CluedIn: Graph-Based MDM With Agentic Data Management
CluedIn differentiates itself in the master data management (MDM) market through its graph-based architecture, schema-flexible ingestion model, AI integration, and deep alignment with the Microsoft Azure ecosystem. AI-assisted agent data management and support for federated deployments enable scalability and semiautomated data governance.
Technology Highlights:
- Multidomain MDM
- Golden Records & Survivorship
- Reference Data Management
- Hierarchies & Relationship Management
- Data Integration
- Workflow & Federation
- Governance & Stewardship
- Data Quality Management
- AI Agents for Data Management
CluedIn’s graph enables a “no up-front schema” approach, which allows organizations to ingest data without predefined models. This design supports flexible and iterative relationship-building across multiple business domains, reducing the need for extensive modelling before integration begins.
CluedIn was the first MDM platform to integrate Azure OpenAI for data management processes. The company subsequently released CluedIn Copilot – the first AI assistant experience in the MDM space – allowing users to interact with data and metadata using natural language.
The platform has tight integration with Microsoft Purview, providing automatic data lineage generation and asset tagging within the Purview data catalog. This integration aligns CluedIn closely with Microsoft’s broader data governance ecosystem.
In terms of record management, CluedIn supports both automated and manual master record creation. Users can leverage workflow engines and form builders or extend functionality further using native Power Apps integration for complex or industry-specific scenarios.
CluedIn also supports unstructured data types such as documents, images, and presentations. Using AI models, it performs entity recognition and relationship mapping, incorporating insights from unstructured sources into its graph model.
For enterprise-scale environments, CluedIn supports federated deployment models. This allows local business units to maintain their own MDM instances that roll up into a central system – an approach used by customers in insurance and facility services sectors.
Source: CluedIn, accessed December 2025.
CluedIn uses AI agents to monitor and suggest data quality improvements. What is interesting in their approach: Instead of one Copilot-like AI working behind the scenes, users can choose from a list of different explicitly defined roles (Data Steward, Data Architect, Data Enricher, Data Compliance Expert, etc.) to build their own data management team. Each agent can run in observe-only, suggest, or auto-fix modes. Each action is logged with a full audit trail to maintain governance and transparency.
Our Take
CluedIn may be a good choice for organizations with strategic reliance on Microsoft Azure cloud. It provides key functionality that otherwise would have to be built by MS Fabric users themselves: master data and data quality management. It’s easy to deploy (no up-front schema) and to run (using virtual data agents), and it’s fully integrated with Microsoft’s security tools (Purview and Entra).
It also provides a prebuilt interface to Azure OpenAI and enables unstructured data processing. Overall, CluedIn offers quite a versatile bundle of features in one cohesive platform. But it is limited to the Microsoft Azure environment.
Here’s a quick comparison:
|
Traditional MDM trait |
CluedIn’s approach |
|
Heavy up-front data modelling, schema design, long “data project” before value. |
User can start ingesting first and do modelling later (with the help of AI) – reaching the value sooner. |
|
Relational/rigid data models; limited flexibility in linking relationships. |
CluedIn uses graph modelling to enable first-class relationships. |
|
Domain-by-domain rollout (e.g. start with customer, then product) because each domain is siloed. |
CluedIn promotes multidomain support by default and bridges structured and unstructured data. |
|
Manual matching, deduplication, a lot of human effort. |
CluedIn enables automation, machine learning, and virtual agents for data quality. |
Considerations:
- While CluedIn touts flexibility, you still need to define entities, relationships, governance, stewardship, etc.
- Adoption of a modern platform may require new skills (graph modelling, cloud operations, etc.).
- Established MDM vendors might have deeper domain-specific features and mature communities; CluedIn’s modern approach may still evolve in some edge areas.