Blog by Ole Wendland

The Data Dilemma, Part 1

When AI agents fail in a company, it is rarely the model’s fault. The real problem is the data: semantics, quality, and context are written down nowhere, they live in the heads of the domain experts. Data Mesh addresses exactly this problem, but it organizationally overwhelms many mid-sized companies. This first part describes why that is and derives a Minimum Viable Data Mesh from it. The second part shows the implementation in four layers.

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The Data Dilemma, Part 2

The first part showed why agents fail on undocumented data semantics and why Data Mesh is not a realistic answer for many mid-sized companies. This second part gets concrete: a Minimum Viable Data Mesh in four layers, from an honest inventory through curated views with ODCS contracts to agent access via MCP. A purchasing agent that compares suppliers and evaluates price histories serves as a running example.