Track 4: Dynamic Meshes

How to DataMesh - A practitioner’s view

Data Mesh is a socio-technical paradigm on how to shape an organization to leverage the value of data even in the presence of frequent change and massive growth. The four main principles of Data Mesh propose organizational changes towards a decentralized, domain-oriented mesh of data products with a centralized, automated governance structure. Since this is a clear shift from traditional centralized data organization with dedicated data analytics teams,
the change comes with additional challenges of its own.

In this workshop, we develop a common understanding of Data Mesh, its four core principles, and how they combined help to overcome challenges in today's data ecosystem. We will discuss the most prevalent questions based on our project experience and will take you on a journey through the whole process of how to start, scale and sustain a Data Mesh.

Learning goal

  • Understand the basic components of Data Mesh and how they interoperate

  • Learn the importance of product thinking applied to the data ecosystem

  • How to initiate, scale, and sustain a mesh of data products

  • What are the challenges of a federated approach to governance

  • Common pitfalls and best practices to avoid them

Pre-requisites

  • None

  • Moritz Haag

    Moritz holds a PhD in Computational/Theoretical Chemistry and a M.Sc. in Interdisciplinary Sciences from the Swiss Federal Institute of Technology (ETH Zurich). He also holds a B.Sc. in Chemistry and Biochemistry from the University in Munich. Moritz joined the team in 2022.

  • Nino Weingart

    Nino holds an M.Sc. and B.Sc. in Computer Science from Swiss Federal Institute of Technology (ETH). Before joining D ONE, Nino worked as a Software and Machine Learning Engineer in Switzerland. Nino also works as a Lecturer on the topic “AI for Manager” and has been with the team since 2021.