The problem this solves
One-off data cleanups do not hold. Six weeks after the big deduplication, new duplicates arrive through forms and imports, reps invent new values for fields that already have conventions, and lifecycle stages drift from their definitions. The root cause is that bad data is a process symptom: every recurring data problem traces back to an entry point, an integration, or a workflow that keeps producing it. Cleaning data without fixing the process is renting cleanliness by the month.
How we work
We work both layers on a monthly rhythm. The data layer: scheduled hygiene passes covering duplicates, formatting, property completeness on the records that matter, and list health. Quality is measured with a consistent scorecard, so improvement is visible cycle over cycle instead of anecdotal.
The process layer is where the compounding happens. Each recurring data problem gets traced to its source, whether that is a form without validation, an import template nobody updated, an integration writing stale values, or a process step reps skip, and the source gets fixed. Over a few cycles the hygiene passes find less and less, which is the goal.
Process change requests from your team flow through the same backlog: a new lifecycle definition, a changed handoff rule, a property restructure. We assess the data impact before shipping, because most process edits have one.
Deliverables
- Monthly data quality scorecard with trend over cycles
- Recurring deduplication and hygiene passes
- Root cause fixes at data entry points and integrations
- Process adjustments shipped from a prioritized backlog
- Documented data standards and field conventions