Ryan Dolley, VP of Product Strategy at GoodData and co-host of Super Data Brothers podcast, joined Yuliia and Dumke to discuss the DBT-Fivetran merger and what it signals about the modern data stack's consolidation phase. After 16 years in BI and analytics, Ryan explains why BI adoption has been stuck at 27% for a decade and why simply adding AI chatbots won't solve it. He argues that at large enterprises, purchasing new software is actually the only viable opportunity to change company culture - not because of the features, but because it forces operational pauses and new ways of working. Ryan shares his take that AI will struggle with BI because LLMs are trained to give emotionally satisfying answers rather than accurate ones. Ryan Dolley linkedin

Thomas in't Veld, founder of Tasman Analytics, joined Yuliia and Dumke to discuss why data projects fail: teams obsess over tooling while ignoring proper data modeling and business alignment. Drawing from building analytics for 70-80 companies, Thomas explains why the best data model never changes unless the business changes, and how his team acts as "data therapists" forcing marketing and sales to agree on fundamental definitions. He shares his controversial take that data modeling sits more in analysis than engineering. Another hot take: analytics engineering is merging back into data engineering, and why showing off your DAG at meetups completely misses the point - business understanding is the critical differentiator, not your technology stack.

Patrick Thompson, co-founder of Clarify and former co-founder of Iteratively (acquired by Amplitude), joined Yuliia and Dumky to discuss the evolution from data quality to decision quality. Patrick shares his experience building data contracts solutions at Atlassian and later developing analytics tracking tools. Patrick challenges the assumption that AI will eliminate the need for structured data. He argues that while LLMs excel at understanding unstructured data, businesses still need deterministic systems for automation and decision-making. Patrick shares insights on why enforcing data quality at the source remains critical, even in an AI-first world, and explains his shift from analytics to CRM while maintaining focus on customer data unification and business impact over technical perfectionism.Tune in!

Jason Touleyrou, Data Engineering Manager at Corewell Health joined Yuliia to discuss why most organizations struggle with data governance. He argues that data teams should focus on building trust through flexible systems rather than rigid controls. Challenging traditional data quality approaches, Jason suggests starting with basic freshness checks and evolving governance gradually. Drawing from his experience across healthcare and marketing analytics, he shares practical strategies for implementing governance during migrations and measuring data team value beyond conventional metrics. Jason's linkedin page - https://www.linkedin.com/in/jasontouleyrou/

John Grubb is the Sr. Director of FinOps and Cost Modeling at Platform.sh. With experience as a former Data Platform Director, Director of BI & Analytics, and Director of Customer Care, John brings a sharp perspective on why cloud costs matter. He knows how to align financial and engineering teams and believes that FinOps is about maximizing the value of every cloud dollar rather than just cutting costs.Follow John on Linkedin- https://www.linkedin.com/in/johnnygrubb/John's blog - https://www.thefinoperator.com/

Rui Falhas Santos - is the Manager of Data Analytics at one of the largest banks in the world. He joined us to discuss the AI applications they have implemented. The creativity and the use case are very captivating, as it is applied in one of the most regulated and legacy-bound industries. Rui is a hands-on manager with an extensive background in data. His data team helped build a solution that reads, on average, 300k articles a month to catch any alarming signals about their clients. Additionally, we discussed responsible AI from the decision-making side of model output and how data is collected for model processing.