Data Lifecycle Management (DLM): Why is it Important?

As data is now the most important asset of most organizations, data breaches, mishandling, or loss can and will cause significant disruption across many areas of operations. To mitigate these risks, you’ll need to implement Data Lifecycle Management (DLM). Effectively protecting and managing your organization’s information has become more critical than ever, and DLM is an approach that aims to ensure data privacy and security from data creation to destruction. DLM provides benefits such as risk management, a better understanding of a business’s requirements and goals, and optimal decision-making. 

What is Data Lifecycle Management (DLM)?

Data lifecycle management (DLM) is a policy-based approach to managing the flow of an information system’s data throughout its lifecycle: from creation and initial storage to when it becomes obsolete and is deleted.

DLM products automate lifecycle management processes. They typically organize data into separate tiers according to specified policies. They also automate data migration from one tier to another based on those criteria. As a rule, newer data and data that must be accessed more frequently are stored on faster and more expensive storage media, while less critical data is stored on cheaper, slower media.

The Need for Data Lifecycle Management

Organizations are now dealing with more data than ever before. Managing this information has become a critical job for any enterprise. Data moves in many different ways across an organization, whether it’s from one department to another, or back and forth with a client. To keep track of all of these different data paths, most companies need the data lifecycle approach. This approach helps to ensure that the data being used is the most up-to-date and accurate version of the data while maintaining its security.

data lifecycle management

Benefits of Data Lifecycle Management

DLM has several important benefits which include:

  • Process improvement: Data plays a crucial role in driving the strategic initiatives of an organization. DLM helps maintain data quality throughout its lifecycle, which in turn enables process improvement and increases efficiency. A good DLM strategy ensures that the data available to users is accurate and reliable, enabling businesses to maximize the value of their data.
  • Controlling costs: A DLM process places value on data at each stage of its lifecycle. Once data is no longer useful for production environments, organizations can leverage a range of solutions to reduce costs such as data backup, replication, and archiving. For example, it can be moved to less-costly storage located on-premises, in the cloud, or network-attached storage.
  • Data usability: With a DLM strategy, IT teams can develop policies and procedures that ensure all metadata is tagged consistently so it can improve accessibility when needed. Establishing enforceable governance policies ensures the value of data for as long as it needs to be retained. The availability of clean and useful data increases the agility and efficiency of company processes.
  • Compliance and governance: Each industry sector has its own rules and regulations for data retention, and a sound DLM strategy helps businesses remain compliant. DLM lets organizations handle data with increased efficiency and security while maintaining compliance with data privacy laws regarding personal data and organizational records.

Three main goals of DLM

The number one challenge companies face while growing and amassing data is a data breach, which means that the data must be managed effectively throughout its lifecycle. The three most important data lifecycle management goals can be categorized as follows:

  • Data storage & security – Once the data is acquired it needs to be stored securely thus limiting the misuse of data.  Structured data can be stored in on-premise databases or the cloud while unstructured data is typically stored in file servers or the cloud.  Regardless of where it is stored, the data needs to be secured against unauthorized access and theft.
  • Data availability – Since the business is essentially driven by data, it’s crucial to ensure its availability to the business.  Availability also includes processing and visualization of data as required by the business.
  • Data resiliency – As the data ages, it can morph over time due to modifications, and cleansing activities.  Such activities can also result in data sprawl, meaning the same data can exist in multiple locations in slightly different forms.  Therefore, it’s necessary to put a process in place to ensure the integrity and resiliency of data.

data lifecycle management

Data Lifecycle Management stages

A well-run and strong data management process ensures that organizations understand, map, and control their data throughout the journey, as it is created, published, modified, and stored. The lifecycle of data management involves the following six stages:

  • Data acquisition: Data enters a system through data entry or when another system sends it.
  • Data storage and maintenance: The data is processed for validation and accuracy and then stored in the relevant system.
  • Data usage: The constructed data is converted into usable formats that a specific software can interpret.
  • Data publication: Data is sent to the concerned platforms and published for concrete use (for example, purchase orders (data) are printed and attached with the shipments)
  • Data archiving: Once a piece of data is used for its purpose, the system archives it.
  • Data cleaning: If systems find the data no longer in use, they clean it or shift to less costly or on-premises storage systems.

These stages reflect how a piece of information moves through different systems within an enterprise.


Data lifecycle management as a practice is vital for all organizations. Holistic data management should not be an afterthought but a critical strategic capability of an organization. Key roles that an organization may consider are data trustee, data custodian, data steward, data owner, and data manager.

Not managing data effectively is a considerable risk for the organization and its customers. Data is growing, and its management of it cannot be left behind. Open architectures and the ability to connect to anything anywhere give a better experience because of data accessibility and create more opportunities for data challenges, especially malicious use of data. Make data lifecycle management a strategic initiative for your organization.


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