New Perspectives on Team Topologies: Organizing Data Teams for Fast Flow
Join Manuel Pais, co-author of Team Topologies (2nd Edition), for a conversation with Actian’s Chief Evangelist Ole Olesen-Bagneux on how organizations can structure teams around data platforms, AI capabilities, and modern technology ecosystems to sustain fast flow of value.
Date: April 7, 2026
Time: 5:00–6:00 PM CET | 11:00 AM–12:00 PM EST
Format: Live Webinar (with audience Q&A)
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This is Episode 7 of Data, Explored, Actian’s webinar series spotlighting real conversations about the future of data, governance, and AI.
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About the Webinar
Modern enterprises operate multiple platforms at once—application platforms, infrastructure platforms, and increasingly data, AI, and analytics platforms. As a result, teams such as data engineers, data scientists, analytics engineers, and ML engineers must collaborate across domains while managing growing technical and organizational complexity.
So how should organizations structure teams so that value can flow quickly without overwhelming people with cognitive load?
In this episode of Data, Explored, we welcome Manuel Pais, co-author of the influential book Team Topologies, to revisit the principles that have reshaped how organizations design teams and platform capabilities. The Team Topologies model introduced a practical framework for organizing teams around the fast flow of value while managing team cognitive load, defining four team types and three interaction patterns that help organizations scale delivery sustainably.
But today’s environments differ from those in 2019, when the book was first published. Platform ecosystems have multiplied, data platforms have become central to enterprise architecture, and AI capabilities are increasingly embedded across systems and teams.
Topics Explored
- What Team Topologies is and why it reshaped how companies organize technology teams
- How organizations are typically structured today—and why that often slows delivery
- The growing role of platform teams, including data, analytics, and AI platforms
- How data engineers, data scientists, analytics engineers, and ML practitioners fit into modern team structures
- Whether the principles of fast flow and cognitive load still hold as data and AI ecosystems expand
Who Should Watch?
This session is designed for leaders and practitioners shaping modern data and technology organizations:
- Chief Data Officers and data platform leaders
- Data architects and platform architects
- Data engineers, analytics engineers, and ML engineers
- Engineering and platform leaders
- Enterprise architects and transformation leaders
Featured Speakers
Manuel Pais
Co-Author, Team Topologies | Fast Flow Thought Leader
Co-author of the industry-changing book “Team Topologies”, Manuel is on a mission to both make work more humane and businesses more valuable to customers. Manuel delivers motivational keynotes and practical masterclasses on the principles and practices for fast flow that resonate across the organization, from C-level executives to the teams on the ground. He has also helped several Fortune 500 companies pave the path for accelerated value delivery via strategic & organizational assessments. On the product side, Manuel has led the creation and evolution of the Team Topologies Academy, providing online, worldwide access to the latest insights around fast flow, and Teamperature, the industry-leading model for assessing and managing team cognitive load.
Ole Olesen-Bagneux
Chief Evangelist, Actian | O’Reilly Author
Ole Olesen-Bagneux is a globally recognized thought leader in metadata management and enterprise data architecture. As VP, Chief Evangelist at Actian, he drives industry awareness and adoption of modern approaches to data intelligence. Ole is the best-selling author of Fundamentals of Metadata Management (O’Reilly, 2025) and The Enterprise Data Catalog (O’Reilly, 2023). With a PhD in Library and Information Science from the University of Copenhagen, he brings a unique perspective that bridges information science and modern data management.
