DataTerminology.comNEW
Also known as: Information Management
The disciplined practice of planning, governing, and enabling data collection, organization, protection, and use.
DESCRIPTION
Data management encompasses policies, processes, and technologies that ensure data is reliable, secure, and usable. It includes governance, stewardship, quality controls, metadata, architecture, and privacy. Effective data management aligns with strategic objectives and regulatory requirements. It enables analytics, AI, and operational efficiency through trusted, well-documented data assets.
ALTERNATIVE DEFINITIONS
IBM
Data management is the practice of collecting, organizing, protecting, and storing an organization’s data so it can be analyzed for business decisions.
Focuses on enabling analytics and decision-making.
Oracle
Data management is the process of collecting, storing, and using data securely, efficiently, and cost-effectively.
Highlights security, efficiency, and cost considerations.
DAMA International
Data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets.
Formalized governance-oriented definition from the DMBOK.
RELATED TERMS
Governance Metadata Management Data Quality Master Data Management Data Architecture Security Data Privacy Data Stewardship Data Strategy Data Discovery Platform
COMPETING TERMS
RELATIONS
- Narrower: Master Data Management Product Data Management Reference Data Management
- Referenced by: DAMA-DMBOK
KNOWLEDGE AREAS
Data Management Data Governance Data Architecture Metadata Management Data Quality Data Security Data Privacy & Compliance
TARGET AUDIENCE
Chief Data Officer Data Governance Manager Data Steward Data Management Consultant Data Architect Enterprise Architect Data Strategy Consultant
EXTERNAL SOURCES
- Wikipedia - Data management
- DAMA International - DMBOK overview of data management
- Microsoft - Azure data management scenario guidance
- Google Cloud - Google Cloud architecture data management
TOP QUESTIONS
Why is Data Management foundational for analytics and AI?
It ensures data is trustworthy, discoverable, and secure. Without sound management, analytics outputs degrade, models drift, and compliance risks escalate.
Category: Relevancy
What are the core components of Data Management?
Governance, quality, metadata, architecture, privacy, security, and stewardship. Together they enable controlled, purposeful data use.
Category: Overview
How does Data Management differ from Data Governance?
Governance sets decision rights and policies; management operationalizes them via processes, tools, and people across the data lifecycle.
Category: Comparison
What is a common mistake in Data Management programs?
Focusing only on tools and not on roles, policies, and processes. Sustainable management requires organizational change and accountability.
Category: Mistakes
How is Data Management measured?
Use maturity assessments, data quality metrics, catalog adoption, SLA compliance, time-to-data metrics, and reduction in incidents.
Category: Measurement
What are the risks of weak Data Management?
Inaccurate reporting, model failures, security breaches, and regulatory violations. Costs rise due to rework and duplication.
Category: Risk
Who owns Data Management in an enterprise?
A Chief Data Officer typically leads, supported by governance councils, stewards, architects, and platform teams.
Category: Roles
How do cloud platforms influence Data Management?
They provide managed services for storage, security, catalogs, and lifecycle, but policies and stewardship remain essential.
Category: Cloud
Where to start improving Data Management?
Define governance roles, catalog critical data, establish quality SLAs, and implement privacy and security controls iteratively.
Category: Getting started