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What is Master Data Management (MDM)?

Master Data Management (MDM) provides a unified view of data across multiple systems to meet the analytic needs of a global business. MDM creates singular views of master and reference data, whether it describes customers, products, suppliers, locations, or any other important attribute.
 
Most companies rely on “master data” that is shared across operational and analytic systems. This data includes information about customers, suppliers, accounts, or organizational units and is used to classify and define transactional data.
 
The challenge is keeping master data consistent, complete, and controlled across the enterprise. Misaligned and inaccurate master data can cause costly data inaccuracies and misleading analytics, which can negatively impact everything from new product introductions to regulatory compliance.
 
The answer to these and other related issues is master data management (MDM), a set of processes that creates and maintains an accurate, consistent view of reference data that the entire organization can access for decision making. By standardizing business entity definitions, improving data quality, and aggregating and distributing data across the organization, MDM simplifies and improves business processes, enhances organizational speed and agility, and leads to a consistent, holistic view of the entire enterprise.
 
A good MDM solution mitigates the risk of poor data quality across the enterprise by managing data architecture, metadata, data quality, data hierarchies, master data workflow, and data governance. It also synchronizes master data so that changes are propagated across the entire enterprise.
 
Manage Meaning in Master Data
A business can create enterprise definitions of master data, but it also can create and use business unit or trading partner definitions of master data as well.
 
Core Values Related to Master Data Management
Reference Data Management: Reference Data Manager (RDM) gives you a self-service solution to increase analytic accuracy and improve your data governance regime.
 
Hierarchy Management: The multi-dimensional Hierarchy Manager lets you visually explore, maintain, version, compare, and conduct hierarchy mass maintenance.
 
Customer Data Integration: Customer Data Integration (CDI) capabilities help clean, arrange, load, track, and synchronize customer data to form a 360-degree customer view.
 
What to Look for in an MDM Solution
Multiple Domain Management: A single solution that supports multiple domains, eliminating the need to buy multiple solutions, product, and reference data.
 
Comprehensive Data Consolidation: Consolidate and master data from numerous heterogeneous systems and channels.
 
Business User Control: Direct user interface data entry or Microsoft Excel upload into the database with governance but without requiring IT involvement.
 
Enterprise Agility: Powerful automation to create workflows that can be managed by data stewards.
 
Enterprise Analytical Accuracy: Provides a central framework with complete support of a workflow and process-driven data governance environment.
 
MDM practices are diverse, but all solutions share a common goal: consensus-driven definitions of common business entities—like customers, products, and financials—applied consis­tently across an agreed-upon list of IT applications and business units. In turn, consistent data usage (as enabled by MDM) leads to greater accuracy, insight, and compliance in data-based operations and decision-making.
 
To share data consistently an organization needs to define how applications and databases should represent shared business entities. To reach the goal of sharing data, stakeholders must agree first on definitions, then establish the needed teams, policies and procedures. Ongoing owners­hip of each area of master data must be established. Doing so will speed up resolution of future issues, identify data stewards and establish ap­proval workflows implemented as part of the overall solution.
 
For master data to achieve its primary goal—consensus-driven definitions applied consistently—a cross-functional team of technical and business people must drive the agreement. In order for IT to hand over responsibility for master data additions, updates and deletions to business data stewards, seve­ral steps should be taken. The organization must secure facilities such as role-based security, deep history of workflow-driven actions, and management agreement to enforce accountability for actions taken—and not taken.