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Understand the basics and benefits of master data management, why it’s an important practice to help maintain the quality and consistency of data throughout an organization, technologies used to implement master data management, and what to look for in an effective MDM solution.
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.
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.
MDM can be understood both as a technology and as a set of practices.
MDM technology refers to the tools necessary to implement and manage MDM for users within an organization. They automatically govern how data is managed and shared enterprise wide. These tools include:
Effective MDM technology is critical to ensuring that data is clean, accurate, and readily available to systems and users across the organization.
MDM as a discipline defines the principles, policies, practices, and overall approach for effective master data management. It includes defining data standards, processes, and roles for handling master data within the organization, such as:
An effective MDM solution can drive significant growth within an organization.
MDM streamlines business processes by providing a centralized and standardized data repository. This reduces manual data entry, duplicate records, and time spent on data reconciliation.
With a single source of data truth for critical business entities like customers, products, and suppliers, organizations can unlock more informed and data-driven decisions faster.
The comprehensive view of customer data provided by MDM can help organizations identify cross-selling and upselling opportunities, increasing revenue and customer lifetime value. Plus, it lowers data maintenance costs by reducing errors and redundancies.
MDM enables a complete view of customer data, which can facilitate better customer relationship management. This results in personalized, connected offerings, more responsive customer service, and enhanced customer journeys.
MDM ensures the accuracy and reliability of supplier data, can improve inventory management, and can drive better demand forecasting.
MDM assists in meeting regulatory requirements around data accuracy, privacy, and security. It also provides transparency and traceability of data changes to aid in compliance efforts.
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, 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 businesspeople must drive the agreement. 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, a deep history of workflow-driven actions, and management agreement to enforce accountability for actions taken—and not taken.