Data Lifecycle Management
The process of managing business data from creation to deletion is known as Data Lifecycle Management.
Updated: December 2, 2023
The process of managing business data from creation to deletion is known as Data Lifecycle Management. Businesses must develop policies and procedures for generating, storing, and retiring their data when work becomes increasingly data-driven. DLM generally deal with general attributes of data files, such as type, size, or age.
DLM is an approach to managing proprietary data in which managing applications, systems, databases, and storage media are included. Product data management (PDM) software can be used for designing and manufacturing new products and big data integration platforms can be used for retaining massive data sets and managing the data pipeline. These software act as repositories for data and help manage the data lifecycle.
Data collection, Data storage, Data preparation, Data usage, Data maintenance and Data cleaning are multiple phases and types of data lifecycle management which are incremental and range from collection to expiration of data.
Internal processes for collecting, storing, and synthesizing data can be improved with an effective DLM system. Compliance with regulatory requirements, efficient business processes and security are some of the benefits of data lifecycle management.
Deployment of automated solutions, internal alignment on DLM policies, defined data types, contingency planning, and implementing naming conventions need to be consider when managing the lifecycle of internal data.
Types of data lifecycle management
- Data Creation
- Data Ingestion
- Data Storage
- Data Processing
- Data Analysis
- Data Sharing
- Data Archiving
- Data Backup
- Data Retrieval
- Data Transformation
- Data Migration
- Data Governance
