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

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.

  1. 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
  2. 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
  3. “AI's Experimentation Era is Over” Says Davos

    FEB 5

    “AI's Experimentation Era is Over” Says Davos

    The era of AI experimentation without accountability is over. Following Davos 2025, where business leaders declared the end of pilot-purgatory after $1.5 trillion in AI investments, we dig into what "AI ROI" actually means for data teams. Matt Sekac from Welocalize shares how they have moved beyond demos to production AI across translation workflows, product development, and internal operations, and why the measurement challenges are more nuanced than boardroom proclamations suggest. We explore the tension between needing disciplined experimentation and executives demanding immediate returns, why live data infrastructure matters more than the models themselves, and how AI value shows up in ways beyond cost reduction.  About the host 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. About the guest Matt Sekac leads R&D, the Data Office, and Data Analytics at Welocalize, where he focuses on building data-driven systems and capabilities that improve decision-making, forecasting, and commercial performance across the business. With a career that blends strategy, analytics, and operational execution, Matthew is known for turning complex data into practical tools, models, and workflows that teams can actually use. Connect with us:  Sponsor: CorrDyn, a data consultancyConnect with Ross Katz on LinkedIn Connect with Matt Sekac LinkedIn

    46 min
  4. IBM Buys Confluent: The $11B Bet on Streaming's AI Future

    JAN 22

    IBM Buys Confluent: The $11B Bet on Streaming's AI Future

    IBM is acquiring Confluent for $11 billion, and the data world is trying to figure out what it means. We cut through the press releases and LinkedIn hot takes to discuss what's actually happening here. Is this about mainframe modernization, hybrid cloud expansion, or IBM's bet on AI agents driving streaming adoption? We examine why IBM paid a 34% premium, what this means for the future of open-source Kafka, and whether the agentic commerce narrative holds water. Plus, we dig into what data leaders should actually be paying attention to as the industry consolidates around a few major platforms. About the hosts James 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, because someone has to pay for all that streaming infrastructure. Ross 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. Connect with us:  Sponsor: CorrDyn, a data consultancyConnect with Ross Katz on LinkedInConnect with James Winegar on LinkedIn

    31 min
  5. 02/13/2025

    People as Both the Problem and the Solution in Data | Featuring Nicole Radziwill

    In this episode of Data BS, James Winegar sits down with Nicole Radziwil, Co-Founder & Chief Data/AI Officer at Qzuku, to dive into the intersection of data, strategy, culture, and power within organizations. They discuss the crucial role of data teams in shaping decision-making, fostering alignment, and navigating power dynamics. Nicole shares valuable insights on how data professionals can drive impact beyond technical execution by facilitating shared understanding across teams. They also explore the importance of process improvement, visibility, and leadership in data-driven organizations—highlighting how Chief Data Officers (CDOs) and data teams can enhance collaboration and break down silos. Plus, Nicole introduces key concepts from her book Data, Strategy, Culture, and Power and shares actionable steps for organizations to address these challenges. Data teams have unique visibility into organizational misalignments and power dynamics. They should play an active role in resolving these tensions.Improving processes leads to better data and decision-making. Strong processes help teams align on shared goals.A CDO's job isn't just about technology—it’s about enabling the organization to access and use data effectively to drive meaningful change.Data teams should proactively surface misalignments between departments (e.g., sales and marketing) to create shared understanding.Whether in product analytics or broader data strategy, intentional problem-solving leads to scalable and effective solutions.Recognizing and addressing power imbalances in decision-making can help organizations unlock the full potential of their data.Organizational challenges are often rooted in human factors—improving communication, expectations, and feedback loops can significantly enhance productivity. Resources Mentioned Data, Strategy, Culture, and Power – Nicole Radziwill’s book on data leadership and organizational alignment.People and Data – A book by Tom Redman exploring the human side of data.Force Field Analysis – A technique for analyzing the forces that drive or hinder change in an organization Connect with Nicole Radziwill on LinkedIn

    39 min
  6. 01/15/2025

    From Pipelines to People: Building Strategic and Balanced Data Teams

    In this episode, Lindsay Murphy, a Head of Data at Hiive and host of the Women Lead Data podcast, joins James Winegar to share her unique journey in the data industry, offering insights on building data-driven strategies, creating balanced teams, and fostering authentic connections.  From leveraging LinkedIn to understanding the strategic value of data, Lindsay breaks down practical advice for professionals looking to thrive in the evolving world of data. Key Takeaways: Building Balanced Teams: Lindsay emphasizes the importance of creating diverse and inclusive data teams, discussing her proactive approach to sourcing talent, especially women, on LinkedIn.LinkedIn Optimization: Tips for making your LinkedIn profile recruiter-friendly, including the importance of authenticity and strategic keyword placement.Networking in a Post-COVID World: How virtual coffee chats and remote connections are transforming professional relationships and making networking more accessible.Data as a Strategic Function: The significance of aligning data initiatives with business goals and focusing on ROI to avoid inefficiencies and cuts.Empowering Teams with Self-Serve Models: How self-service tools and well-structured data models can enable smaller teams to deliver outsized value. Sponsor: CorrDyn, a data consultancy Connect with Lindsay Murphy on LinkedIn  Podcast: Women Lead Data on Apple Podcasts | Women Lead Data on Spotify Mentioned in the episode: Data and AI Diversity Report

    41 min

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.