation, integration, maintenance, governance and where AI actually helps vs. creates new demand >> The budget squeeze: compute costs up 50%, team budgets up 36% and what that asymmetry forces data teams to deprioritise >> Trust as infrastructure: when it matters most, where it can be traded off, and what dbt's framing gets right and oversimplifies >> From enablement layer to control layer: how the data team's role is shifting as AI agents enable end-runs around traditional data infrastructure >> The distinction between summarization and synthesis and why it matters for anyone building an organisational knowledge base for AI 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 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

    39 min
  4. How Software Supply Chain Risk Became Everyone's Problem

    Apr 23

    How Software Supply Chain Risk Became Everyone's Problem

    The threat isn't coming from outside your perimeter. It's already inside, embedded in the open source libraries your engineers pulled in last quarter, the routers running on your network, the security tooling sitting in your CI/CD pipeline. In this episode of Eventual Consistency, host James Winegar is joined by Jamil Bou Khair, founder and CEO of Firezone, and Brian Manifold, senior full stack engineer at Firezone, to unpack what software supply chain security actually looks like in practice and why most enterprises are still building their defenses for a threat model that no longer reflects reality. The conversation is grounded in a real incident: the March 2026 Trivy vulnerability, in which attackers exploited a misconfiguration in Aqua Security's GitHub Actions environment, extracted a privileged access token, and used it to publish a malicious binary that was live in distribution channels for nearly three hours.  They also discuss the hardware dimension of supply chain risk, with the US government's ban on foreign-made routers, and the reality that manufacturing on US soil doesn't solve the chip provenance problem.  They dig into the tension between moving fast and maintaining operational security, drawing on Jamil's experience scaling Firezone from a startup with three engineers to a team with GitHub Enterprise security policies enforced at the repo level. The episode closes with the practical architecture of defensibility: why blast radius reduction and zero trust segmentation matter more than perimeter security in an AI-accelerated threat environment, how Firezone approaches dependency management including cooldown periods and IP allow listing across SaaS platforms, and why the industry may be approaching a moment where writing your own dependencies rather than pulling in shared libraries, becomes a legitimate security strategy. About the Host James Winegar is a data consultant who has spent years in the trenches helping enterprise organizations actually implement the technologies that vendors promise will revolutionize their business. He specializes in data infrastructure, real-time systems, and the practical realities of what works when the proof of concept becomes production. His approach is skeptical, pragmatic, and focused on the economics of technology decisions, a lens he brings to bear throughout this episode on the real costs of getting MCP wrong and what it actually takes to make AI agents useful in a production enterprise environment. About the Guests Jamil Bou Khair is the Founder and CEO of Firezone, a zero trust network access platform. Jamil brings a founder's perspective on the operational security trade-offs that real engineering teams face when moving fast in a threat environment that is moving faster. Connect with Jamil on LinkedIn: https://www.linkedin.com/in/jamilbk/ Brian Manifold is a Senior Full Stack Engineer at Firezone. Brian works across the stack on security architecture and has direct experience with the dependency management, access control, and incident response challenges discussed in this episode. Connect with Brian on LinkedIn: https://www.linkedin.com/in/brian-manifold-536a0a3a/ Connect with us:  Sponsor: https://www.corrdyn.com/ a data consultancyConnect with James Winegar on LinkedIn: https://www.linkedin.com/in/james-winegar/

    34 min
  5. The New Plumbing: What MCP's Rise to Industry Standard Means for Enterprise Data Strategy

    Apr 9

    The New Plumbing: What MCP's Rise to Industry Standard Means for Enterprise Data Strategy

    In March 2026, Digital Applied reported that Anthropic's Model Context Protocol had crossed 97 million installs in just 16 months, a faster adoption curve than most enterprise infrastructure standards ever achieve. OpenAI, Google, and Microsoft are all now shipping MCP-compatible tooling. What started as one company's open standard has quietly become the default interface between AI agents and the systems they work with. But is 97 million installs the moment a standard becomes infrastructure or just a very large number? In this episode of Eventual Consistency, Ross Katz sits down with James Winegar to unpack what MCP's rise actually means for the companies building on it, the enterprises adopting it, and the data leaders who are going to be asked to make decisions about it. James argues that the standardization happened much earlier than most people realize; the install count is a lagging indicator, not the inflection point. The real question now isn't whether to pay attention to MCP, but how to build for a world where it's the default. And the answer, he contends, isn't to throw AI at your raw source systems and hope for the best. The companies that will win are the ones that do the foundational work first: getting data into a warehouse, modeling it properly, and then giving AI agents a well-scaffolded, cost-efficient interface to query against, rather than burning compute calling out to Salesforce, your ERP, and five other SaaS platforms in real time and hoping the answer comes back with reasonable fidelity. The episode also gets into the tension between MCP and CLI tooling, when each is the right interface, why startups are naturally gravitating toward MCP to get to market fast, and why the security and authentication story around MCP is still catching up to what enterprise compliance actually requires. James draws a sharp distinction between MCP servers that authenticate as a privileged service account and those that can actually propagate user-level permissions and explains why that distinction matters enormously once HIPAA, SOC 2, or SEC compliance enters the conversation. Ross and James close with a provocation: by 2028, what does the world look like for a data team that treated MCP as someone else's infrastructure problem? 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. James Winegar is a data consultant who has spent years in the trenches helping enterprise organizations actually implement the technologies that vendors promise will revolutionize their business. He specializes in data infrastructure, real-time systems, and the practical realities of what works when the proof of concept becomes production. His approach is skeptical, pragmatic, and focused on the economics of technology decisions, a lens he brings to bear throughout this episode on the real costs of getting MCP wrong and what it actually takes to make AI agents useful in a production enterprise environment. Connect with us:  Sponsor: CorrDyn, a data consultancyConnect with Ross Katz on LinkedInConnect with James Winegar on LinkedIn

