Expert Techniques for High-Impact Data Lifecycle Programs
Beyond the basics, mature programs leverage advanced techniques like:
- Using workflow automation across the lifecycle for efficiency.
- Building centralized data services and tools for reuse.
- Leveraging artificial intelligence for classification and quality monitoring.
- Developing multi-tiered storage strategies to balance cost and performance.
- Extensive use of master data management and data catalogs.
- Embedding data stewardship into cross-functional teams.
- Collaborative issue escalation and change control processes.
- Convergence of analytical sandbox environments for flexibility.
- Automating disposal of unused data replicas and versions.
- Monitoring adoption and success metrics to guide enhancements.
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.
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