Taxonomy Principles

To help ensure that the open analytics taxonomy will enable data scientists to collect better data and model more effectively, we’ve created a set of core principles. Any new development or update will be checked against these principles.

The open analytics taxonomy should:

  1. Give data scientists the best possible data to work with for modeling.
  2. Be supported by tooling to implement it, with as little manual work as possible.
  3. Be covered by automated validation.
  4. Work for any website, web-app or app built on modern, well adopted frameworks.
  5. Be consistent across events, contexts, frameworks, and platforms.
  6. Have a clear intended instrumentation & modeling use for each element.
  7. Never create unnecessary complexity or inheritance.

These principles are a starting point and we’ll adapt them along the way if that proves to be necessary to reach the taxonomy goals. They are open to feedback and contribution.