The heart of DAMA-DMBOK®

Data Governance:
Establishes policies, roles, and standards for managing data as a valuable business asset. It ensures compliance, accountability, and consistency across the organization.

Data Architecture:
Focuses on designing and maintaining the data structures and frameworks that support business strategy. This includes defining data models, selecting technologies, and ensuring data flows align with enterprise goals.

Data Modeling & Design:
Develops structured representations of data to support business operations and analytics. It involves creating conceptual, logical, and physical data models that enable better data integration and usability.

Data Storage & Operations:
Ensures data is efficiently stored, retrieved, and maintained. This includes managing databases, implementing backup and recovery solutions, and optimizing data performance.

Data Security:
Protects data from unauthorized access, breaches, and loss. It encompasses access controls, encryption, regulatory compliance, and security policies to safeguard sensitive information.

Data Integration & Interoperability:
Enables the seamless flow of data across systems and platforms. This includes Extract, Transform, Load (ETL) processes, API-based integrations, and ensuring consistent data formats for operational efficiency.

Documents & Content Management:
Manages unstructured data such as documents, images, videos, and other content. This area ensures that digital assets are properly stored, categorized, and accessed according to business needs.

Reference & Master Data Management:
Ensures consistency in core data entities like customers, products, and locations across business units. Master Data Management (MDM) and Reference Data Management provide a “single source of truth” for key business information.

Data Warehousing & Business Intelligence:
Supports analytics, reporting, and decision-making by organizing data for easy access. It involves designing data warehouses, data lakes, and business intelligence (BI) tools to enable strategic insights.

Metadata Management:
Maintains “data about data” to improve understanding, governance, and accessibility. Metadata catalogs help organizations track data lineage, definitions, and usage across different systems.

Data Quality Management:
Ensures that data is accurate, complete, consistent, timely, valid, and unique. This includes defining data quality metrics, detecting errors, and implementing cleansing and enrichment strategies.