Professional experience (anonymized)
A maintainable approach to CRM data quality
- Data
- Reports
Context
Over time, a CRM had accumulated duplicate and incomplete records that undermined trust in reports and dashboards.
Challenge
Inconsistent data made reporting unreliable and forced users to spend time correcting records instead of using them.
Constraints
Cleanup had to be careful and reversible where possible, without disrupting active users or losing legitimate history.
Role and responsibilities
Led data assessment, cleanup, validation, and the supporting reporting improvements.
Discovery and requirements
Profiled the data to quantify duplicates and gaps, then agreed on rules for matching, validation, and required fields.
Solution architecture
Combined controlled data imports and exports, deduplication, and validation with improvements to the reporting structure so quality would hold over time rather than degrade again.
Implementation
Performed deduplication and cleanup in stages, added validation to prevent recurrence, and refined reports to reflect the cleaner data model.
Security and testing
Validated cleanup against samples before applying broadly and preserved appropriate access controls throughout.
Outcome
Improved data quality, increased reporting accuracy, and reduced the time spent correcting CRM records.
Lessons and next steps
Data quality is sustained by prevention, not one-time cleanup. Validation rules and clear ownership keep the CRM trustworthy as it grows.
Relevant service
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