Entity model
Represent organizations, offices, teams, people, affiliations, source records, and relationships separately rather than forcing all information into one account and contact pair.
Representative implementation
Resolving duplicate firms and contacts across a wealthtech stack
A representative data-quality workflow for matching firms, branches, teams, advisors, and contacts without discarding valuable relationship history.
Firm profile
An asset manager using CRM, advisor-intelligence data, marketing automation, event lists, broker-dealer files, and internal spreadsheets to support distribution.
Operating trigger
The same real-world firm or person appears under several names and identifiers. Reporting, territory ownership, personalization, and AI retrieval inherit the ambiguity.
Systems in scope
The workflow crosses system boundaries.
Current state
What makes the workflow break down.
Firm names vary by legal entity, brand, office, branch, parent, and source-specific convention.
Contacts are duplicated when emails, affiliations, or employers change.
Automated merges risk combining separate people or deleting important source history.
Territories and campaigns use different assumptions about which level of the hierarchy matters.
AI and analytics retrieve incomplete or contradictory context because identity is unresolved.
Solution architecture
A controlled operating design.
Matching strategy
Combine durable source IDs, normalized attributes, exact rules, scored comparisons, and explicit non-match conditions.
Golden record policy
Select values by field-level authority and freshness while retaining source lineage instead of destroying non-selected records.
Stewardship queue
Send ambiguous matches, conflicting hierarchies, and consequential affiliation changes to an accountable reviewer.
Implementation sequence
Prove the workflow before expanding it.
Profile and classify
Measure duplicates, missing identifiers, hierarchy patterns, source conflicts, and downstream dependencies.
Build labeled examples
Create known match, non-match, and ambiguous pairs covering common names, advisor moves, shared domains, and branch structures.
Run without merging
Generate proposed entity links and golden values first so users can evaluate quality without changing production records.
Release by confidence and impact
Automate high-confidence, low-risk decisions and route uncertain or consequential cases to stewardship.
Controls
What keeps the workflow dependable.
- Reversible links and complete source lineage
- Explicit non-match and do-not-merge decisions
- Human review for ambiguous people and hierarchy changes
- Downstream impact preview before material merges
- Ongoing monitoring as new source data arrives
Target state
What changes after implementation.
- Systems can reference a shared entity while preserving vendor and application identifiers.
- Firm hierarchy, team membership, and historical affiliations are explicit relationships.
- Territory, segmentation, reporting, and AI workflows receive more coherent context.
- Data stewards work a bounded exception queue instead of repairing downstream symptoms.
Measurement
Metrics to baseline and track.
Duplicate and unresolved-record rate
Precision and recall on labeled match examples
Manual decisions per thousand incoming records
Downstream territory or campaign exceptions caused by identity
Time required to resolve advisor affiliation changes
Evidence note
Identity resolution is probabilistic when source data is incomplete. The operating design should favor explainable, reversible decisions over aggressive automatic merging.
Have a version of this workflow inside your firm?
We can map the current state, identify a credible first release, and define the controls and measures required to operate it.
Discuss this use case