Understanding the Real Trading Partner
The organization provides employment background screening, workforce verification, drug and health testing, and related workforce-risk services for enterprise employers across regulated and high-volume industries.
Years of growth and acquisition activity created a complex data environment. Different businesses and systems maintained their own versions of vendors, customers, and other organizations, often using different names, addresses, and data-entry conventions.
As the company consolidated its data platforms under a single team and standardized on Databricks, it needed its master-data architecture to align with that same direction rather than introducing another hosted MDM environment.
The enterprise needed to answer what appeared to be a straightforward question:
How much business are we actually doing with this vendor or legal entity?
The answer was difficult because the same organization could appear multiple times across source systems under different names, addresses, or account records.
Duplicate Organization Records
A single trading partner could appear several times across different system instances.
Those records often looked unrelated even when they represented the same underlying organization.
That meant spend, contracts, and relationships could be distributed across multiple records instead of being associated with one trusted enterprise identity.
Hidden Corporate Relationships
The problem extended beyond straightforward duplicate records.
Subsidiaries could have different names, locations, addresses, and master-data attributes while still rolling up to a common parent organization from a purchasing-agreement perspective.
Without understanding those relationships, procurement could not easily calculate total exposure or spend at the corporate-parent level.
Manual Spend Reconciliation
Finance and procurement teams had to reconcile duplicate vendor records before they could create reliable spend reports.
That slowed reporting and made contract negotiations more difficult because teams did not begin with a defensible view of total business conducted with the legal entity.
Limited Sales Visibility
Fragmented organization identities also affected sales.
Teams could not clearly understand whitespace or greyspace within a strategic account or across its subsidiaries because related accounts were not consistently connected under one enterprise identity.
A Trading Partner Master in Databricks
LakeFusion established a consolidated Trading Partner master across customer, vendor, and prospect records.
Data from Salesforce, Oracle EBS, and Microsoft Dynamics 365 contained company information such as organization names, billing addresses, and contact details.
LakeFusion used LLM-based entity resolution to identify duplicate accounts and assign a single master identity across the fragmented records.
Once the mastered entity was established, D&B data enriched the golden record with DUNS numbers, corporate-parent information, and additional firmographic context.
This allowed the organization to understand not only whether two accounts represented the same business, but also how multiple accounts and subsidiaries rolled up to a larger legal entity.
D&B-Enriched Organization Identity
D&B enrichment was especially important because the enterprise already used D&B information within Salesforce.
Instead of creating a new corporate-identity framework that business teams would need to learn, LakeFusion aligned mastered Databricks records with an external company-data foundation already familiar to users.
That gave the organization a consistent link between its mastered Trading Partner entities, DUNS identifiers, and corporate hierarchy.
How LakeFusion Implemented the Trading Partner Master
The implementation combined profiling, entity resolution, survivorship, stewardship, and enrichment across approximately 340,000 source records.
Data Profiling and Assessment
LakeFusion began by documenting inconsistent organization names, addresses, and other vendor information across the different system instances.
A sample vendor dataset was mastered early in the engagement to validate whether the proposed matching approach could produce acceptable results before the broader scope was finalized.
Matching and Linking Strategy
Matching logic was designed to balance automation with stewardship.
High-confidence records could move through automated matching and merging, while ambiguous cases could be reviewed manually.
The approach also considered whether match quality would remain consistent as record volumes increased rather than optimizing only for the initial sample.
Golden Record Creation
Once records were resolved, LakeFusion created trusted Trading Partner identities using survivorship rules.
Cross-references back to the original source records were maintained so the mastered entity remained connected to the systems that contributed its data.
Data Quality and D&B Enrichment
Databricks notebooks and D&B data were used to standardize and enrich the mastered records.
Corporate-parent details, DUNS numbers, and firmographic information were appended to LakeFusion golden records, creating richer enterprise context around each trading partner.
Business Value Delivered
The Trading Partner master gave the enterprise a consistent organization identity that could be used across finance, procurement, sales, and data operations.
Defensible Total Spend
Procurement and finance can report more accurately on total business conducted with a single vendor and understand which accounts roll up to the same corporate parent.
That gives teams a more defensible spend figure going into vendor and contract negotiations.
One View Across Duplicates
Vendor records previously distributed across four to five system instances can be consolidated around a single mastered identity.
This reduces the manual reconciliation work that previously had to happen before spend reporting.
Corporate Parent Visibility
D&B enrichment adds corporate parent, DUNS, and firmographic context to LakeFusion golden records.
This helps teams understand how individual accounts relate to larger organizations rather than evaluating each local record in isolation.
Databricks as the Master-Data Foundation
The enterprise had already committed to consolidating its data platform on Databricks.
LakeFusion allowed master data to follow the same architecture, making Databricks the system of record for the vendor master rather than introducing another separately hosted MDM platform.
The design also avoids moving mastered data into an external MDM environment.
Update Once Through LakeFusion
Business users can update a vendor through the LakeFusion interface instead of repeating the same update independently across every source system.
This provides a more practical operational layer around the mastered organization identity.
What Made the Initiative Successful
Several decisions helped keep the implementation focused on a measurable business problem.
Databricks Strategy Came First
The enterprise had already chosen Databricks as its consolidated data platform.
That made a Databricks-aligned MDM layer a natural architectural fit and avoided reopening the question of whether master data should live in another separate environment.
One Data Platform Team
Bringing the organization’s data platforms under one team created clear ownership and simplified access to the source data required for mastering.
Match Quality Was Proven Early
Running real vendor records through matching early gave stakeholders evidence of match quality before the complete project scope was finalized.
This helped turn matching performance from an assumption into something that could be evaluated against real enterprise data.
The First Domain Stayed Focused
The initial business problem was deliberately narrow: consolidate duplicate vendor and business-entity records and understand their corporate-parent relationships.
That gave the implementation a concrete outcome—reliable total spend—rather than beginning with an overly broad enterprise MDM program.
Matching Was Refined Iteratively
Multiple testing and tuning cycles were used to validate that match quality continued to hold as the implementation scaled.
Automation handled straightforward cases, while steward review remained available for edge cases where additional business judgment was required.
From Fragmented Vendor Records to a Trusted Trading Partner Foundation
The enterprise started with a fundamental visibility problem: the same vendor could appear multiple times across its systems under different names and addresses.
That meant every spend report and contract negotiation could begin with manual reconciliation before the organization could determine how much business it actually conducted with a vendor or corporate parent.
LakeFusion changed that by mastering Trading Partner data directly in Databricks.
LLM-based entity resolution connects duplicate accounts to a single organization identity, while D&B enrichment adds the DUNS and corporate-parent context required to understand the broader business relationship.
The first domain now provides a foundation that can be extended into additional master-data domains as the enterprise expands its MDM strategy.
Build a Trusted Trading Partner Master with LakeFusion
LakeFusion helps enterprises resolve fragmented vendor, customer, company, and other business-entity records directly within Databricks.
By combining AI-powered entity resolution, golden records, survivorship, stewardship, corporate hierarchy, and external enrichment, LakeFusion gives procurement, finance, sales, and data teams a trusted view of the organizations they do business with without introducing another disconnected MDM stack.
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