Data lifecycle management (DLM) is a policy-based approach to managing the flow of an information system's data throughout its lifecycle: from creation and initial storage to when it becomes obsolete and is deleted.
DLM products automate lifecycle management processes. They typically organize data into separate tiers according to specified policies. They also automate data migration from one tier to another based on those criteria. As a rule, newer data and data that must be accessed more frequently is stored on faster and more expensive storage media, while less critical data is stored on cheaper, slower media.
Organizations are handling more data than ever, and that data might be stored on premises, at colocation facilities, in edge environments, on cloud platforms or any combination of these platforms. The need for an effective DLM strategy has never been greater, but the strategy must be a comprehensive one to be effective.
Many resources cite the following three goals -- or a close variation of them -- as the most important ones to achieve in an effective DLM strategy:
Data security and confidentiality have become increasingly important as organizations face the mounting body of compliance regulations such as the Sarbanes-Oxley Act (SOX), General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) and California Consumer Privacy Act (CCPA).
Data management experts stress that data lifecycle management is not a product, but a comprehensive approach to managing an organization's data, involving procedures and practices as well as applications.
DLM can be broken into multiple phases that provide a framework for working with data throughout its lifecycle. Although different resources identify these phases in various ways, they often follow a structure similar to the following:
Not all DLM phases are strictly linear. As already pointed out, the third stage might result in additional data being generated. In fact, the first three stages often occur simultaneously, with data being continuously generated, collected, stored, managed and made available for authorized usage.
Hierarchical storage management (HSM) is sometimes confused with DLM, but HSM is only one type of DLM product. The HSM hierarchy represents different types of storage media, such as solid-state drives (SSDs), hard disk drives (HDDs), optical storage or tape systems. In this model, each storage type represents a different level of cost and performance.
Using an HSM product, an administrator can define guidelines for how often different types of files should be copied to a backup storage device. Once a guideline has been deployed, the HSM software manages everything automatically.
Another source of confusion is the difference between DLM and information lifecycle management (ILM). Although they're sometimes used interchangeably, they differ in important ways. According to Karen Dutch, who was once vice president of product management at Fujitsu Softek, DLM products deal with general file attributes such as type, size and age; ILM products have more complex capabilities.
For example, an administrator can use a DLM product to search stored data for a certain file type of a certain age. In contrast, the administrator can use an ILM product to search various types of stored files for instances of a specific piece of data, such as a customer number. This type of control has become increasingly important in the age of compliance regulations.
The GDPR, for example, guarantees an individual's right to be forgotten. An ILM product can help locate the individual's personal data, but a DLM product cannot.
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