Agata Reistenbach Oleksy

Associate Director of Data Engineering Brenntag

Conference Day 1

3:30 PM Panel Discussion: Maintaining Trust, Security and Control as Data Products, Self Service and AI Scale

As data products, self service access, and AI adoption expand, governance complexity increases significantly. This panel brings together data, governance, and risk leaders to discuss how governance models evolve to support scale, automation, and regulatory pressure while still enabling teams to deliver at pace.

 Scaling governance across products, platforms, and AI workloads

 Aligning data, risk, and compliance teams around shared objectives

 Maintaining trust and control as data usage expands

Conference Day 2

11:30 AM Fireside Chat: What Breaks at Scale: Lessons from Growing Data Product Portfolios

Scaling data products exposes issues that rarely appear early on, from ownership gaps and governance strain to cost visibility and cultural resistance. This intimate fireside chat brings together enterprise leaders to share practical lessons from scaling data products and platforms, focusing on what breaks first, what requires redesign, and how organisations adapt operating models as scale increases.

 Identifying early warning signs that limit scalability

 Adapting ownership and governance models as portfolios grow

 Learning what successful organisations change as scale increases

2:00 PM Presentation: Moving Beyond Legacy: Evolving Data Platforms to Support Scalable Data Products

Many enterprises operate across fragmented data landscapes shaped by legacy systems, regional solutions, and inconsistent standards. As demand for data products and AI grows, these environments make it harder to deliver data that is consistent, reusable, and scalable.

Agata Oleksy explores how data engineering teams are introducing more structured data platforms, moving towards standardised, self-service capabilities while redefining what a data product means in practice. This includes aligning ownership with business context and creating platforms that are both usable and sustainable at scale.

 Standardising data platforms and engineering practices to reduce fragmentation across regions

 Defining data products in a way that reflects real business use cases rather than theory

 Enabling self-service adoption by balancing platform design, usability, and behaviour change

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