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Also known as: Data Lifespan, DLM
The stages data moves through from creation and use to archival or deletion.
DESCRIPTION
The data lifecycle describes how data is created, ingested, stored, processed, shared, maintained, archived, and eventually deleted. It enables organizations to plan controls for quality, security, privacy, retention, and compliance across each stage. Lifecycle practices reduce risk, control costs, and improve discoverability and reuse. Effective lifecycle management uses policies, automation, and governance to ensure data remains fit-for-purpose through its entire lifespan.
ALTERNATIVE DEFINITIONS
NetApp
Data lifecycle management (DLM) is the process of managing the flow of data through its life cycle—from creation and initial storage to the time when it becomes obsolete and is deleted.
Emphasizes end-to-end flow and final disposal.
Varonis
Data lifecycle management (DLM) is the practice of managing the flow of an organization’s data throughout its life cycle—from creation to disposal.
Frames DLM as an organizational practice covering all stages.
Microsoft Learn
Microsoft Purview Data Lifecycle Management helps organizations govern their data from creation to disposal by defining retention and deletion policies.
Highlights governance via automated retention and deletion policies.
RELATED TERMS
Data Retention Records Management Data Archival Lineage Data Discovery Platform Data Classification Data Deletion Data Minimization Data Policy
COMPETING TERMS
Information Lifecycle Management
KNOWLEDGE AREAS
Data Management Data Governance Data Privacy & Compliance Data Quality Data Architecture Data Storage & Operations
TARGET AUDIENCE
Data Steward Data Governance Manager Data Architect Chief Data Officer Data Management Consultant Data Strategy Consultant
EXTERNAL SOURCES
- Wikipedia - Data lifecycle
- Microsoft Learn - Purview lifecycle governance guide
- AWS - S3 lifecycle policies documentation
- ISO - Records management standard context
TOP QUESTIONS
What stages make up the Data Lifecycle?
Typical stages include creation, ingestion, storage, processing, sharing, maintenance, archival, and deletion. Each stage requires tailored controls for quality, security, privacy, and compliance.
Category: Overview
Why is Data Lifecycle management important?
It ensures data remains trustworthy and compliant while controlling costs and risks. Managing retention and disposal reduces exposure and improves operational efficiency.
Category: Relevancy
How do policies influence the Data Lifecycle?
Retention, classification, and deletion policies guide actions at each stage, often enforced via automation in data platforms and governance tools.
Category: Management
What’s a common mistake in Data Lifecycle practices?
Ignoring deletion and archival leads to data hoarding, higher costs, and privacy risks. Lifecycle controls must cover the full end-to-end journey.
Category: Mistakes
How does the Data Lifecycle intersect with GDPR?
GDPR principles like storage limitation and purpose limitation require lifecycle controls, especially around retention, deletion, and data minimization.
Category: Compliance
What tools support Data Lifecycle governance?
Data catalogs, classification tools, policy engines, and cloud lifecycle policies help automate retention and manage discovery and access.
Category: Tooling
What are risks when ignoring the Data Lifecycle?
Data sprawl, increased breach impact, compliance violations, and higher storage costs. Old data may be inaccurate and harm decision-making.
Category: Risk
How do backups fit into the Data Lifecycle?
Backups are part of storage and maintenance stages, with distinct retention rules and secure deletion requirements for expired backups.
Category: Operations
What is the difference between Data Lifecycle and ILM?
ILM traditionally focuses on information records and storage tiers; Data Lifecycle broadens to governance, quality, and compliance across all stages.
Category: Comparison