The 5 Phases of Data Lifecycle Management

Data Lifecycle Management - February 21, 2023
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How to manage your data throughout its lifecycle

Within your organization, data is constantly being created, sent, received, edited, moved, and deleted. Each employee throughout your organization makes daily decisions on what to do with data in their care. Most are not considering the ramifications of these decision. To ensure proper measures are taken by your team, you need to develop, communicate, and enforce appropriate data management guidelines.

There’s a lot to consider when it comes to data lifecycle management (DLM). At its core, it’s an approach to help a company know how to store, organize, use, evaluate, preserve, and destroy all the data that is created or received within the organization. This is vitally important because data needs to be preserved and managed properly to ensure its usability and protect the company from security and legal risks.

While it might seem overwhelming, the truth is that you can break down a DLM strategy into five main phases. By developing rules and guidelines within each phase, you are well on your way to creating a holistic DLM strategy.

5 phases of data lifecycle management

DLM consists of creating a governance framework of best practices and standards for managing the flow of data throughout its lifecycle. While there are different takes on how to categorize the phases, they can generally be summarized in the following.


Design create store manage use share archive preserve delete destroy


Phase 1: Data Design & Creation – New data is created all the time, every single day. Think of all the operational, transactional, and technical data that is created within your organization Transactional data, consumer support, financial info. The list goes on. That data needs to be captured and organized in a way that is both secure and accessible for its future use. And the truth is, not all data is created equal. Some – like sensitive customer information – have strict rules and regulations for how it must be stored and accessed. Whereas other data sources can be a liability to keep past their necessary use case. In a DLM approach, data is evaluated based on its quality and relevance to the business, metadata tags are created based on data source and veracity, and guidelines are created for how to best handle various types of data creation.


Phase 2: Data Storage & Management – Once data has been created, it needs to be stored and protected. Proper data storage is about much more than simply deciding where you host your data. Of course, that is an important component. Is it stored on-premises or in the cloud? What is the backup process to ensure proper redundancy? When storing data, it is typically encrypted, cleansed, compressed, or transformed to ensure integrity and security. You’ll want to create a log so that you can track storage locations and log movements.

You also need to consider data protection rules and regulations within your industry or region. Building a process to ensure compliance with regulations such as HIPPA, SOX, GDPR, and CCPA, to name a few, will help protect your organization against legal and financial risks. Considering the costs of GDPR fines can reach $20 million Euro or 4% of your annual revenue, mismanagement of this type of data can be detrimental to a business.


Phase 3: Data Usability – Data is only as powerful as its usability.  Putting parameters around how to optimize data for use is an essential component of proper Data Lifecycle Management.  What are you doing to make sure you increase the value of your data?
Questions for consideration during this phase include:

  • Are you adding encryption to ensure data is secure?
  • What metadata can be added to increase its usability?
  • How do you manage who has access to various types of data?
  • Is it organized and categorized in a way that it can be easily accessed and manipulated for its various applications?
  • What are you doing to maintain data integrity if users unintentionally manipulate critical data?

Phase 4: Data Retention & Preservation – When data is not needed on an ongoing basis, but shouldn’t be deleted, it needs to be preserved. Data preservation is the process of maintaining and preserving digital information over time so that it remains accessible and usable. Often this means archiving it in a non-alterable format so that it is available if needed, but not at risk for manipulation.

Another important part of an organization’s DLM strategy is a data retention policy. Considering any industry or regional regulations, a data retention policy outlines a protocol for how long a company will retain various types of information. A comprehensive policy helps a company automate compliance while reducing legal risks, reduces storage costs, and increase the relevance of existing data.


Phase 5: Data Destruction & Deletion – Eventually, data is no longer needed to support an organization’s day-to-day operations and needs to be deleted. Keeping data past its necessary shelf-life is not only costly, but it can also put the organization at risk. But deleting it haphazardly can cause legal ramifications. It is important to be strategic when determining what data gets deleted and what gets archived. Companies will often set up a data deletion committee that is responsible for creating a data deletion policy and action plan. Additionally, it’s common practice to log deletion locations and dates so that you have an up-to-date record.

Data management needs to be an integral part of an organization’s IT strategy. When handled correctly, data management helps a business streamline its operations and protects it from a myriad of risks. Learn more about why a comprehensive data lifecycle management strategy is the key to efficient, effective, and secure IT management and get tips on how to get started creating a DLM approach, by reading our Data Lifecycle Management FAQ.

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