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Enterprise data teams have put years into pulling information into the lakehouse. All customer records, supplier information, products, accounts, locations, and operational datasets can live in Databricks, but having them in one place doesn't automatically mean they are trusted.
There are still duplicate entries. The same customer can look different across CRM and ERP systems. Supplier names can change by region. Product records can hold conflicting details. Data engineering teams can clean individual datasets, but downstream applications and AI still need to know which record represents the right entity.
That is where LakeFusion Master Data Management becomes important.
LakeFusion brings Master Data Management directly into the Databricks environment, helping enterprises resolve and govern master data without moving it into a separate external MDM platform. Governed golden records remain close to the analytics, applications, and AI workloads that need them.
What Is LakeFusion Master Data Management?
LakeFusion Master Data Management is best master data management platform built on Databricks to master data already stored and processed within the lakehouse.
The objective remains the same as in the standard MDM: provide trusted and governed representations of the critical enterprise entities. What changes is the architecture.
Traditional MDM platforms often force organizations to pull data out of source systems or the lakehouse, master it somewhere else, and then push the mastered records back downstream. A Databricks-native approach keeps the mastering work closer to the enterprise data platform itself.
With LakeFusion MDM, mastered entities and golden records stay inside the customer's Databricks environment instead of moving to an external MDM repository. LakeFusion is built specifically around this architecture.
Why Enterprise Data Teams Need MDM Even After Building a Lakehouse
A lakehouse resolves all the big issues of storage, processing, analysis and data engineering. It does not automatically determine if two records refer to the same entity in the real world. That difference matters.
Duplicate Entities Remain Duplicate
For any given customer, the name in one system may appear as ABC Holdings Ltd., ABC Holdings or a different subsidiary name in another. Determining whether records belong to the same enterprise entity requires dedicated matching and entity resolution logic beyond basic data cleansing.
Source Systems Still Disagree
CRM, ERP, procurement, billing, product, and operational platforms may each hold different values for the same entity. Enterprise teams need clear rules for deciding which attributes win when records get consolidated.
Relationships Need Governance
Master data is rarely flat. Companies have subsidiaries. Customers belong to households or corporate groups. Suppliers work through hierarchies. Products sit inside categories and taxonomies. MDM supplies the governance and relationship context that basic data cleansing cannot deliver.
AI Needs Trusted Entities
AI applications may reach huge amounts of lakehouse data, but inconsistent identities can still produce inconsistent answers. Before enterprise AI can reliably reason about customers, suppliers, products, or organizations, those entities need to be resolved and governed.
How LakeFusion Master Data Management Works on Databricks
LakeFusion MDM works with enterprise records already available within the Databricks lakehouse, applying the mastering processes needed to profile, match, consolidate, govern, and operationalize trusted entity data.
1. Connect Enterprise Data
Customer, supplier, product, account, place, and reference data may be sourced from CRM, ERP, procurement systems, operational systems, and external databases.
LakeFusion works directly with data in the Databricks environment, avoiding the need to introduce a separate external mastering repository for the MDM lifecycle.
2. Profile Data Quality
Before matching starts, teams need visibility into missing attributes, inconsistent formatting, duplicate patterns, and other quality issues. Profiling helps show which fields are reliable enough to support entity matching and survivorship.
3. Resolve Duplicate Entities
Entity resolution decides which records represent the same real-world entity. This may use deterministic rules, similarity-based techniques, probabilistic matching, and AI-assisted approaches depending on the data and business needs.
LakeFusion uses AI-driven matching capabilities on Databricks to support enterprise entity resolution. Databricks has also published technical material describing LakeFusion's native MDM architecture and AI-driven approach.
4. Match and Merge Records
Once duplicate records are identified, matching and merge logic decides how they should be consolidated. The goal is not simply deleting duplicates. It is creating a governed representation that keeps the most trustworthy information from the contributing sources.
5. Create Golden Records
The resulting golden record becomes the governed representation of the entity. For example, multiple customer records across CRM and ERP systems can be resolved into one customer golden record with source lineage and controlled survivorship logic.
6. Apply Stewardship and Governance
Not every match decision should run fully automated. Data stewards may need to review uncertain matches, approve merges, correct records, or handle exceptions.
LakeFusion provides no-code stewardship capabilities for operations such as approving matches, unmatching records, updating records, and managing golden records, with Databricks providing the underlying processing environment.
Which Enterprise Data Domains Can LakeFusion Support?
