The 5 Main Goals Of Data Lifecycle Management

It should come as no surprise that the data lifecycle, which refers to the journey data takes from the time it is created until it is removed, has significantly expanded in recent years given the prevalence of data in everyday activities such as sending and receiving texts and viewing movies. Previously, the data was kept in paper files, which were difficult to handle and were, as a result, thrown away on a regular basis. However, because data is now kept in a digital format and connected to construct a profile, it is archived for many years and is utilized in a variety of contexts.

The practice of managing the flow of data in an information system throughout its lifecycle, such as when data is created, processed, used further, and then finally removed, is referred to as data lifecycle management (or simply, data lifecycle management). The data in the modern world is processed by a variety of technologies in order to provide findings that are useful, and it is often utilized for a variety of functions. Because of this, the value of the data is significantly increased.

Why Goals are Important in Data Lifecycle Management?

Since there has been a rise in the collecting of data, which also includes sensitive data such as personally identifiable information or passwords, Hence It is now a difficult task to manage data because there are many things that need to be implemented, such as proper authorization, encrypting the PII, as well as managing the security and confidentiality of the data, which should have to be maintained.

These are just some of the things that are required to be implemented. These objectives will serve as the basis for ensuring that information may continue to be efficiently transmitted.

Data Security

Primarily due to the fact that many businesses store vast amounts of data, some of which may include sensitive information such as personally identifiable information (PII), credit card information, and other details that could lead to monetary loss.

The possibility of information being misappropriated is at an all-time high due to the enormous amount of data that is currently available and being used. In most cases, the attackers’ goal is to obtain this data so that they can cause financial damage.

As a result, ensuring the safety of data during the data lifecycle management process is of the utmost importance, and this data should be protected in a more effective manner by implementing new controls such as multi-factor authentication (MFA), CCTV, amongst others.

Data Availability

In this age of information technology, data is collected and used everywhere. Whether you are making an online money transfer or registering for a driver’s licence, data is collected or obtained wherever you go. As a result, data should be required to be available whenever it is required, often known as the availability of data should be required to be 99.9%. If for whatever reason data cannot be accessed, this might cause problems for the user as well as the business that is relying on that data.

This organization now saves data and its replica’s in two or three different data centres so that it can better monitor its availability. Therefore, it can be deduced that the availability of data is the most important aspect of the data management life cycle.

Data Compliance

When an organization makes use of sensitive data belonging to a user, the company is legally obligated to comply with a great deal of regulation. The accomplishment of compliance and governance standards is not only a goal of the organization, but it is also a goal of data lifecycle management.

The government has established a great number of rules and regulations, some of which include data localization and the General Data Protection Regulation (GDPR), amongst others. These compliance guarantees that the best practices connected to data are being followed by ensuring that they are implemented. Depending on the nature of their operations and the products or services they offer to end users, every company is required to adhere to a specific set of government rules.

Without achieving this as a goal, no organization would be able to carry out its business functions that are related to the management of data throughout its lifecycle.

Data Categorization

Data is everywhere, and it often contains a lot of sensitive information, such as credit card numbers, Social Security numbers, driver’s licence numbers, and a lot of other data relating to the user. Therefore, data protection can be found anywhere.

It is important for businesses to assign labels such as protected, private, and public to their data in order to facilitate more effective data management. This will, in essence, classify the data, and it will make it much simpler for the organization or the consumers to manage them. If the material is confidential or otherwise off-limits, there is no reason to make it available to the general public because doing so opens the door to abuse. As a result, the objective of the data management lifecycle should also be the labelling and management of the access to the data.

Data Resiliency

Data can be found anywhere and is currently being gathered by organizations in a variety of formats. Attributes in data can be amended or changed over time. For example, a corporation may start collecting customers’ genders but may decide to stop doing so once a certain amount of time has passed. As a result, the data may be subject to change over time as a result of updates and cleansing actions such as the elimination of attributes.

These kinds of operations can also lead to data sprawl, which means that the same data can reside in many locations and be represented in somewhat different ways. As a result, it is essential to implement a procedure to guarantee that the data’s integrity and resiliency are being preserved and that duplicate data is being removed.

Conclusion

Each company has its own method of analyzing, processing, and categorizing data. This method is dependent on the company’s particular business model, the software tools it employs, and the individual data management methods it employs. However, the goals associated with the management of the data lifecycle have not changed, and each business should make it a priority to protect the confidentiality, integrity, and availability of their data. In order to make the process of managing the data lifecycle more effective and safe, data protection and goals should be required to be handled at each stage of the process.