ILM vs. DLM: The importance of data management

Knowing the differences between information lifecycle management (ILM) and data lifecycle management (DLM) will ultimately determine which storage offerings you bring to a client's table.

Sometimes the terminology that springs up around concepts can be baffling, even for experts. Consider the confusion that surrounds the terms information lifecycle management (ILM) and data lifecycle management (DLM). Since the words "information" and "data" are used so interchangeably, both in layman's terms and in professional parlance, it's hard to tell at a glance if there's any difference.

Information lifecycle management

ILM is "a comprehensive approach to managing the flow of an information system's data and associated metadata from creation and initial storage to the time when it becomes obsolete and is deleted." That descriptive information about a file is not just confined to its name, but often rides with the file in the form of metadata (or is stored externally in some kind of "sidecar" application), the mention of metadata in this definition is doubly important.

More on information and data management:
Data lifecycle management (DLM) services for SMBs

Tiered storage gone wrong

Another important clue comes a little later in the same definition: "ILM involves all aspects of dealing with data, starting with user practices, rather than just automating storage procedures." ILM isn't just about where data is stored, but what's done with it. Perhaps the best way to talk about ILM is to say it is application-specific. Not just applications in the sense of a given software suite, but the way things are used. The nature of the data itself is important -- the way it's used, the way it's structured internally, and who has access to it.

Data lifecycle management

DLM deals with information mainly on the file level. It's more abstracted and less concerned with the specific utility of the data within the file than the physical way the file stored. Take a music CD, for example. ILM would be about the songs -- lyrics, cover art, instrumentation and metadata. DLM would be about the bits on the disc and perhaps the physical disc itself.

Because DLM is more catholic in the way it deals with data, DLM is more suited to applications and procedures where the data is one commodity among many. If you were creating a generic backup tool where the type of data isn't as important as ensuring it is safe, DLM-style strategies would make sense. People offering generic or relatively undifferentiated storage or backup services may eventually want to add more features that deal with the content being archived, but a DLM-style approach would probably be the best place to start.

Differentiating ILM and DLM management systems

The distinction between ILM and DLM becomes more important when it comes to which of these two approaches to apply to a given business practice or application. For some things, ILM may be too granular. For other applications, DLM may be too broad. Find the right lifecycle approach for what you're trying to create or sell, and it'll be easier to frame the features you plan to offer.

For example, if you're preparing an indexing or search application -- everything from simple organizational software to the kind of utility that would be needed for Sarbanes-Oxley compliance -- it'd be useful to have an understanding of ILM processes and procedures. This could apply to internal optimization of items in a search index for the sake of speed to archiving strategies for index entries. The physical data structures in use for this type of work would only really be of interest to the programmer or the admin, and not the end user.

About the author: Serdar Yegulalp wrote for Windows Magazine from 1994 through 2001, covering a wide range of technology topics. He now plies his expertise in Windows NT, Windows 2000 and Windows XP as publisher of The Windows 2000 Power Users Newsletter and writes technology columns for TechTarget.

Dig Deeper on MSP technology services

Cloud Computing
Data Management
Business Analytics