Editing Data management (section)

== Topics in Data Management==
The Data Management Body of Knowledge, DMBoK, developed by the [[Data Management Association]], DAMA, outlines key knowledge areas that serve as the foundation for modern data management practices. suggesting a framework for organizations to manage data as a strategic [[Data asset|asset]].
===Data Governance===
{{main|Data governance|Data steward|Big data ethics|Data custodian|}}
Setting policies, procedures, and accountability frameworks to ensure that data is accurate, secure, and used responsibly throughout the organization.

===Data Architecture===
{{main|Data Architecture|Data mesh}}
Focuses on designing the overall structure of data systems. It ensures that data flows are efficient and that systems are scalable, adaptable, and aligned with business needs.

===Data Modeling and Design=== 
{{main|Data modeling|Database design}}
This area centers on creating models that logically represent data relationships. It’s essential for both designing databases and ensuring that data is structured in a way that facilitates analysis and reporting.
===Data Storage and Operations===
{{main|Database administration|Database management system}}
Deals with the physical storage of data and its day-to-day management. This includes everything from traditional data centers to cloud-based storage solutions and ensuring efficient data processing.

===Data Integration and Interoperability===
{{main|Data integration|Extract, transform, load|Extract, load, transform|Dataflows}}
Ensures that data from various sources can be seamlessly shared and combined across multiple systems, which is critical for comprehensive analytics and decision-making.

===Document and Content Management===
{{main|Document management system|Content management|Records management}}
Focuses on managing unstructured data such as documents, multimedia, and other content, ensuring that it is stored, categorized, and easily retrievable.
===Data Warehousing, Business Intelligence and Data Analytics===
{{main|Data warehouse|Business intelligence|data mart|Data analytics|Data analysis|data mining|Data science}}
Involves consolidating data into repositories that support analytics, reporting, and business insights.

===Metadata Management===
{{main|Metadata management|Metadata|Metadata registry|Metadata discovery|Metadata publishing}}
Manages data about data, including definitions, origin, and usage, to enhance the understanding and usability of the organization’s data assets.
===Data Quality Management===
{{main|Data quality|Data quality assurance|Data integrity|Data cleansing|Data discovery}}
Dedicated to ensuring that data remains accurate, complete, and reliable, this area emphasizes continuous monitoring and improvement practices.
===Reference and master data management===
{{main|Reference data|Master data management}}
Reference data comprises standardized codes and values for consistent interpretation across systems. Master data management (MDM) governs and centralizes an organization’s critical data, ensuring a unified, reliable information source that supports effective decision-making and operational efficiency.
===Data security===
{{main|Data security}}
Data security refers to a comprehensive set of practices and technologies designed to protect digital information and systems from unauthorized access, use, disclosure, modification, or destruction. It encompasses encryption, access controls, monitoring, and risk assessments to maintain data integrity, confidentiality, and availability.

{{see also|Data access|Data erasure|Data theft}}

===Data privacy===
{{main|Data privacy}}
Data privacy involves safeguarding individuals’ personal information by ensuring its collection, storage, and use comply with consent, legal standards, and confidentiality principles. It emphasizes protecting sensitive data from misuse or unauthorized access while respecting users' rights.
{{see also|Data subject|Pseudonymization}}