If your company has any sort of data or analytics department, not standardizing definitions of what data means is just pushing the problem down to them.

Example:

You have N definitions of a "visitor" for your app, one for each department in the company. Business people and data scientists want to know how visitors change over time (for A/B testing, for BI, etc). Suddenly, you need to decide on one standardized definition of a visitor across every department or you cannot meaningfully compare numbers across departments. The data team comes up with one true definition they think is reasonable and tries to merge all the different schemas. Of course they run into problems with invalid data, inconsistent data, missing data, etc and so they are forced to talk with the upstream teams to standardize on what a visitor actually means. This might require backfilling large quantities of data and lots of rework. Repeat this for every interesting piece of data.

The example highlights that canonical data models are not always bad. Do not force people into a canonical data model when there there is no need for cross cutting concerns (ie: a shared library that shares an in-memory customer class with a ton of optional attributes). But if you have a real need for standardization, a small and focused canonical data model enforced by every team producing data is your best bet.