Like many other concepts in the growing pool of resources called information technology, Data Lifecycle Management (DLM) is important to enterprise users but also somewhat abstract.

In a nutshell, DLM refers to a policy-driven approach that can be automated to take data through its useful life. But what exactly does this mean? Let’s use an example to illustrate.

Imagine that a piece of data is captured and entered into a database. The new data will either be accessed for reporting, analytics or some other use. Or, it will sit in the database and eventually become obsolete. The data may have logic and validations applied to it throughout either process. But at some point, it will come to the end of its useful life and be archived, purged, or both.

The concept of defining and organizing this process into repeatable steps for enterprise organizations is known as Data Life Cycle Management.

A Brief History of Data Lifecycle Management

The 1980s brought the introduction of random access storage (RAM) and with that enterprise businesses transitioned from sequential card-punch and tape approaches to databases. This era heralded the rise of data management to solve the issues of the time. The duplication of sensitive customer data, for instance, was a major cause for concern.

On a parallel path, information lifecycle management (ILM) was also born. In fact, ILM solved an even older problem than contemporary data management initiatives. That’s because ILM could be applied to all types of records from microfiche to film.

However, this definition of ILM became too broad for the digital resources of today. In fact, in 2004, it was decided by the Storage Networking Industry Association (SNIA) that ILM’s definition needed to be reevaluated. Now, ILM it refers to a policy, process and practice-driven approach to aligning the worth of business information with appropriate IT tools, systems and infrastructures for the useful life of a piece of data.

But what exactly does this mean and how is this process different from DLM?

DLM vs ILM

Think of DLM as the set of governing principles that defines and automates the stages of useful life, and determines prioritization. In more simple terms, data lifecycle management is the catalyst that pushes data from one stage to the next, from creation to deletion. It’s a system designed to answer the question: when should this information be deleted?

Information lifecycle management is even more nuanced. It seeks to answer: is this information relevant and accurate?

In a way, DLM and ILM are two sides of the same coin. But you can’t have an effective ILM strategy without implementing a strong system for data management or DLM.

DLM deals with entire files of data, while ILM is concerned with what’s in the file. ILM seeks to ensure every piece of datum included in a record is accurate and up-to-date for the useful life of the record. In the context of ILM, even metadata becomes particularly important.

DLM isn’t concerned with the individual pieces of data within a given record, just with the record itself. And a good DLM strategy will ensure that the most useful and most recent records are accessible with speed and ease. However, under the principles of DLM, as a record passes through defined lifecycle stages, it becomes more and more obsolete. As a result, speed and accessibility are no longer prioritized for stale data.

Both DLM and ILM should form critical aspects of an organization’s overall data protection strategy.