Clear, authoritative explanations of key data management concepts, aligned to DAMA DMBOK v2. Definitive guides for practitioners, leaders, and CDMP candidates.
Clear, jargon-free explanations of the concepts every data management professional needs to understand, aligned to DAMA DMBOK v2 definitions and industry best practice.
DAMA International (Data Management Association) is the global professional body for data management. It publishes the DMBOK, administers the CDMP certification, and sets the international standard for data management practice across industries and geographies.
Read more →The DAMA Data Management Body of Knowledge (DMBOK) is the globally recognized framework for enterprise data management, covering 11 knowledge areas from Data Governance through to Document and Content Management. The current version is DMBOK v2.
Read more →Data governance is the exercise of authority, control, and shared decision-making over the management of data assets. It defines who can take what actions with what data, in what situations, using what methods. It is a business capability, not an IT function.
Read more →Data quality refers to the fitness of data for its intended use in operations, analytics, and decision-making. The key dimensions include accuracy, completeness, consistency, timeliness, uniqueness, and validity. Poor data quality is estimated to cost organizations an average of $12.9 million per year.
Read more →Master Data Management (MDM) is the processes, governance, policies, standards, and tools that consistently define and manage the critical data entities of an organization, customers, products, locations, employees, creating a single, trusted source of reference.
Read more →CDMP (Certified Data Management Professional) is DAMA International's professional certification program for data management practitioners. It has four levels, Fundamentals, Associate, Practitioner, and Master, and is the globally recognized credential for enterprise data management expertise.
Read more →BCBS 239 (Basel Committee on Banking Supervision Principle 239) sets out principles for effective risk data aggregation and risk reporting for global systemically important banks. It requires banks to have strong data governance, accurate data, and the ability to aggregate risk data quickly and reliably.
Metadata management is the administration of data that describes other data, including business definitions, technical data types, data lineage, ownership, and quality metrics. Effective metadata management makes data discoverable, understandable, and trustworthy across the enterprise.
Data architecture defines the rules, policies, standards, and models that govern and define the type of data collected and how it is used, stored, managed, and integrated within an organization. It provides the blueprint that ensures data assets support business strategy and information requirements.
