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Also known as: DLM
Policy-driven practice managing data from creation and use through retention, archival, and secure disposal.
DESCRIPTION
Data Lifecycle Management (DLM) orchestrates how data is created, classified, stored, used, shared, retained, archived, and securely disposed. It aligns legal, regulatory, and business requirements with technical controls and operating processes. DLM reduces cost and risk by moving data to appropriate tiers across its life and enforcing retention, security, and privacy policies. It connects governance artifacts (policies, standards) with operational tools (catalogs, DLP, ILM tiers). Modern DLM often leverages automation, metadata, and classification to trigger actions. In regulated contexts, DLM supports compliance evidence through audit trails and records. Effective DLM requires cross-functional collaboration between governance, architecture, security, and engineering.
ALTERNATIVE DEFINITIONS
Microsoft Learn
Data lifecycle management (formerly Microsoft Information Governance) provides tools to govern the lifecycle of your data by importing, storing, classifying, retaining, reviewing, and disposing of data.
Highlights operational tooling and automated controls across the lifecycle within Microsoft 365.
The National Archives (UK)
The information lifecycle is the way records are managed from creation to disposal.
Frames lifecycle as a records management discipline from creation to final disposition.
RELATED TERMS
Records Management Data Retention Data Archival Data Purging Data Classification Governance Data Discovery Platform Backup Data Privacy
COMPETING TERMS
Information Lifecycle Management
KNOWLEDGE AREAS
Data Management Data Governance Data Architecture Data Storage & Operations Data Privacy & Compliance
TARGET AUDIENCE
Data Governance Manager Data Steward Data Architect Chief Data Officer Chief Information Officer Data Management Consultant Data Engineer
EXTERNAL SOURCES
- Wikipedia - Data lifecycle management
- Microsoft - Microsoft 365 DLM product documentation
- The National Archives (UK) - Government guidance on information lifecycle
TOP QUESTIONS
Why is Data Lifecycle Management important?
It reduces risk, controls cost, and ensures compliance by moving and governing data according to its value and obligations throughout creation, use, retention, and disposal. Clear policy-to-control alignment is the outcome.
Category: Relevancy
What are the key stages in Data Lifecycle Management?
Typical stages include creation/ingestion, classification, storage, usage/sharing, retention, archival, and secure disposal. Each stage has associated controls, roles, and evidence requirements.
Category: Overview
Who owns Data Lifecycle Management in an organization?
Ownership is shared: governance defines policy, architecture designs patterns, security sets controls, and engineering implements automation. The Data Governance Manager often coordinates accountability and metrics.
Category: Management
How does Data Lifecycle Management interact with cloud storage tiers?
DLM policies drive movement between hot, cool, and archive tiers based on access patterns, cost, and compliance. Automation enforces retention and deletion when criteria are met.
Category: Application
What is a common mistake in Data Lifecycle Management?
Ignoring metadata and classification leads to blunt, manual lifecycle actions. Without automated classification, retention and deletion are inconsistent and risky.
Category: Mistakes
How do regulations influence Data Lifecycle Management?
Regulations set retention, deletion, and evidence obligations. DLM operationalizes these via policy, controls, and audit trails to demonstrate compliance.
Category: Compliance
What metrics indicate effective Data Lifecycle Management?
Key metrics include percentage of classified data, policy coverage, timely deletions, storage cost per TB by tier, and audit findings closure rate.
Category: Measurement
How does Data Lifecycle Management differ from Information Lifecycle Management?
DLM emphasizes data across systems and analytics contexts; ILM historically centers on storage tiering for files and records. Modern practices often blend both.
Category: Comparison
Where should I start implementing Data Lifecycle Management?
Begin with a data classification policy, define retention schedules, map systems, and automate a few high-value policies. Expand coverage iteratively with metrics.
Category: Getting Started