What Is Master Data Management?

Effective data management is vital to any modern company in the digital age. Data shapes significant decisions and is key to creating a roadmap to future success, so master data management (MDM) is useful for keeping data accurate and accessible.

Creating a master data record and using automated data quality software gives companies an excellent overview and management system for all data sources and business applications.

Understanding Master Data Management

Master data is an organization’s core data. Employees compile data from various sources to create a master data record. It is the base from which other data types, such as transactional and reference data, derive.

Examples of master data include:

• Product information
• Customer information
• Employee details

Importance of MDM

MDM offers several benefits, including:

• Data quality: Ensures data is accurate and recent.
• Compliance: Utilizes a data governance framework, including policies and features for tracking changes and auditing.
• Efficiency: Reduces redundancies, improves consistency and makes data more accessible.
• Organization: Resolves fragmentation issues with a single master record.

The above benefits create an environment where decision-makers can easily view critical data, helping to strategize and make informed business decisions.

Master Data Management Concepts and Approaches

MDM operates on the core principles of data centralization, effective data governance policies, and enforcement of these policies through data stewardship. To implement these principles, a data steward can take various approaches, such as:

• Centralized
• Decentralized
• Hybrid
• Registry
• Data-centric
• Transactional
• Process-centric
• Cloud-based

Data Quality in MDM

Companies must use data validation and quality control to ensure high-quality data.

Data Validation Processes and Quality Control

The master data management tools that data stewards can use for validation and quality control include:

• Data deduplication
• Data enrichment
• Data auditing
• Data matching
• Data profiling
• Data cleansing

These processes ensure the information is in a navigable framework, full of detail without duplications, complies with data governance rules, and is relevant. These are time-consuming processes, so using cutting-edge automated data quality software such as Anomalo will save you time and money with continuous AI data monitoring.

MDM Use Cases

Companies apply MDM across many departments, including supply chain, compliance and risk management, and customer relationship management (CRM). A master data management example in CRM would include ensuring correct billing processes and deduplication of marketing efforts.