Every CRM cleanup project ends the same way: a tidy export, a careful reimport, and six weeks later the same mess. The data was never the problem. The entry point was.
Form submissions, imports and integrations each write values in their own shape. Unless something normalizes them on arrival, inconsistency accumulates faster than anyone can clean it.
Normalize on create and on change
Phone numbers are the obvious case: formatting varies, leading zeros come and go, country codes are missing. Converting to a single international format as the record is created means dialler integrations work and duplicate detection has a fair chance.
Names and email addresses are quieter offenders. Block capitals and stray whitespace make personalisation look careless and cause segmentation to miss records that should have matched.
Split and reshape what arrives as one field
A single full name field is convenient for the visitor and useless for personalisation. Splitting it correctly means handling titles and surname particles, which simple operators cannot express.
The same applies to references buried in free text. A reference number inside a notes field cannot be reported on or matched against until something extracts it into a property of its own.
Translate between systems deliberately
Your CRM says United Kingdom and the system downstream expects GB. Left implicit, that mapping ends up duplicated inside every integration, or silently dropped.
Holding the translation as readable mapping lines keeps it in one place, visible to the people who maintain it rather than buried in code.
Build it
Each of these is a working pattern with the exact steps, using the actions this article describes.
The test of a data quality process is whether the next quarterly audit finds anything. Normalizing at entry is what makes that answer boring.