The cost of the wrong schema
A fixed field set forces a choice between distorting your data to fit and keeping it outside the system. Both are expensive.
Information in free-text notes cannot be filtered, grouped or reported on. It is technically stored and practically lost, which is the worst of both arrangements because it looks like the data is captured.
What earns a field
The test is whether you will ever filter, group, sort or report on it. If yes, it needs to be a structured field with defined values. If it is narrative context for a human to read, notes are correct.
The second test is whether it will actually be filled. A field completed on a fifth of records makes reports misleading rather than partial, because the gaps are invisible in aggregate.
- Will you filter or report on it? Then it is a field.
- Is it narrative for a person to read? Then it is a note.
- Will it be filled most of the time? If not, reconsider.
- Does it duplicate something already captured? Remove one.
Field sprawl is the other failure
Customization makes adding fields easy, and easy things accumulate. The result is a record form with forty inputs where six matter, and a team that abandons the optional ones.
Reviewing field usage periodically — which are populated, which are never filtered on — and retiring the dead ones keeps the form honest. Removing a field is a legitimate act of maintenance, not an admission of error.
Customize the vocabulary too
Fields are one layer. Categories, lifecycle statuses, pipeline stages and event types are the vocabulary the team reasons with daily.
Where the software's default words do not match how your business talks, adoption suffers in a way that is hard to diagnose. Renaming to match existing language is usually the cheapest adoption improvement available.