The problem master data management solves
In any company with a few years of technology history, master data — customers, products, suppliers, locations, employees — ends up scattered across multiple systems with conflicting definitions. The ERP has a product catalog with one coding scheme, e-commerce uses another, and the warehouse system uses a third. The CRM records customers by different criteria than the billing system.
The outcome is predictable: reports that don’t reconcile, lost orders, profitability analyses nobody trusts, and data teams that spend more time reconciling information than creating value.
Master data management (MDM) establishes a single source of truth for each business-critical entity and governs how that data is created, updated, and synchronized across every system.
The real cost of poor master data management
According to Gartner, poor data quality costs organizations an average of at least $12.9 million a year (Gartner); much of that goes into correcting, reconciling, and cleaning master data.
MDM isn’t an added cost — it removes a hidden cost you’re already paying.
The most critical master data entities
The entities with the biggest impact when they’re poorly managed are:
- Customers (customer MDM): duplicates, outdated contact details, incorrect segmentation.
- Products (product MDM): inconsistent descriptions, different prices by channel, conflicting classifications.
- Suppliers (supplier MDM): different codes in each system, outdated tax details, risk of duplicate payments.
- Locations: cost centers, stores, plants, and warehouses coded differently by each department.
MDM implementation styles
There’s no single MDM architecture. The main options are:
Consolidation style: operational systems remain where data is created. MDM aggregates, cleans, and consolidates a master view without changing the source systems. Ideal as a first step in environments with many legacy systems.
Registry style: MDM acts as a central repository that systems query, but it doesn’t create data. Source systems remain autonomous.
Coexistence style: MDM and operational systems share responsibility for creating and updating master data, with clear ownership rules.
Centralized style: MDM is the only system where master data is created and modified, and it’s then distributed to all other systems. Maximum control, greater implementation complexity.
Tools and technologies
The market offers specialized MDM platforms (Informatica MDM, SAP MDG, IBM InfoSphere) as well as solutions built on cloud data platforms with identity resolution and data quality components. The choice depends on your existing ecosystem, how many systems need integrating, and your budget.
For many midsize companies, a solution built on modern cloud tools with well-designed data pipelines can deliver most of the value of a specialized MDM platform at a fraction of the cost.
A roadmap for businesses
A successful MDM implementation generally follows these phases: inventory and profiling of current master data, definition of business rules and the canonical model, tool and architecture selection, rollout by priority entity, and a continuous governance process.
At AIRES, we combine the DAMA methodology with hands-on experience in manufacturing and distribution to design MDM implementations proportionate to each company’s size and complexity.
Frequently asked questions
Where should we start an MDM project if we have many systems? The most effective starting point is to pick a single master entity — usually customers or products — and a specific business use case that justifies the investment: for example, eliminating duplicate customer records that cause billing errors. Trying to tackle every entity at once is the most common reason MDM projects fail.
Are MDM and data quality the same thing? They’re complementary but different. Data quality assesses and improves the characteristics of data (completeness, accuracy, consistency). MDM establishes the governance and architecture for a single source of truth. A good MDM program includes data quality processes, but goes further.
How long until we see a return on investment? Well-focused projects deliver returns progressively from the early phases. The fastest benefits usually come from eliminating manual reconciliation and improving the success rate of sales campaigns that used to rely on duplicate or incorrect customer data.