What Is Data Lifecycle Management and Why Is It Important?

Key takeaways

Data lifecycle management (DLM) is the process of organizing data, from collection to deletion, to enhance its visibility and processing efficiency.

• DLM has three goals: to ensure users have access to the data they need, to maintain data integrity and consistency, and to keep data secure.

• DLM consists of five key phases: data collection, data storage and maintenance, data utilization, data archival, and deletion.

• You can ensure efficient data lifecycle management by establishing a centralized data management system, creating an appropriate data storage plan, installing a data security system, setting up data accessibility policies, and maintaining high-quality data.

Use this guide to discover more about the data lifecycle management process and how it can help your business function more effectively. If you’re ready to start learning more about the data lifecycle, enroll in the Google Data Analytics Professional Certificate. You’ll have the opportunity to gain experience with cleaning, organizing, and analyzing data, and visualizing your findings in as little as six months. Upon completion, you’ll have earned a career certificate for your resume.

What is data lifecycle management?

Data lifecycle management refers to the processes that enhance data’s visibility, security, and processing efficiency. All data has a lifecycle, which involves several different phases, including:

• Creation

• Storage

• Sharing

• Using

• Archival

• Deletion

DLM allows companies to have greater control over how they collect, store, and manage their data throughout each phase of the data lifecycle. With more control comes the ability to easily delete and replace old data and protect it for future use.

Why is data lifecycle management important?

Data lifecycle management enhances your ability to comply with regulations and maintain data quality while aiding you in controlling data storage costs and supporting accessibility and usability across the organization. Collecting data helps businesses learn about customers, create a marketing strategy, enhance customer service, and track company performance. Proper DLM helps ensure your company has quick and easy access to the correct data and secure data storage. 

Who uses data lifecycle management?

In business, many departments collect and use data, including accounting, finance, business development, human resources, sales, marketing, and operations. However, IT professionals, such as chief data analysts or other IT experts, typically oversee data lifecycle management.

What are the three main goals of data lifecycle management?

Data lifecycle management achieves three important goals for companies. They include:

1. Availability: DLM provides relevant users with access to the data they need.

2. Integrity: DLM ensures data accuracy, reliability, and consistency.

3. Security: DLM ensures the protection of data from unauthorized access or corruption.

5 phases of data lifecycle management

When managed properly, data cycles through several phases, from collection to deletion. These phases may differ slightly depending on your company, but most businesses follow a similar structure. The five main phases of DLM include collection, storage and maintenance, use, archival, and deletion.

1. Collection

The data lifecycle starts with collection (or creation). Your company might collect data in various ways, including:

• Customer interviews or focus groups

• Online forms or surveys

• Online tracking of user behavior

• Social media monitoring

• Website monitoring

Companies can also collect data by acquiring it from another company.

2. Storage and maintenance

The next phase involves data storage and maintenance. To store and maintain data correctly, you may want to adopt processes to ensure reliability, avoid redundancy, and guarantee recovery in the event of an emergency. Stored data may require encryption to comply with industry practices or government rules and regulations regarding security.

3. Use

In this phase, you define who uses and shares data and for what purposes. Once it’s available and locatable, users can access data for internal activities like daily business operations. They may also share data for contracted services like advertising, marketing, consulting, and financial management with external service providers.

4. Archival 

When your company no longer needs data daily, you may want to archive it for future access. Maybe you don't need to access the data readily, but legal and government regulations might require that you retain it. Data archival involves moving this data type to an offline storage medium like a hard disk drive. Your company can then access and use it when needed.

5. Deletion 

You should delete data that no longer serves your company's current or future purpose. Purging data generates space for new, more valuable data, and it saves on the expense of data storage. 

The following methods allow for automated deletion of archived data:

• Time-based: The system purges archived data after a certain period of time, such as 12 months.

• Volume-based: The system purges archived data when it reaches a specific volume. 

What are some benefits of data lifecycle management? 

Data lifecycle management can offer varied benefits to your company, including improved data quality and operational efficiency. DLM-related advantages include:

• Better data: Following DLM guidelines ensures more accurate and reliable data.

• Greater efficiency: DLM sustains data quality throughout the entire lifecycle, which may allow your company to operate more efficiently. 

• Improved staff performance: When users can locate and access data easily, they make more informed decisions and respond better when conflicts arise.

• Enhanced customer service: DLM allows users to get the most out of data, which can improve customer service and may increase sales. 

• Higher security: Establishing data use, sharing, and storage protocols boosts security and reduces risks of misuse and unauthorized access. 

• Reduced redundancy: DLM lowers duplicate data risks, saving storage space.

• Compliance: Policies set by DLM can help your company comply with data regulations.

What tools help manage the data lifecycle?

Data lifecycle management tools help automate and optimize the data management process. This can include tools like AWS Data Lifecycle Manager, Microsoft Purview Data Lifecycle Management, and Nutanix Data Lens. These DLM tools allow you to set retention policies that allow you to specify and automate what data will be retained or deleted while ensuring the security of your data.

5 data lifecycle management best practices

Data users must feel that the information they access is updated and reliable. Your company can ensure reliable, timely data by incorporating good DLM practices. Consider these five best practices for DLM:

1. Establish a centralized data management system.

A centralized system that imports, stores, and manages all data is mission-critical. It helps ensure more effortless data transformation, higher security, better user access and sharing management, and effective backup procedures in case of server failure. 

Read more: What Does a Data Management Specialist Do?

2. Create a data storage plan that works for your business. 

Think about how you'll preserve and back up data, the size and scope of your business, and the following storage options:

• Desktop computers

• Laptops

• Networked drives

• External hard drives

• Flash drives

• Cloud storage

• Optical storage

3. Consider installing an extensive data system. 

To ensure the highest level of security in your company, it’s vital to ensure your company complies with local data security regulations. To that end, you should consider creating a security plan in case of a data breach.

4. Define data accessibility more clearly across your company.

 To ensure the right user receives the right data, incorporate a policy that provides access to data according to a user's position in the company. 

5. Maintain high-quality data with a few simple practices. 

Carefully examine the data to identify missing or inaccurate information. Cleanse the data to remove redundancies and standardize it for better compatibility and easier use.

Explore resources to take your skills further

Want to keep the momentum going? Keep up with the latest trends and in-demand skills shaping your industry by subscribing to our LinkedIn newsletter, Career Chat! Or if you want to keep exploring data-related careers and skills, check out these free resources:

• Take the quiz: Which Data Analysis Course Should You Take?

• Watch on YouTube: 7 Essential Data Analytics Skills for Beginners 

• Hear from a fellow learner: Meet the CPA Advancing Her Data and Leadership Skills with an M.B.A. 

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