Privacy-Compliant Identity Graph Build (Delivery)

Privacy-Compliant Identity Graph Build (Delivery)

1. 01

   Map the client's data sources and consent status

   Inventory CRM, product analytics, and marketing platforms, then classify each record by consent level and data origin to establish what can legally be unified.

2. 02

   Define the golden record schema

   Agree with the client on which fields constitute a persistent profile, such as email, company, and lifecycle stage, and document the source hierarchy for conflicting values.

3. 03

   Run deterministic matching first

   Match records on exact identifiers like email or user ID, then log unmatched records for probabilistic review, keeping a clear audit trail of match confidence.

4. 04

   Apply probabilistic matching with a confidence threshold

   Use fuzzy logic on attributes like name and domain, set a minimum score (for example 0.85) to avoid false merges, and flag borderline cases for manual review.

5. 05

   Resolve conflicts and merge profiles

   Merge duplicate records into a single profile, preserving the most recent or source-preferred values, and record the merge history for reversibility.

6. 06

   Validate the unified profiles against known test records

   Insert test contacts with known attributes before matching, then confirm they resolve correctly and that no unrelated records were merged.

7. 07

   Document the matching logic and data lineage

   Write a short spec covering match rules, thresholds, and source priorities, so the client's team can audit or adjust the graph without vendor lock-in.