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The larger a business becomes, the more difficult it gets to manage the data it generates in a consistent and safe manner. While this might seem like a benign issue at first, the consequences of poorly managed data can quickly lead to things like data sprawl, lost files or privacy violations that can severely harm a company.

Understanding exactly what data lifecycle management or DLM, for short is can be daunting, as the topic can be overwhelming for people without a background in the field. This article will give you a better idea of what DLM is, how you can implement it and its benefits.

DLM is crucial for any business environment to ensure that data is handled responsibly so that it is secure, accessible and accurate.

The three stages of the data handling lifecycle are data creation and acquisition, data retention and protection, and data retirement. This constitutes the entire lifespan or lifecycle, if you will of any piece of information. This ensures that data is managed in a consistent and responsible manner, no matter its origin or purpose. All of this is achieved through automated policies that are defined in advance, to minimize the potential for human error anywhere in the process.

In a broad sense, data lifecycle management and information lifecycle management ILM are the same thing. However, ILM operates on a much larger scale because it takes into account a greater amount of information and data than DLM does.

Because the difference is just one of scope, any ILM system inherently includes a data lifecycle management framework as its core. Data lifecycle management is usually divided into three separate stages that cover data throughout its life.

These are data creation and acquisition, data retention and protection, and then data retirement. Occasionally, these stages are further separated into smaller ones, like data collection, data protection and data destruction.

This first stage is exactly what it sounds like. This is when the data or file is created or transferred into the system, which includes creating and naming the file, classifying it for easy retrieval and determining access controls. Once the data has been acquired, it needs to be stored and maintained, which is what this second stage covers. This includes ensuring data redundancy by backing up , safety by employing sufficient protections such as strong encryption and physical server security and accuracy by continually validating and auditing files to ensure accurate data.

When data is no longer useful, it needs to be responsibly retired. This can mean archival storage for information that needs to be retained or potentially referred back to in the future, or destruction for files that are outdated or otherwise useless. Although this is by no means an exhaustive list of everything you need to know when creating a data management policy, these are some of the most critical things to remember.

Hopefully you now have a clearer understanding of how your organization or company should ideally handle every piece of data, as well as how you can go about implementing a robust data management system we also have a guide to what data governance is.

What did you think of our crash course on data lifecycle management? Did we answer all your questions? Are you still as confused as you were when you started? Has your company already taken steps to implement DLM? If so, what benefits have you seen as a result? Let us know in the comments below. Thank you for reading. Wave FreshBooks vs. Xero FreshBooks vs. QuickBooks Online Xero vs. A Guide for Your Business.

However, any affiliate earnings do not affect how we review services. Data lifecycle management involves data creation and acquisition, data retention and protection, and then data retirement. Aleksander Hougen. Read their full bio.

 
 

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