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What is Agentic Master Data Management?
Agentic Master Data Management (Agentic MDM) is an evolution of traditional Master Data Management where AI agents continuously manage, resolve, govern, and improve master data on a persistent knowledge graph rather than relying on static rules and manual workflows.
The old way
Traditional master data management (MDM) systems depend heavily on predefined matching rules, batch processing, and manual stewardship.
The shift
Agentic master data management replaces this reactive model with autonomous agents that operate continuously, identifying inconsistencies, resolving entities, enforcing governance policies, and improving data quality in real time.
The significance
This represents a structural shift in how enterprises manage core business entities such as customers, products, suppliers, and locations.
What is Traditional Master Data Management?
Traditional master data management (MDM) platforms centralise key business entities into a master data hub.While effective at initial consolidation, these systems struggle with scale, system proliferation, and continuous change.
THEY TYPICALLY RELY ON:
Why Traditional MDM Breaks at Enterprise Scale
At enterprise scale, traditional MDM models create operational friction:
How Agentic Master Data Management Works
Agentic MDM introduces AI agents that:
- Continuously monitor entity changes
- Automatically resolve duplicates
- Enforce governance policies
- Detect anomalies and inconsistencies
- Adapt resolution logic over time
Instead of relying solely on relational hubs, Agentic MDM operates on a persistent knowledge graph that:
- Preserves entity relationships
- Maintains context across systems
- Enables dynamic resolution
- Supports real-time updates
Agentic MDM functions as a closed loop:
- Detect inconsistencies
- Resolve entities
- Apply governance controls
- Learn and refine
This removes reliance on static rule sets.
Traditional Master Data Management vs Agentic Master Data Management
| CAPABILITY | TRADITIONAL MASTER DATA MANAGEMENT | AGENTIC MASTER DATA MANAGEMENT |
|---|---|---|
| Entity Resolution | Rule-based | AI-driven continuous |
| Governance | Manual workflows | Automated enforcement |
| Architecture | Relational hub | Graph-native |
| Change Handling | Batch updates | Real-time adaptation |
| Scalability | Operational overhead | Autonomous scaling |
Business Impact of Agentic Master Data Management
Agentic Master Data Management enables:
CluedIn as an Agentic Master Data Management Platform
CluedIn is a graph-native, agentic Master Data Management platform built to continuously manage and govern enterprise master data using AI agents operating on a persistent knowledge graph.
It delivers core master data management capabilities, entity resolution, mastering, governance, and data quality, within an autonomous framework designed for modern enterprise data ecosystems built on trusted enterprise guard-rails.
Find out more about Agentic Master Data Management
How to Modernise Master Data Management in the Enterprise
Modernising Master Data Management requires more than upgrading tooling.
Preparing Enterprise Data for AI
AI initiatives depend on consistent, governed, high-quality master data. Without trusted core entities, AI models amplify data inconsistencies rather than solve them.
CluedIn vs Traditional Master Data Management Platforms
Traditional master data management platforms were designed around centralised data hubs and manual stewardship.
Master Data Management for Microsoft Fabric and Purview
Microsoft Fabric and Microsoft Purview provide powerful capabilities for analytics, data engineering, and governance. However, neither platform delivers full MDM functionality.
Enterprise data challenges solved.
The resource drain
Challenge: Data teams spend most of their time cleaning and maintaining data.
CluedIn: Agents automate the grunt work - detect, fix, enrich - so teams focus on strategy.
Free your experts to deliver insight, not maintenance.
Scale without scale
Challenge: Manual data management can’t keep up with business or AI velocity.
CluedIn: CluedIn Agents handle millions of records in parallel - continuously improving quality and context.
Scale 100x faster without scaling headcount.
Fragmented systems, fragmented truth
Challenge: Data lives across clouds and apps, breaking consistency and governance.
CluedIn: Agents unify and govern data across all platforms - enforcing global rules locally.
A single, trusted layer across your data landscape.
Rising cost, falling ROI
Challenge: Traditional MDM is expensive and slow to prove value.
CluedIn: Autonomous Agents deploy in minutes and cost cents per run.
$0.13 vs $1,000 per job - measurable impact from day one.
Governance at scale
Challenge: Automation often introduces compliance risk.
CluedIn: CluedIn Agents are governed by design - every action is logged and explainable.
Autonomous, auditable, and compliant by default.
Data quality blind spots
Challenge: Even ‘good’ data hides silent errors that undermine AI.
CluedIn: Agents continuously validate, enrich, and learn from feedback.
Data that gets smarter every day - and AI you can trust.
The AI readiness gap
Challenge: AI fails without complete, current, trusted data.
CluedIn: Agents continuously prepare and enrich data to feed copilots and models.
AI that performs as promised - powered by data you can depend on.