Perils of Centralization

A long time ago, I wrote a blog post about reasons to avoid a canonical data model. It keeps getting referred to and still creates some interesting conversations, so I thought it might make sense to write down my current thoughts about the topic.

First of all, the idea of simplifying an organization’s IT structure and architecture by “just” standardizing the data models is alive and well (though still as misguided as ever). These days, there are two places where it appears.

The first one is in connection with some sort of API-first strategy. It’s still extremely tempting to believe that once you’ve managed to just give everyone access to the same pool of well-managed, well-described APIs, they’ll simply be able to simply build their applications by using those APIs. It still considers efficiency by avoiding redundancy as the central tenet, and just as described in the discussion about CDMs in my old post, misses the much larger benefits of autonomy.

The second area where standardizing and centralizing models is firmly rooted as a best practice is in the data space. Luckily, there is a counter-movement: The data mesh approach fundamentally adopts the idea of decentralized responsibility, and is a great match for organizations that have adopted the concept of self-containend, value stream-oriented teams.

I believe that especially as money has become more expensive, creating centralized pools of (meta) data will be harder and harder to justify to stakeholders. It’s much better to do things that allow end-to-end teams to work in fast iterations that provide immediate feedback to the value they create. To my mind, this has been the defining characteristic of many successful architectural approaches in the past few years.