Information lifecycle management (ILM) is a comprehensive approach to managing an organization's data and associated metadata, starting with its creation and acquisition through when it becomes obsolete and is deleted. An effective ILM strategy can help lower storage and data management costs, as well as reduce the security, compliance and legal risks that come with failing to maintain full control over organizational data.
Unlike earlier approaches to data storage management, ILM deals with all aspects of data throughout its life span, rather than focusing only on one facet of data management. For example, hierarchical storage management is concerned only with automating storage processes and not with how data is transformed or used. ILM addresses how data is utilized and many other issues. In addition, ILM enables more complex criteria for storage management than systems that rely only on basic metrics, such as data age or access frequency.
Information lifecycle management takes a policy-based approach to handling data, providing a centralized, consistent strategy for managing the entire data lifecycle. ILM also facilitates automation and storage tiering. In this way, data can be automatically migrated from one storage tier or format to another based on the applicable policies. As a rule, newer data and data that must be accessed more frequently are stored on faster, more expensive storage media, while less-critical data is stored on slower and cheaper media.
The ILM approach enables IT teams to specify different policies for different types of data throughout its life span. ILM takes into account that data declines in value at different rates, with some types of data retaining its value much longer than other types. In some cases, ILM might also incorporate path management capabilities, which make it easier to retrieve stored data by tracking where it is in the storage cycle.
To be effective, however, ILM needs to be an organization-wide effort, involving procedures and practices, as well as applications and technology platforms. That ability to better track and retrieve information provides a key benefit for IT, the legal team and the business when faced with e-discovery requests, according to consultancy Deloitte.
Deloitte also noted that ILM can introduce "management rigor and controls" of information for the entire business.
ILM is commonly confused with data lifecycle management (DLM). In fact, the two terms are often used interchangeably; however, they're not the same thing. One way to look at ILM is as a more complex subset of DLM.
So, while DLM products deal with general attributes of files, such as their type, size and age, ILM provides more complex capabilities.
Think of DLM as being concerned with data sets as a whole. However, ILM focuses on what's inside those data sets, such as the information in document files. For example, a DLM product would enable a user to search for a certain file type of a certain age, but an ILM product would enable the user to search through multiple file types for instances of a specific piece of information, such as a customer number and, subsequently, the data associated with that customer account.
The type of control that ILM can provide has become increasingly important as more regulations have been enacted. The European Union's General Data Protection Regulation (GDPR), for example, guarantees an individual's right to be forgotten, and the California Consumer Privacy Act specifies that an individual has the right to know about the personal information that a business collects and how that information is used and shared. An ILM product can help locate the individual's personal data, but a DLM product cannot.
The ILM process is often described in terms of the phases, or stages, that data passes through as part of the information lifecycle. Different resources often define these phases in different ways, although many of them are usually close in concept. The following seven phases provide a general overview of what happens with data during its lifecycle:
Although data typically passes through all seven stages, this process should not be thought of as a strictly linear flow of information. For example, data creation and collection are ongoing operations that can occur as some of the data passes through other phases. In addition, data might be transformed before it is stored, after it is stored or both before and after. Meanwhile, data use might come right after data is stored, right after it's transformed or both. Data might even be used after it has been archived.
The only phase that consistently follows a linear pattern is the last phase, in which data is deleted.
See best practices to ensure GDPR compliance and business benefits of data protection and GDPR compliance. Explore storage tiering strategies for modern media and why storage tiering is necessary now more than ever. Also, check out benefits of enterprise content management, ways to manage your data storage strategy and how classification of data can solve your data storage problems.
25 Jan 2022