What is Data Lifecycle Management (DLM) ?
Data lifecycle management refers to the holistic set of practices for governing data from inception through retirement across the entire organization. It focuses on managing data seamlessly through all phases of its useful life – from planning and creation to processing, storage, utilization, archival, and deletion. Robust policies and procedures systematically cover data security, integrity, usability, costs, and regulatory compliance at each stage.
What is Data Lifecycle Management?
Data lifecycle management provides end-to-end visibility and control over the data flowing through systems and processes. It enables harnessing data as an asset at every stage – from planning through active use and eventual retirement. With strong data lifecycle management, organizations can channel data into a strategic advantage rather than suffer from overwhelming disarray.
This article will explore the key stages, practices, benefits, and implementation steps for robust data lifecycle management programs. With these foundations, organizations can transform even exponentially growing data from an unruly liability into a trustworthy and adaptable asset powering competitive advantage. Taming the data deluge through lifecycle management unlocks lasting business value.
Data LIfecycle Management System
Table of Content
- The Critical Role of Data Lifecycle Management in the Era of Big Data
- Defining Data Lifecycle Management
- Main Phases of Data Lifecycle Management
- Key Data Lifecycle Stages
- Data Lifecycle Management Capabilities
- Benefits of Data Lifecycle Management
- Implementing Data Lifecycle Management
- Expert Techniques for High-Impact Data Lifecycle Programs
- What are the 3 Main Goals of Data Lifecycle Mangement ?
- Conclusion