What Is Data Lifecycle Management and Why Is It Important?

Data Lifecycle Management (DLM) refers to the various stages the data navigate throughout its life. Data lifecycle stages comprise five critical steps – data creation, utilization, secured sharing, proper storage, and deletion.

A set of unique policies controls each stage of the data life cycle.

Data Lifecycle Management refers to the best practices from the various stages of the data life cycle. It defines data management at each phase of the data life cycle. So, following the data lifecycle management rules ensures you have the right data in the right place. Likewise, you can use the insights appropriately provided and monitor data usage across the various stages of the customer’s journey.

Organizations generate data from numerous sources. All such data must be managed and used efficiently to ensure its safety and adhere to all applicable regulations.

The Key Goals of DLM

With the massive increase in the volumes handled by organizations, they are getting stored in more places now than before. You might have to use cloud environments, on-premise servers, and edge computing systems to store data securely. That’s why proper data management is imperative.

The goals of DLM are:

1. Data security and confidentiality

You must make sure that data is stored securely at all times. Using the best DLM practices, you can ensure that your confidential or sensitive information is always protected against potential breaches, compromise, and theft.

2. Data integrity

A successful DLM strategy involves retaining the original form of data and tracking changes as and when they happen. Data integrity needs maintaining regardless of its storage place and who works with it.

3. Data availability

Data is worthless if not available for anyone who needs it for business management purposes. At the same time, easy availability also may cause problems. Authorized users require data availability when and where they need it.

Data Lifecycle Management Framework

Every business interprets and classifies data based on how they use it and its business model and custom data management strategies. However, data goes through a series of consistent steps in its lifespan.

1. Data Creation

The first phase in DLM is the creation and capture of data. You can create data in many forms, such as PDFs, Word documents, SaaS data, SQL database information, and others.

You can obtain data from an outside organization in a pre-existing format or enter data manually internally.

2. Data Storage

Data acquired and captured needs stored properly. Moreover, it must be stored based on the importance or sensitivity of the information contained in them. A robust backup plan also needs put in place, ensuring that the data retains securely in the long term.

Also, consistently implement the tested and established policies around data storage to ensure its security.

3. Data Usage

Data is classified and used by members of an organization at different times based on their needs. Adherence to the data regulatory policies is mandatory during its usage. In the data lifecycle, usage is the most sensitive and critical phase. Your business must establish tracking systems and audit trails to document any changes.

4. Data Archival

The next vital phase in the DLM framework is to archive your data in a secure environment. An archive is a secure location where all your data can be securely stored without maintenance or extra effort. Generally, data not needed for ongoing business operations are archived and separated from the main data.

5. Data Destruction

Data is the most critical component of your business operations, but it can also be a dangerous asset, especially the ones that stop serving any purpose. Keeping such data in your mainstream storage will expose your business to risks. That’s why you must have an effective data destruction policy. Data destruction depends on several factors, such as the media or devices it is on and its form.

Who should use DLM?

All organizations handling private and sensitive data that are subject to regulatory compliance must have a DLM policy in place. If your business needs to collect or store critical information such as contact details, health-related details, bank account numbers, etc., you must use DLM.

Moreover, using DLM helps create a systematic process around the various data lifecycle stages. It also helps create affordable systems based on how your technology stack correlates with your data.

Conclusion

The primary objectives of data lifecycle management are security, integrity, confidentiality, and availability. These objectives need applied to every step of data management – from collection to archival and its final destruction. Likewise, DLM aims at providing adequate protection and disposal of data suitably. At the same time, it requires accessibility for use. Data lifecycle management is a vital aspect of every business today.

Businesses realize the importance of keeping pace with the ever-changing data management requirements. It can prove quite challenging in today’s technology-driven, fast-paced business environment. Moreover, it is important to use the recommended and accepted DLM processes to remain in control of your data at all times and to realize its optimal value.