Modeling the Mesh: understanding data products and domains [Engelstalig]

Data Mesh, coined by Zhamak Dehghani, is a framework for federated data management and governance that gets a lot of attention from large organizations around the world facing problems with bottlenecked data teams and sprawling solution spaces. While the core principles of Data Mesh are well established in the literature, and practical implementation stories have started to emerge giving meat to the theoretical bones, some questions remain.
One of the biggest challenges is managing business context across multiple domains and data products. In this session, we will discuss how data modeling can be used to enable both within-domain design of understandable and discoverable data products as well as cross-domain understanding of domain boundaries, overlaps, and possibly conflicting business concepts. The well-known best practices of conceptual and logical data models prove their worth in this modern de-centralized framework by enabling semantic interoperability across different data products and domains, as well as allowing the organization to maintain a big picture of their data contents.

Topics and discussion points:

• Data Mesh basics – the paradigm and its principles
• Pros and cons of the Mesh approach
• Why context matters – semantic interoperability
• Data modeling as the key to context and semantics
• Modeling at two levels – data products and domains
• Maintaining the big picture and why the “Enterprise Data Model” is still a valid topic.