Data Quality & Integrity Assessment
Data Quality & Integrity Assessment is a diagnostic engagement that measures the actual condition of your CRM data: duplicates, completeness, staleness, and consistency, with a per-field baseline instead of a general impression. It is built for companies that know their data is bad but cannot say where, how bad, or what it costs.
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
What buyers ask before scoping.
Do you clean the data as part of this assessment?
No, deliberately. Cleanup executed before the full picture exists tends to fix the visible problems and miss the recurring causes. The assessment produces the prioritized plan; execution is a separate scoped effort, whether run by us or your team. Bulk-safe quick wins are flagged as such in the plan.
How is this different from the AI Data Readiness Assessment?
This assessment measures operational data hygiene for everyday CRM use: dedup, completeness, consistency. The AI readiness assessment tests a different bar: whether records carry the context and connectivity AI workloads consume. A portal can pass this assessment and fail that one. If AI adoption is the driver, start there; if daily operations hurt, start here.
Our data problems keep coming back after every cleanup. Will this help?
That recurrence is a core focus. Repeat pollution always has mechanics: an unvalidated form, a broken sync mapping, an import routine without dedup, manual entry with no required fields. The assessment identifies which mechanisms refill the mess, because fixing those is the difference between cleaning your data and renting cleanliness.
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