Fragmented Company Data at Scale
The organization is a global professional services enterprise that relies on company information from internal systems and external data providers.
Maintaining consistent company identities across these sources was important for accurate search, reliable analytics, and compliance-related processes. As data volumes and source complexity increased, the organization needed a scalable approach to resolving entities and managing company information within its existing Databricks environment.
1. Duplicate Entities and Conflicting Firmographics
Company information was distributed across internal CRM data and multiple third-party providers. Different sources could contain separate records for the same company, along with conflicting firmographic attributes.
These inconsistencies made it difficult to maintain one authoritative view of each company and increased the complexity of downstream data use.
2. Manual Reconciliation Slowing Operations
Resolving inconsistencies across sources required manual effort. As the volume of company records grew beyond three million, the organization needed a more scalable process for identifying duplicates and consolidating company information.
3. Reduced Search Accuracy
Duplicate and inconsistent company records affected search accuracy. Users needed a more reliable way to find the correct company entity without navigating conflicting representations across sources.
4. Compliance Risk Exposure
Fragmented company information created additional challenges for compliance-related workflows. The organization needed better-controlled company data and a stronger governance foundation to support these processes.
AI-Powered Entity Resolution on Databricks
LakeFusion was deployed directly within the Databricks Lakehouse to consolidate company information and establish a governed Company Master.
The architecture aligned with the Medallion approach—Bronze, Silver, and Gold—and used Unity Catalog for governance. The implementation was designed to operate natively within Databricks, without external data movement.
AI-powered entity resolution helped identify company records representing the same entity. Custom LLM prompt refinement supported distinctions between parent and subsidiary companies and allowed the matching approach to reflect business-defined entity granularity.
The solution also introduced governed golden records, survivorship logic, firmographic conflict resolution, and centralized stewardship workflows.
How LakeFusion Implemented the Solution
1. Integrated Company Data Within the Lakehouse
LakeFusion operated directly within Databricks, bringing company data management into the existing lakehouse environment. The implementation followed the Bronze, Silver, and Gold Medallion Architecture and used Unity Catalog for governance.
This approach kept the solution within the Databricks environment without requiring external data movement.
2. Refined AI Matching for Business Requirements
The entity resolution process used custom LLM prompt refinement to address important business distinctions, including whether records represented parent or subsidiary entities.
Matching granularity was defined according to business requirements. Iterative tuning helped refine precision and performance, ensuring that entity resolution aligned with how the organization needed companies to be represented.
3. Established Governed Golden Records
The solution consolidated matched records into governed golden records using survivorship logic. Conflict resolution across firmographic attributes helped determine how inconsistent information should be handled.
This established a more consistent company representation while providing a structured approach to managing conflicting source values.
4. Centralized Stewardship and Governed Outputs
LakeFusion introduced centralized stewardship workflows to support the management of company records and data quality decisions.
Governed lakehouse outputs made consolidated company information available to downstream systems. This helped establish a consistent data foundation for business users and applications that relied on company information.
Business Value Delivered
A Single Authoritative Company View
The implementation consolidated millions of records into a trusted Company Master, providing a more consistent view of company entities across internal and external sources.
Faster, More Accurate Search Workflows
Resolving duplicate entities and conflicting firmographic information supported faster and more accurate company search workflows.
More Consistent Analytics
A consolidated company view helped improve consistency in analytics across teams, reducing discrepancies caused by fragmented entity records.
Reduced Manual Reconciliation
Governed entity resolution and golden-record management reduced the reliance on manual reconciliation across company data sources.
Stronger Compliance Foundation
The combination of governed company records and centralized stewardship established a stronger foundation for compliance-related workflows.
What Made the Initiative Successful
AI-first entity resolution: The approach supported company matching at a scale exceeding three million records.
Business-driven matching granularity: Custom prompt refinement addressed parent-versus-subsidiary distinctions and aligned matching decisions with business requirements.
Native Databricks architecture: The implementation operated directly within the lakehouse without external data movement.
Governance-first design: Unity Catalog governance, survivorship logic, and centralized stewardship supported controlled management of company information.
Iterative refinement: Matching precision and performance were refined through iterative tuning rather than relying on a fixed initial configuration.
Strategic Expansion and Future Opportunities
The Company Master established a foundation for extending entity mastering beyond company records. The identified expansion path includes People and Relationships domains, integration of additional third-party data through native connectors, and stronger compliance and Do Not Call (DNC) workflows.
The organization also identified a need to scale AI matching with a predictable cost strategy. These opportunities provide a direction for extending the trusted data foundation as requirements evolve.
Conclusion
For a global professional services enterprise managing more than three million company records, establishing a trusted Company Master was an important step toward improving search, analytics, and governance. LakeFusion brought AI-powered entity resolution, survivorship, conflict resolution, and stewardship directly into Databricks.
The result was a single authoritative company view supported by a governed lakehouse architecture. With a foundation for future People and Relationships domains, the organization established a repeatable approach to managing enterprise entity data at scale.
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