MDM maturity assessment
Where does your organisation stand? Which domains take priority?
Service · Master Data Management
Inconsistent master data is the silent killer of AI initiatives, reporting and compliance. cimt builds MDM solutions that harmonise your core data across ERP, CRM, PIM and legacy systems — with an approach tuned to your maturity.
Master Data Management (MDM) is the discipline that ensures your critical master data — customers, products, suppliers, locations — is consistent and reliable across every system. cimt delivers MDM in five building blocks: a maturity assessment that shows where you stand, an architecture design (registry / consolidation / coexistence) tuned to your landscape, canonical data models with erwin Data Modeler, an integration strategy via Qlik Talend Cloud (real-time or batch), and a stewardship model that assigns ownership and quality monitoring. Result: one golden record per domain, trustworthy reporting and a data foundation that AI applications can rely on.
Why MDM
Customer records that vary per system, product codes that don't match, supplier data that's wrong everywhere. The result: bad reporting, lost revenue and compliance risk. Organisations trying to scale AI hit this wall first.
| MDM domain | Typical sources | Impact without MDM |
|---|---|---|
| Customer data | CRM, ERP, e-commerce, customer service | Duplicate customer records, wrong invoicing |
| Product data | PIM, ERP, webshop, suppliers | Inconsistent catalogues, returns loss |
| Supplier data | ERP, procurement, contract management | Compliance gaps, duplicate payments |
| Location & reference data | ERP, GIS, HR, logistics | Reporting errors, wrong allocations |
Strategy & architecture
A successful MDM implementation starts not with tooling but with strategy. We use the DAMA DMBoK framework to position MDM within the broader data management landscape.
Where does your organisation stand? Which domains take priority?
Registry, consolidation or coexistence model, tuned to your system landscape.
Models for customer, product and supplier with erwin Data Modeler.
Real-time synchronisation or batch, via Qlik Talend Cloud or API integration.
Roles, responsibilities and decision-making processes for master data.
Stewardship
Technology alone doesn't solve MDM. The key is clear ownership per data domain, defined responsibilities and workable processes for quality monitoring.
| Role | Responsibility |
|---|---|
| Data Owner | Strategic decisions on the data domain, budget responsibility |
| Data Steward | Day-to-day quality monitoring, rule enforcement, issue resolution |
| Data Custodian | Technical execution, integration, management of MDM tooling |
| Data Consumer | Uses master data in daily processes, provides quality feedback |
First step
In a no-obligation conversation we identify which MDM domain has the highest business impact and what a first pilot could look like.
Frequently asked
A data warehouse aggregates historical data for analysis and reporting. MDM manages the current, 'true' version of your core data (golden record) for operational use across all systems. They often work together: MDM provides clean core data, the warehouse uses it as reference. Many organisations build a warehouse first and hit the wall because the source master data isn't right — MDM solves that structurally.
Depends on your integration appetite and source systems. Registry: light, references only — good for analytics. Consolidation: copies data into one golden record for analysis — good when source systems cannot be replaced. Coexistence: synchronises changes back to source systems — most impact, requires strict governance. We pick the right model in the architecture phase, not as a pure tech choice but based on your operational reality.
No. We use erwin Data Modeler for canonical data models because it has proven integration with the most-used MDM platforms and supports the DAMA approach natively. If you already use another modelling tool or an MDM suite (Informatica MDM, Stibo, Reltio), we integrate our approach around that.
A working MDM for a single domain (e.g. customer data) typically stands in 4–6 months: maturity assessment → architecture → canonical model → integration of top-3 sources → stewardship process. Subsequent domains follow faster because the architecture is in place. A full enterprise MDM (all 4 domains, all systems) is a multi-year programme.