What is Data Lifecycle Management DLM? The Complete Guide

In today’s tech-driven world, we’re moving towards a future defined by huge amounts of data. According to the World Economic Forum, by 2025, it’s estimated about 463 exabytes of data will be produced each day. That’s over 212 million DVDs of data per day.

Data lifecycle management is how companies ensure they have the right processes in place for effectively storing, managing, and leveraging data.

Here are the answers to some of the key questions you may have about Data Lifecycle Management (DLM): what is data lifecycle management, what does it involve, and why is it so important?

What is Data Lifecycle Management (DLM)?

Data lifecycle management, or DLM for short, is a policy-driven strategy for managing the flow of information in a company or system. It starts from the moment a data point is created, continues through its storage strategy, and ends when the data is deleted.

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DLM is essentially the governing principles which defines and automates the stages of your data’s life, determining which information is prioritised, where it’s stored, and how it’s pushed from one stage in the funnel to the next.

DLM strategies are the guidelines companies use to keep up with a constantly growing series of regulations, eDiscovery requirements, and data expectations. The right tools also help to ensure businesses get the most out of the data they collect. With a DLM strategy, you can optimize everything from data archiving to storage and usage. Learn how to implement a data lifecycle management strategy, too.

DLM in Project Management

In a project management environment, DLM refers to the best practices used to manage data throughout the life of the project. It involves looking at how data is implemented into the project, where and when it might be used, and how it will be stored or removed from the system.

How is DLM Different from ILM (Information Lifecycle Management)?

Data and information lifecycle management often go hand-in-hand. However, it’s a mistake to use these terms interchangeably. Companies should think of DLM as the key principles used to manage, store, and remove entire files of data. DLM looks at the flow of data from one stage of a lifecycle to the next, answering exactly when data should be deleted.

ILM looks more at the relevancy and accuracy of the information being managed. While DLM operates on the entire file, ILM looks at the type of information within the file. While DLM products manage files based on their type, shape, maturity, and size, ILM solutions allow businesses to search for specific files and understand the information within them.

What Are the Three Main Phases of the Information Lifecycle?

Data Lifecycle Management needs to consider all stages of the “information lifecycle”. These include:

1. Creation/Acquisition: Where data enters the business, through emails, faxes, letters, phone calls, or the creation of crucial reports.
2. Publication: How data is used or published to a company’s intranet or website.
3. Retention/Removal: How the data is archived, stored, and eventually removed from the business system.

What are the Stages of the Data Life Cycle?

As data management becomes a more common concern for businesses, every company is beginning to explore it’s own options for Data Lifecycle Management. However, these processes must always consider each stage of the data lifecycle, which is similar to the information lifecycle mentioned above. The key stages to consider include:

• Collection or creation: This is the first stage in the data journey, when new data points enter the organisation’s information systems. Data can be collected from external devices, like IoT systems, software, reports, and proprietary research. During the collection or creation stage, organisations classify data based on its nature, file type, and structure.
• Data storage and maintenance: Once data is created or collected, it’s important to secure it in the right environment for consistent data hygiene. Comprehensive data backup and recovery processes are often essential to ensuring the data is retained correctly. Data must be stored in a way compliant with relevant contracts and legislation.
• Data usage: This is the stage when companies view, process, and leverage the data they collect. This involves transforming data into analytical insights and actionable information to serve various business purposes. At this point, it’s important for the company to ensure it’s following the right compliance and governance standards.
• Data sharing: Similar to the “publication” aspect of the information lifecycle, the data sharing stage involves delivering data to other people in the organization. Data needs to be protected at all stages, in rest and in transit, to ensure it doesn’t fall into the wrong hands.
• Data archiving: Once data has been successfully used, shared, and managed by the team, it can be placed in an archive for auditing and compliance purposes. DLM strategies must outline when, where, and for how long each piece of data should be stored.
• Data deletion or reuse: Since most companies are producing an incredible amount of data (Around 2.5 quintillion bytes a day), it would be impossible to store everything. This means organisations must use their DLM strategies to determine which data will be deleted, and which needs to be reused.

What are the Benefits of Data Lifecycle Management?

Data Lifecycle Management is crucial to streamlining the flow of information and optimizing data at every stage. The most significant benefits of it include:

• Governance and compliance: Every industry sector has specific stipulations for how data is managed and used. For instance, the Criminal Justice Information Services state agencies should retain records for at least 1 year, or until they’re no longer needed. DLM helps companies comply with regional and local regulations, while preparing for audits and investigations.
• Data Protection: Data security is quickly becoming a major concern in today’s digital landscape. DLM helps organisations protect data from loss, deletion, attacks, and more. With DLM systems, companies can access a level of redundancy, minimise the risk of data breaches, and prevent sensitive data from being misused.
• Value and efficiency: Almost all businesses today are driven by data. The right information plays a crucial role in determining the next steps of any business strategy. With a good DLM strategy, companies can ensure the data available to help them make their decisions is as reliable and accurate as possible. A good DLM strategy also helps companies to maintain data quality throughout the entire lifecycle.