Data Lifecycle Management Practice

A Data Lifecycle Management Practice is a systematic data governance practice that can support data lifecycle management tasks.

• AKA: Information Lifecycle Management Practice, Data Lifecycle Governance Practice, End-to-End Data Management Practice.
• Context:
  • It can typically govern Data Creations through data lifecycle management practice collection controls.
  • It can typically manage Data Processings through data lifecycle management practice transformation rules.
  • It can typically control Data Storages through data lifecycle management practice retention policys.
  • It can typically orchestrate Data Archives through data lifecycle management practice preservation strategys.
  • It can typically execute Data Disposals through data lifecycle management practice deletion procedures.
  • ...
  • It can often optimize Data Values for data lifecycle management practice business benefit.
  • It can often minimize Data Risks for data lifecycle management practice risk reduction.
  • It can often ensure Data Compliances for data lifecycle management practice regulatory adherence.
  • It can often track Data Lineages for data lifecycle management practice provenance documentation.
  • ...
  • It can range from being a Manual Data Lifecycle Management Practice to being an Automated Data Lifecycle Management Practice, depending on its data lifecycle management practice automation level.
  • It can range from being a Basic Data Lifecycle Management Practice to being a Comprehensive Data Lifecycle Management Practice, depending on its data lifecycle management practice coverage scope.
  • It can range from being a Reactive Data Lifecycle Management Practice to being a Proactive Data Lifecycle Management Practice, depending on its data lifecycle management practice planning approach.
  • It can range from being a Centralized Data Lifecycle Management Practice to being a Distributed Data Lifecycle Management Practice, depending on its data lifecycle management practice organizational model.
  • It can range from being a Rigid Data Lifecycle Management Practice to being a Flexible Data Lifecycle Management Practice, depending on its data lifecycle management practice adaptability.
  • ...
  • It can integrate with Data Catalog Systems for data lifecycle management practice metadata management.
  • It can connect to Data Quality Platforms for data lifecycle management practice quality assurance.
  • It can interface with Storage Management Systems for data lifecycle management practice capacity planning.
  • It can communicate with Compliance Management Platforms for data lifecycle management practice regulatory tracking.
  • It can synchronize with Master Data Management Systems for data lifecycle management practice reference data control.
  • ...
• Example(s):
  • Stage-Based Data Lifecycle Management Practices, such as:
    • Data Collection Management Practice, governing data acquisition and ingestion.
    • Data Processing Management Practice, controlling data transformation and enrichment.
    • Data Retention and Deletion Schedule, managing retention periods and disposal.
  • Domain-Specific Data Lifecycle Management Practices, such as:
    • Customer Data Lifecycle Practice, managing customer information lifecycle.
    • Research Data Management Practice, governing scientific data lifecycle.
    • IoT Data Lifecycle Practice, managing sensor and device data streams.
  • Compliance-Driven Data Lifecycle Management Practices, such as:
    • GDPR Data Lifecycle Practice, ensuring privacy regulation compliance.
    • Healthcare Data Lifecycle Practice, managing HIPAA-compliant data.
    • Financial Data Lifecycle Practice, governing financial record retention.
  • ...
• Counter-Example(s):
  • Data Quality Management Practice, which improves data accuracy but not lifecycle.
  • Data Integration Practice, which combines data but doesn't manage lifecycle.
  • Data Analytics Practice, which analyzes data but doesn't govern its lifecycle.
• See: Data Governance, Information Management, Data Management Strategy, Records Management, Data Retention Policy, Data Classification, Data Privacy Technique, Storage Tiering, Data Archival, Secure Deletion, Regulatory Compliance.

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