Who this is for

Teams relying on incomplete CRM, catalog, or operations data
Managers preparing a bounded migration or cleanup

The problem

Data cleanup becomes unsafe when workers guess missing values, overwrite source evidence, or process broad exports without a defined purpose and retention rule.

Scope

Record standardization and deduplication
Source-based enrichment
Migration preparation and validation
Data-quality exception queues

Inputs

Field dictionary and required formats
Authoritative sources
Match and merge rules
Access, export, and retention policy

Workflow

Stage 1
Profile
Measure missing, inconsistent, and duplicate fields before changes.
Stage 2
Transform
Apply documented formatting and source rules.
Stage 3
Validate
Compare samples and totals with the original source.
Stage 4
Resolve
Send ambiguous matches and policy questions to the data owner.

Deliverables

Cleaned approved dataset
Change and exception log
Validation summary
Updated field procedure

Boundaries and human decisions

Ambiguous identity, regulated classification, deletion, and policy decisions remain with the data owner
No unapproved scraping or enrichment source is used

Quality controls

Read-only source preservation
Before-and-after counts
Sample accuracy checks
Export and deletion restrictions

Starting scenarios

CRM cleanup

Standardize contact and account records without merging uncertain identities.

System migration

Prepare mapped records and reconcile counts before import approval.

Frequently asked questions

Will every missing field be filled?

No. Values need an approved source; unknown information remains identified as an exception.

Can source records be deleted?

Not by default. Retention and deletion require the responsible owner’s approval.

Operational handoff canvas

Bring one real queue to the intake.

Share representative work, the tools involved, the schedule, and the decisions that must remain with your team. We’ll use those inputs to discuss a bounded role.

Data Management