Chapter 5. Principle of Federated Computational Governance

For peace to reign on Earth, humans must evolve into new beings who have learned to see the whole first.

Immanuel Kant

A refreshed look at data governance is the missing and final piece to make data mesh work. So far, data mesh expects independent teams to own and serve their analytical data. It expects this data to be served as a product, accompanied by behavior that enriches the experience of the data consumer, to discover, trust, and use it for multiple purposes. And it heavily relies on a new set of self-serve data infrastructure capabilities to make it all feasible. Governance is the mechanism that assures that the mesh of independent data products, as a whole, is secure, trusted, and most importantly delivers value through the interconnection of its nodes.

I must admit, governance is one of those words that makes me, and perhaps many, feel uneasy. It evokes memories of central, rigid, authoritative decision-making systems and control processes. In the case of data governance, it evokes memories of central teams and processes that become bottlenecks in serving data, using data, and ultimately getting value from data.

Data governance teams and processes have noble objectives: ensuring the availability of safe, high quality, consistent, compliant, privacy-respecting, and usable data across an organization with managed risk. These objectives are well intended and necessary. However, traditionally, our approach in achieving ...

Get Data Mesh now with the O’Reilly learning platform.

O’Reilly members experience live online training, plus books, videos, and digital content from nearly 200 publishers.