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How to Modernise Master Data Management in the Enterprise
Modernising Master Data Management requires more than upgrading tooling. It requires rethinking how master data is continuously managed across distributed enterprise systems. This shift represents the move toward Agentic Master Data Management.
Signs Your MDM Approach Is Failing
Common symptoms include:
- Duplicate customer records across ERP and CRM systems
- Inconsistent product definitions across business units
- Manual stewardship queues growing faster than resolution
- Delays in analytics due to data reconciliation
- Governance policies enforced inconsistently
These issues indicate structural limitations in traditional master data management (MDM) architecture. See how Agentic master data management differs from traditional master data management platforms.
Step 1:
Establish a Persistent Master Data Foundation
Modern MDM requires a persistent entity layer that:
Step 2:
Automate Entity Resolution Across ERP and CRM
Modern enterprises require continuous entity resolution across:
Step 3:
Create a Single Customer View Across Systems
A modern master data management architecture unifies:
Step 4:
Move from Manual Rules to AI-Driven Governance
Governance must shift from manual workflow enforcement to automated policy validation and continuous monitoring. This is the transition toward Agentic Master Data Management.
How CluedIn Modernises Enterprise MDM
CluedIn replaces rule-heavy MDM hubs with a graph-native, AI-driven architecture that continuously resolves, governs, and improves enterprise master data.
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.