The problem this solves
Everyone agrees the data is a mess; nobody can quantify it, so cleanup never gets prioritized against work with numbers attached. Meanwhile the mess taxes everything quietly: reps distrust the CRM and keep private lists, marketing emails bounce or hit the same person three times, reports undercount because key fields are blank, and automation misfires on records that stopped being true years ago. Bad data is rarely a crisis; it is a permanent drag that compounds.
How we work
We measure rather than estimate. Duplicate analysis across contacts and companies using layered matching, not just exact email match. Field-level completeness on the properties your processes actually depend on, which requires first establishing which those are. Staleness profiling: when records were last touched by a human versus a sync. Consistency checks: formats, picklist abuse, free-text fields where structured data should live.
Every finding gets sized and tied to its operational cost: this duplicate rate means this many broken email sends, this blank field breaks that routing rule. Data quality becomes a set of numbers with consequences instead of a vibe.
The deliverable is a data quality baseline report with a remediation plan ordered by impact per unit of effort: what to fix by bulk operation, what needs process change to stop recurring, and what is honestly not worth fixing. The baseline also gives you a before number, so post-cleanup progress is measurable rather than asserted.
Deliverables
- Duplicate analysis across contacts and companies with match-quality tiers
- Field-level completeness baseline on process-critical properties
- Staleness profile separating human activity from sync noise
- Consistency audit: formats, picklists, and structural misuse
- Remediation plan ordered by impact per effort, with recurrence fixes flagged