IBM Infosphere MDM Reference Data Management(RDM)

1. Centralized Reference Data Management: Provides a centralized repository to store and manage reference data, ensuring consistency and accuracy across systems and applications.

2. Versioning and Auditing: Supports versioning of reference data to track changes over time and provides audit trails for compliance and governance purposes.

3. Data Governance and Stewardship: Enables data governance practices by defining data ownership, roles, and responsibilities for managing reference data. Supports data stewardship workflows for data quality management and issue resolution.

4. Data Quality Management: Includes tools for data profiling, cleansing, and standardization to ensure the quality and integrity of reference data.

Before learning IBM InfoSphere MDM Reference Data Management (RDM), it's beneficial to have a foundational understanding of several key areas:

1. Data Management Fundamentals: Familiarity with basic concepts of data management, including data modeling, data governance, data quality, and data integration.

2. Master Data Management (MDM): Understanding of MDM principles and practices, including the management of master data domains, such as customer, product, and supplier data.

3. Database Concepts: Knowledge of relational database concepts, SQL query language, and database management systems (DBMS) is helpful, as RDM often involves working with databases to store and manage reference data.

4. Data Governance and Data Stewardship: Awareness of data governance frameworks, policies, and best practices, as well as the role of data stewards in managing data quality, consistency, and compliance.

By learning IBM InfoSphere MDM Reference Data Management (RDM), you gain a set of skills that are valuable for managing and governing reference data within an organization. These skills include:

1. Reference Data Modeling: You will learn how to design and implement reference data models to standardize and govern the structure, relationships, and attributes of reference data entities.

2. Data Governance: You'll understand the principles and practices of data governance as applied to reference data, including data stewardship, data quality management, metadata management, and policy enforcement.

3. Data Integration: You'll gain skills in integrating reference data across heterogeneous systems and applications using industry-standard integration techniques and technologies, ensuring consistency and accuracy of reference data across the enterprise.

4. Data Quality Management: You'll learn techniques for assessing, monitoring, and improving the quality of reference data, including data profiling, cleansing, deduplication, and enrichment.