Jovita Tam, data and AI advisor with a background in engineering, law, and finance, joined Yuliia and Dumke to challenge how organizations approach governance. Jovita argues that data governance is a way of thinking, not a tool you purchase, explaining why culture eats strategy and why most governance programs fail at the checkbox exercise.  Jovita shares her approach to helping executives understand that governance should be an enabler, not an obstacle, and why treating it as purely compliance or cost center misses the point entirely.

Jovita's Linkedin - https://www.linkedin.com/in/jovitatam/

Jovita Tam: Why Data Governance Is a Way of Thinking, Not a Tool You Purchase

Adnan Hodzic, Lead Engineer and GenAI Delivery Lead at ING, joined Yuliia how ING successfully scaled generative AI from experimentation to enterprise production. With over 60 GenAI applications now running in production across the bank, Adnan explains ING's pragmatic approach: building internal AI platforms that balance innovation speed with regulatory compliance, treating European banking regulations as features rather than constraints, and fostering a culture where 300+ experiments can safely run while only the best reach production.
He discusses the critical role of their Prompt Flow Studio in democratizing AI development, why customer success teams saw immediate productivity gains, how ING structures AI governance without killing innovation, and his perspective on the hype cycle versus real enterprise value.


Adnan's blog: https://foolcontrol.org [https://foolcontrol.org/]

Adnan's Youtube channel: https://www.youtube.com/AdnanHodzic

LinkedIn: https://linkedin.com/in/AdnanHodzic

Twitter/X: https://twitter.com/fooctrl

Adnan Hodzic: Building Enterprise AI at ING

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 [https://www.linkedin.com/in/ryandolley/]

Ryan Dolley: The Modern Data Stack Bundling Era

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.

Thomas in't Veld: Business Value Requires Data Modeling, Not Tooling

Elliot Foreman and Andrew DeLave from ProsperOps joined Yuliia and Dumky to discuss automated cloud cost optimization through commitment management. As Google go-to-market director and senior FinOps specialist, they explain how their platform manages over $4 billion in cloud spend by automating reserved instances, committed use discounts, and savings plans across AWS, Azure, and Google Cloud. The conversation covers the psychology behind commitment hesitation, break-even point mathematics for cloud discounts, workload volatility optimization, and why they avoid AI in favor of deterministic algorithms for financial decisions. They share insights on managing complex multi-cloud environments, the human vs automation debate in FinOps, and practical strategies for reducing cloud costs while mitigating commitment risks.

Elliot Foreman & Andrew Delave: Math Beats AI in FinOps

Kasriel Kay, leading data democratization at Velotix, joined Yuliia and Dumke to challenge conventional wisdom about data governance and catalogs. Kasriel argues that data catalogs provide visibility but fail to deliver business value, comparing them to "buying JIRA and expecting agile practices." He advocates for shifting from restrictive data governance to data enablement through policy-based access control that considers user attributes, data sensitivity, and business context. Kasriel explains how AI-driven policy engines can learn from organizational behavior to automatically grant appropriate data access while maintaining compliance, ultimately reducing time-to-insight and unlocking missed business opportunities.

Kasriel Kay: From Data Governance to Data Enablement

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.
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Patrick Thompson: From Data Quality to Decision Quality - Building Structured Systems in an AI-First World

Kir Titievsky, Product Manager at Google Cloud with extensive experience in streaming and storage infrastructure, joined Yuliia and Dumky to talk about streaming. Drawing from his work with Apache Kafka, Cloud PubSub, Dataflow and Cloud Storage since 2015, Kir explains the fundamental differences between streaming and micro-batch processing. He challenges common misconceptions about streaming costs, explaining how streaming can be significantly less expensive than batch processing for many use cases. Kir shares insights on the "service bus architecture" revival, discussing how modern distributed messaging systems have solved historic bottlenecks while creating new opportunities for business and performance needs.

Kir's medium - https://medium.com/@kir-gcp
Kir's Linkedin page - https://www.linkedin.com/in/kir-titievsky-%F0%9F%87%BA%F0%9F%87%A6-7775052/

Kir Tititevsky: Modern Streaming Architecture Transforming the Service Bus

Serhii Sokolenko, founder at Tower Dev [https://tower.dev/] and former product manager at tech giants like Google Cloud, Snowflake, and Databricks, joined Yuliia to discuss his journey building a next-generation compute platform. Tower Dev aims to simplify data processing for data engineers who work with Python. Serhii explains how Tower addresses three key market trends: the integration of data engineering with AI through Python, the movement away from complex distributed processing frameworks, and users' desire for flexibility across different data platforms. He explains how Tower makes Python data applications more accessible by eliminating the need to learn complex frameworks while automatically scaling infrastructure. Sergei also shares his perspective on the future of data engineering, noting in which ways AI will transform the profession.

Tower Dev - https://tower.dev/
Serhii's Linkedin - https://www.linkedin.com/in/ssokolenko/

Serhii Sokolenko: Building a Python-First Compute Platform for Data Engineers

Andrii Yasinetsky, CTO and co-founder of Diadia Health and AI expert, joined Yuliia to share his perspective on the current AI landscape and its future implications. Currently Andrii and his team are building an AI-first healthcare platform focused on metabolic and hormonal health. Andrii talks about how AI is changing both technology stacks and business economics. We discuss what's wrong with AI claims in enterprises and why it doesn't match reality, he points out the decreasing cost of intelligence, and why the middle layer of tech jobs may disappear within five years. Andrii also shares his take on how the recent US administration change has created a "timeline split" that could dramatically accelerate AI innovation, potentially transforming the global economy.

Diadia Health - https://diadiahealth.com [https://diadiahealth.com/]

Andrii's Linkedin - https://www.linkedin.com/in/yasinetsky/



Andrii Yasinetsky: Building an AI Startup & Reshaping Healthcare, AI Democratization and Changes It Brings to Tech Jobs and World Economy

Data’s messy, and we don’t sugarcoat it. We dig into real-world stories—FinOps, data platforms, streaming, governance—with people who’ve been there, screwed up, figured it out, and can still laugh about it. No buzzwords, no corporate cheerleading—just curious questions, practical insights, and the occasional bad joke.

Yuliia Tkachova

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