Main Definition
Data Lifecycle Management (DLM) is a policy-driven framework for governing data from creation through storage, archival, and deletion to control costs and maintain compliance. DLM organizes data into distinct stages: creation, storage, active use, archival, and secure deletion. It is a dynamic practice that when done well, can increase the ROI an organization gets from its data. Each stage of DLM is governed by rules based on access patterns, business value, and regulatory requirements. The goal is simple: keep the right data accessible, move inactive data to lower-cost tiers, and dispose of what’s no longer needed.
For organizations managing massive unstructured data environments, DLM is a critical priority, but not always easy to implement. However, without it, storage can grow unchecked, sometimes as much as 20–30% annually which drains budgets and creates compliance risk. Likewise, an active DLM approach ensures organizations are living up to regulatory requirements around data sharing or deletion.
At the core of any DLM strategy is full data visibility and the ability to automate policies. Managers must have a complete view of where their data resides, how long it has been there, and who the owner is. Data management tools must be able to automate archive, deletion, and copy policies at scale.
Starfish puts lifecycle policies into practice across heterogeneous file systems at scale, allowing IT teams to work closely with data owners to automate moving the right data at the right time, while optimizing the use of storage resources.
Related links
- The Importance of Data Lifecycle Management & Best Practices | IEEE Computer Society
- Data Lifecycle Management | Coursera
- Data Lifecycle Management | IBM
- Data Lifecycle Management | Varonis
