เกี่ยวกับตอนนี้
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:
