Increased reliance on data brings many responsibilities and challenges, like data management, security, and maximizing the data potential. This is where Data Lifecycle Management (DLM) comes into play. 

What is Data Lifecycle Management? 

At its core, Data Lifecycle Management (DLM) is the process of managing data from its creation to its destruction. During this process, data moves through different stages, such as data creation, storage, sharing, usage, archival, and eventually deletion. DLM aims to deliver value at every stage of its lifecycle while maintaining data security and regulatory compliance. You can look at DLM as a policy-based approach to managing the flow of data in an organization throughout its life cycle across different platforms, systems, and storage media.

Let’s take an example to understand the whole process of DLM. Suppose you have a food delivery application:

  • A customer creates an account, place orders, and write reviews. Such activities lead to generating customer data on your website or mobile application. 
  • The application server stores the customer data in the website or mobile app database and organizes it properly.
  • The data analytics team needs to analyze customer preferences. Therefore, you centralize the data in a data warehouse and share the data with them with the required access controls. 
  • You use the customer data to track trends and improve business operations.
  • After using the application for a while, suppose the customer leaves the application. However, you want to keep customer data for historical purposes, so you archive the customer profile. 
  • Lastly, as the profile becomes outdated and is no longer relevant, you delete the customer data securely, and the data lifecycle of the customer ends here. 

Stages of Data Lifecycle Management 

As mentioned above, different stages are involved in DLM. Here is every stage in detail: 

Data Creation

Data can be created in many ways. It can be generated by users, applications, Internet of Things (IoT) devices, and more. Since data sources are abundant, you have to be careful about what kind of data you need to work with. 

Therefore, it is crucial to evaluate the data quality and relevance while creating or acquiring the data from a source. This stage involves practices like data mapping and classification to quality-check the data for business operations.

You can use ETL tools to extract data from multiple data sources in near-real time and streamline data collection practices. ETL tools provide many features, including data transformation and quality enhancement, to ensure the data is ready for business operations.

Data Storage

This stage is about figuring out how you want to store your data. There are various ways of storing data. You can use a database, a data warehouse, or a data lake. A database is used when storing structured or unstructured data is the only priority. 

However, data warehouses and data lakes are used to store, process, manage, and secure data effectively. A data warehouse is mostly used for analytics requirements. A data lake, on the other hand, can be used to store diverse datasets, including unstructured and semi-structured data. 

Most organizations use all these methods collectively, but the choice of the database for lifecycle management should be according to the characteristics of an organization. Additionally, data storage also involves data protection and compliance. Therefore, you can evaluate infrastructure with the required security measures such as data encryption, access controls, etc.

Data Sharing & Usage

At this stage of DLM, the data becomes available to business users and other working professionals. You can share the data and define who can access the data and for what purpose. 

While performing data sharing, practices like data democratization are crucial to maintaining a collaborative environment.  After the data is made available to the required individuals, it can be used to perform a range of analyses. This includes creating easy-to-understand visualizations, carrying out exploratory analysis, and even harnessing the power of machine learning. 

Data Archival

Some data loses relevancy over time and isn’t used that frequently. However, it is important to maintain a backup of that data for historical record tracking and future remembrance. Therefore, such data must be stored in a more cost-effective solution, such as a data lake, from where it can be restored whenever it is needed. A DLM strategy should have a clear definition of when, where, and how long data needs to be archived.

Good examples of archived data are past financial records, past medical records, and outdated emails. 

Data Deletion

In this stage of DLM, the data that no longer needs to be retained is securely removed from the system. The process for deleting the data is straightforward. However, you must ensure data is removed securely to ensure data privacy. As mentioned above, removing outdated customer data in a food delivery platform is data deletion.

Best Practices of Data Lifecycle Management

Data Lifecycle Management can be a complex process where effective planning and execution are necessary for maintaining data accessibility and security. Below are some of the best practices to build a sound DLM strategy: 

Data Classification

As you know, data come from many sources, and all the data that is created is not the same. Therefore, categorizing it according to its sensitivity, value, and regulatory requirements becomes crucial. 

By differentiating data into categories, you can allocate resources efficiently and take the required security measures for access controls, audit trails, and more. This ensures organizations can easily protect, manage, and identify their most sensitive and valuable data, making management more efficient. 

Data Governance

Data Governance is everything you do to make data more secure, available, and accessible. It ensures that the data is handled consistently and complies with regulatory requirements. 

To build a good DLM strategy, you have to establish a clear data governance framework. This involves establishing data ownership and defining consistent policies and procedures throughout the data lifecycle. To streamline this process, you can use these popular data governance tools

Deploying Automated Solutions

Using a DLM tool that provides automated solutions can significantly enhance the efficiency of data management. Many tools offer services to automate the creation, security, backup, deletion, and other routine tasks of DLM. The choice of the task you want to automate depends on specific business requirements. Some well-known automated backup and security tools for DLM include IBM Spectrum Protect, Commvault, and Veritas NetBackup. By using these tools, organizations can reduce the risk of human error and ensure data consistency and integrity. 

Sharing the DLM Procedure 

Once a fully-fledged DLM strategy is established, organizations should share the procedure across the whole team. Using this practice, you can include the entire team of an organization to onboard and take part in the DLM program. It will ensure everyone knows the procedure and takes action accordingly. Additionally, communicating the procedure can give you new ideas and suggestions from other team members that you might need at the time.

Recovery Plan

A robust recovery plan ensures data recovery during data loss due to unforeseen events like cyberattacks, natural disasters, or human errors. This plan includes data backups and a clear procedure for data restoration. For an effective DLM strategy, it is crucial to invest in a reliable backup system and have a clear plan on how to approach data loss. 

Regular Review and Updates

DLM is an ongoing process that requires regular review and optimizations according to business requirements and data needs. A review of data storage, quality, and compliance with data retention policies will ensure your strategy is in order with the evolving needs of an organization. On the other hand, updates will allow you to make necessary changes to data classification, governance, and access controls. 

Benefits of Data Lifecycle Management 

DLM offers a lot of key benefits to organizations. Some of them are mentioned below: 

  • Data security: A good DLM strategy includes data encryption, secure storage, and data regulation compliance, which keeps your data secure and, in turn, reduces the risk of data breaches.
  •  Data Quality: Data is valued at every stage of DLM, and many practices, such as data classification, regular audits, and data cleansing, ensure you are working with high quality from inception to destruction. 
  • Data Recovery: Data backup and recovery processes are a part of DLM. It enables quick data restoration during hardware failure, data corruption, or other emergencies. This makes sure that organizations don’t face the risk of data loss. 
  • Compliance: With its clear policies for data retention and deletion, secure access controls, and required security measures. DLM makes sure that data management is carried out in a manner that complies with the legal requirements of industry and government laws. 
  • Cost Savings: In the process of DLM, once data becomes non-relatable or is no longer useful for active business operations, organizations can perform various tasks to reduce costs. This can include data backup, archiving, and replication to a cost-effective storage solution. 


In this article, you have learned about Data Lifecycle Management, its different stages, best practices, and benefits. By implementing an effective DLM strategy that includes all the best practices mentioned above, you can harness the full potential of your data assets. 

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Jalaj Jain
Freelance Technical Content Writer, Hevo Data

Jalaj has extensive experience in freelance writing within the data industry and is passionate about simplifying the complexities of data integration and data analysis through informative content for those delving deeper into these subjects.

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