Overview of Data Modeling, Data Architecture, Master Data Management, Metadata Management, and Data Quality Management

Data modeling and data architecture describe the metadata needed to understand enterprise data, processes, and rules. Three main areas are covered: data modeling (logical models defining business terms and data elements), process modeling (defining business processes that create or transform data), and rule modeling (governing data quality and compliance).

Various forms of data models exist, including relational models for OLTP systems (typically in third normal form) and dimensional models for OLAP systems, which can follow Kimball or Inmon methodologies.

Master Data Management (MDM) encompasses processes, governance, policies, standards, and tools to define and manage an organization’s critical data, providing a single reference point. It includes reference data (transaction business objects and analytical dimensions) and analytical data (supporting decisions). MDM shares a common goal with Enterprise Information Architecture (EIA) to maintain a consistent definition of master data.

Metadata provides a reference framework for data. Technical metadata describes technical details (source table names, column names, data types), business metadata supplies business context (names, definitions, owners), and operational metadata records usage information (last update date, access frequency). Metadata management is an end‑to‑end process for creating, enhancing, and maintaining metadata repositories, facing challenges similar to BI and MDM.

Data quality management defines the excellence of data in representing real‑world scenarios and its suitability for specific purposes, covering completeness, validity, consistency, timeliness, and accuracy. It involves establishing roles, responsibilities, policies, and procedures for data acquisition, maintenance, dissemination, and disposition, requiring close partnership between business and IT groups.