    55 min
  6. The Back End Is the New Storefront: What AI Agents Mean for Retail Data Strategy

    Mar 26

    The Back End Is the New Storefront: What AI Agents Mean for Retail Data Strategy

    In early March, two major retailers made headlines with strategies that look like opposite bets on the future. Best Buy announced a partnership with OpenAI, opening its product catalog to ChatGPT and supporting Google's Universal Commerce Protocol, positioning itself to be discovered and purchased through AI agents, not just human browsers. Target, meanwhile, announced plans to open more than 30 new physical stores in 2026, backed by a $5 billion capital investment plan that includes expanded fulfillment, food and beverage, and same-day delivery infrastructure built in from the ground up. On the surface, these look like diverging strategies. In this episode of Eventual Consistency, we discuss the case that they're actually two versions of the same underlying bet on where data advantage lives in the next decade of retail. Ross Katz breaks down what it actually takes in the back end for Best Buy to make its product catalog legible to AI agents: clean, structured product data with consistent taxonomy, real-time inventory APIs capable of handling agent-scale request volumes, programmatic pricing that can respond without a human in the loop, and reputation data that feeds trust signals back to the foundation model companies routing purchases. It sounds straightforward. For most retailers sitting on years of technical debt, it isn't. The Target conversation goes deeper than store count. Ross argues that physical stores in the AI era aren't just retail locations, they're data collection infrastructure. The in-store experience becomes the top of a data flywheel that fuels ad targeting, brand loyalty, and fulfillment optimization. The risk for any retailer that goes all-in on agentic commerce without protecting that flywheel? Becoming a fulfillment center for someone else's platform. The episode also tackles the harder strategic question: what happens to the customer relationship when AI agents become the primary interface between buyer and brand? Who owns that relationship? The retailer, or the foundation model company routing the purchase?  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 how people think about and use data in their daily work. Jason Bradwell is a seasoned B2B marketing leader, founder of B2B Better and host 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 on LinkedIn

    40 min
  7. Power Before Code: The Energy Constraints Reshaping AI Infrastructure

    Mar 6

    Power Before Code: The Energy Constraints Reshaping AI Infrastructure

    Every week brings another AI announcement, another data center project, another promise about what's possible with enough compute. But there's a constraint most people in tech aren't talking about yet - power. In this episode of Eventual Consistency, Ross Katz sits down with Mickey Peters, former energy executive and Vistage Chair, who spent decades running major power operations across South America for Duke Energy, managing over $1 billion in capital employed.  The conversation starts with two converging stories: hyperscalers like Meta, Google, and Microsoft building private power generation to bypass grid constraints - one West Texas project consuming more electricity than all of Chicago - and cities like Denver hitting pause on data center development altogether. The bottleneck isn't capital. It isn't technology. It's whether the electricity exists where you need it, and whether communities will let you build there. Ross and Mickey break down how the major cloud players are each taking radically different approaches to solving the energy problem, from Meta's gas-powered megacampus in Louisiana, to Google's acquisition of an in-house renewable energy developer, to Microsoft reactivating Three Mile Island. They dig into why there's no one-size-fits-all solution, and what the real trade-offs look like between partnering with local utilities versus going behind the meter with your own generation. But the most underappreciated challenge isn't technical, it's human. Mickey draws on his experience managing energy infrastructure in remote Andean communities to explain why community trust is ultimately what makes or breaks a data center project. He unpacks what those conversations between hyperscalers, local regulators, utilities, and communities actually look like, why NIMBYism is more nuanced than a single objection, and what companies consistently get wrong when they show up to make their case. 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 how people think about and use data in their daily work. Mickey Peters is an entrepreneur and executive coach who helps leaders build stronger teams, make better decisions, and grow profitability through his Houston Vistage peer advisory group. He brings decades of international leadership experience, including 20+ years living and working in Latin America, and senior roles at Duke Energy. Connect with us:  Sponsor: CorrDyn, a data consultancyConnect with Ross Katz on LinkedIn Connect with Mickey Peters LinkedIn

    39 min
  8. 8 Ways to Survive the SaaSpocalypse

    Feb 20

    8 Ways to Survive the SaaSpocalypse

    Nearly $300 billion in market value vanished from software companies in a single week after Anthropic’s AI agent launch reignited fears of a “SaaSpocalypse”. In this episode of Eventual Consistency, guest host Jason Bradwell turns the tables and interviews Ross Katz about what’s really happening beneath the headlines. Is AI truly threatening the seat-based SaaS model, or is this another cycle of hype? Ross breaks down the difference between market panic and operational reality, introduces eight key factors that determine which SaaS companies are vulnerable (or defensible), and explains why data gravity, regulatory moats, and revenue model alignment matter more than ever. The conversation explores how AI agents shift the build vs. buy equation, why consumption-based pricing is on the rise, and what data leaders should prioritize in 2026 and beyond. The verdict? AI won’t kill SaaS, but it will reshape it, creating clear winners and losers in the years ahead. 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 how people think about and use data in their daily work. Jason Bradwell is a seasoned B2B marketing leader, founder of B2B Better and host 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

    28 min
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Ratings & Reviews

5
out of 5
4 Ratings

About

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.

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