Collecting data looks easy: add one more column, ask one more question. The problem arrives later. Fixing wrongly collected data costs far more than fixing a wrongly written rule, because a rule sits in one place while data accumulates everywhere.
When you change a rule, the effect starts from that moment. When you change a field, every record collected in the past stays in the old shape. Now there are two shapes, and reports have to count both. As the number of records grows, that distinction turns into a job of its own.
The three most common mistakes are these: collecting fields nobody will use, letting the same customer spread across several rows, and letting a field name drift over time. All three look harmless at the start and break the report later.
One question per field: which decision will this information change? If there is no answer, the field is not needed either. Data that gets collected but not used brings both a filling burden and a keeping responsibility.
If a new row opens whenever the same person writes from a different channel, the customer count looks larger than it is. Decide on a stable field that identifies the person and let the record update against that field.
Renaming a column can quietly return empty in every place that reads that name. Settle the name once and leave it; if it has to change, produce the list of places that read it first.
This is the second question, as important as collecting: which system holds the data, who can reach it, how long is it kept, and how is it removed when removal is asked for? If the answers to those four are not written down, the data has been collected but not owned.
We covered the legal side in a separate article: AI and Data Protection: Where Does Your Data Go?. This lesson looks at what you collect, that article at where what you collected sits. Two faces of one question.
We can review together whether the fields you collect are earning their place. Writing to us is enough.