LakeFusion Master Data Management is not limited to customer data. Enterprise teams may need governance across several domains, from suppliers and products to organizational, location, reference and asset data. LakeFusion supports multidomain MDM on Databricks, helping teams standardize, govern and connect critical enterprise entities within the same trusted data environment.
Customer Data
Resolve fragmented customer identities across CRM, ERP, billing and operational systems to support governed golden records and Customer 360 use cases.
Supplier Data
Standardize supplier identities, hierarchies and procurement-related records across enterprise systems to reduce duplicate and inconsistent supplier data.
Product Data
Govern product records, attributes, SKUs, classifications and related information across enterprise environments. LakeFusion also provides dedicated PIM capabilities for product-data management.
Company and Organizational Data
Resolve companies, parent entities, subsidiaries and related organizational records to create trusted company master data and governed hierarchies.
Location and Reference Data
LakeFusion's MDM architecture supports location and reference-data domains as part of multidomain enterprise data-management programs.
Asset, Material and Plant Data
For manufacturing environments, LakeFusion supports product and part masters, supplier and material data, plant and equipment hierarchies, inventory and location information.
What Makes LakeFusion MDM Different?
LakeFusion MDM is built for enterprise teams that want to master data within Databricks instead of adding another disconnected MDM layer. It combines entity resolution, golden record management, stewardship, multidomain support, relationship intelligence, and governance within the existing lakehouse environment.
AI-Assisted Entity Resolution
LakeFusion supports deterministic, probabilistic, similarity-based, and AI-assisted matching to identify duplicate and related records across fragmented enterprise systems.
Governed Golden Records
Matched records can be consolidated into trusted golden records using survivorship logic, source lineage, and governed merge decisions.
Data Stewardship
LakeFusion provides stewardship capabilities that allow teams to review matches, manage exceptions, update records, and maintain data quality without relying entirely on engineering teams.
Multidomain MDM
LakeFusion supports governance across multiple enterprise data domains, including customer, supplier, product, company, location, reference, and other critical master data.
Relationship and Hierarchy Management
Master data is not limited to isolated records. LakeFusion helps manage hierarchies, parent-child relationships, corporate structures, and connected entities that add context to enterprise data.
Built on Databricks
LakeFusion MDM operates within the Databricks environment, keeping mastered data close to existing analytics, AI, and data engineering workloads while reducing the need for a separate external MDM repository.
Unity Catalog-Aligned Governance
Mastered data can remain aligned with existing Databricks governance, access controls, lineage, and enterprise policies through Unity Catalog.
Operational Access to Trusted Data
Governed master data can be made available to downstream analytics, applications, pipelines, and AI workloads without introducing another complex extraction and synchronization layer.
Build Trusted Enterprise Data with LakeFusion on Databricks
LakeFusion brings enterprise Master Data Management directly to the Databricks environment, helping data teams resolve fragmented entities, create governed golden records, manage complex relationships, and operationalize trusted master data without adding another disconnected MDM stack.
From entity resolution and match-and-merge to stewardship, multidomain MDM, Customer 360, and Unity Catalog-aligned governance, LakeFusion gives enterprises the capabilities required to turn fragmented lakehouse data into trusted entity data for analytics, applications, and AI.
If your enterprise already runs data and AI workloads on Databricks, LakeFusion lets you bring the mastering layer to that environment instead of moving governed enterprise data somewhere else.
Explore LakeFusion Master Data Management
Frequently Asked Questions
What is LakeFusion MDM?
LakeFusion MDM is a Master Data Management solution built on Databricks that helps enterprises resolve fragmented entities, create governed golden records, manage stewardship, and operationalize trusted master data within their existing lakehouse environment.
Does Databricks provide Master Data Management itself?
Databricks provides the underlying data, analytics, AI, and governance platform. LakeFusion adds specialized Master Data Management capabilities on Databricks, including entity resolution, match and merge, survivorship, stewardship, golden records, hierarchy management, and related MDM functionality.
What is the advantage of running LakeFusion MDM on Databricks?
Running LakeFusion MDM on Databricks keeps mastering closer to enterprise data, reducing reliance on external MDM repositories and additional synchronization layers while preserving access to existing analytics, AI, and governance workflows.
How does LakeFusion MDM work with Unity Catalog?
Unity Catalog provides centralized governance features for access control, lineage, auditing, and discovery. With Databricks-native MDM, mastered data stays part of that overarching governance environment.
Can LakeFusion MDM support Customer 360?
Yes. LakeFusion can resolve fragmented customer identities across CRM, ERP, billing, and operational systems to create governed customer golden records that support Customer 360 analytics and downstream operational use cases.
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