CRM Hygiene & Data Quality

Define 5-7 critical data quality rules in a spreadsheet: required fields (contact name, email, company, stage), valid values (stage must be in defined list), no duplicates, recent activity (last touch <=30 days), complete BANT/MEDDIC for qualified deals.

Audit 50 records in Attio manually; for each, check compliance with data quality rules; calculate data quality score (% of rules passed) and identify most common errors (missing fields, stale data, duplicates).

Set pass threshold: achieve >=80% data quality score across audited records and reduce critical errors (missing required fields, duplicates) by >=50% within 1 week.

Fix identified errors manually: fill missing fields, merge duplicates, update stale records, correct invalid stage values; document time spent on cleanup.

Create data entry guidelines for sales team: "Required fields before advancing stage", "Update contact info after every call", "Log all activities in Attio within 24 hours", "Merge duplicates immediately when found".

Log data quality issues in a spreadsheet with columns: issue_type, frequency, impact (high/medium/low), owner, resolution; prioritize fixing high-impact issues.

In PostHog, create events for data_quality_issue_detected and data_quality_issue_resolved with properties for issue type and resolution time.

After cleanup, audit same 50 records again; measure improvement in data quality score; if >=80% score achieved, cleanup was effective.

Calculate ROI of data quality: estimate time saved on reporting, reduced duplicate outreach, improved forecasting accuracy; if benefits exceed cleanup time by >=3x, data hygiene is valuable.

If >=80% data quality achieved and ROI is positive, document data quality standards and proceed to Baseline; otherwise refine rules or improve enforcement.