What Are The Three Main Goals Of Data Lifecycle Management

With the world filled and run by data today, information technology is no surprise that the data lifecycle – the journey of information right from creation to deletion and everything in between – has increased significantly.

A piece of information that would be used and discarded previously is at present stored for years on end, since everything today is linked, and every single part of data is a slice of a puzzle to class a big motion-picture show.

Thus, as the data increases, it is only imperative to be able to manage it properly, and hence, the need for data lifecycle management.

Data lifecycle management is the process of managing the catamenia of the data in an information system in its lifecycle. The need for data lifecycle management varies among industries simply information technology has 3 main goals to fulfill.

3 Main Goals of Data Lifecycle Direction
1. Data Security/Confidentiality
2. Availability
3. Integrity
Data Lifecycle Diagram
1. Data Capture
2. Data Maintenance
3. Information Usage
4. Information Publication/Share
5. Information Archival
6. Data Deletion

Three Main Goals of Information Lifecycle Direction

Managing data is a tough chore, and in that location are sure goals that need to be considered in data lifecycle management.

These goals are the foundation by which there is an unhindered and streamlined flow of information. The three goals are as follows:

Data Security/Confidentiality

With the massive volume of data out there and in utilise, the risk of data being misused is at an all-time high.

And with information beingness the new currency in this digital world, its security is extremely crucial to any organization and individual.

It is thus necessary to ensure data security, i.e., protecting the data from existence accessed past third-party unauthorized users, as well equally protecting the data against whatsoever malware or beingness corrupted.

Availability

Data being the driving force in the digital era, it is only fair that the data is made bachelor whenever needed.

If the data is not available when needed, it results in cascading failures of multiple processes that are dependent on the data from the previous process.

Hence data availability holds a very high priority in processing and thus is a major goal in information lifecycle management.

Integrity

As the recorded data is used in 24-hour interval-to-day operations, it is field of study to multiple edits and revisions for every instance of information technology beingness in use.

Moreover, with the growing popularity and implementation of data-centric technologies such every bit cloud computing, IoT, etc. computing in multi-user environments has increased.

So, there are multiple instances of the information created and in employ as multiple users access the same database. This could lead to differences in versions every bit seen and saved by the various users.

Thus, it is necessary for a DLM to exist able to maintain data integrity, i.eastward., at all times, the same data should exist visible to all the users, and any iteration to the information must be judiciously reflected in all instances.

Data Lifecycle Diagram

Data Lifecycle

As seen from the to a higher place diagram, at that place are half-dozen stages in the data lifecycle, ranging from creation to deletion. Let's go through each step in cursory:

Data Capture

Data could either be acquired from an external source or could also be from the reception of signals from various systems such as in the instance of IoT systems or automobile learning systems among others.

Data Maintenance

Data maintenance implies the processing of data to make it usable without deriving any value from it for the enterprise.

Data Usage

At this phase, the data is used for various activities such every bit decision-making or assay.

Data Publication/Share

Data is shared with diverse users who take authorized access to it. Data publication could refer to the sharing of data exterior the enterprise.

Data Archival

The unused or unnecessary data is saved only in case it is required in any agile surround in instance of any direct or indirect dependencies.

Data Deletion

Once information technology is established that there are no dependencies, whether direct or indirect, upon any other data in any active environs, the data can and so exist deleted. Ideally, the data is deleted from an archive.

Conclusion

Data has evolved to be the new currency in the digital era that we live in. Hence managing data has become an essential aspect in terms of information handling, giving rise to the concept of data lifecycle management.

All organizations large and small-scale manage their ain information, and some opt for services when storing data on the cloud. But the large moving picture is, no 1 can escape it, lest it is unsustainable in this highly competitive earth.

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