What Is Data Lifecycle Management? - Proche
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Every organization, especially those who directly connect and work with the data, must know the exact answer to the question “What is data lifecycle management?”. Otherwise, without having complete knowledge about it along with the three main goals of data lifecycle management, it will be tough on their behalf to manage the data properly.
The data lifecycle management (DLM) ensures the data’s integrity and reliability at every step of its life cycle. With the help of it, the companies don’t need to worry anymore regarding the data. All these are controlled and taken care of with utmost priority by the data lifecycle management.
And for successful implementation, it uses 5 phases consisting of various interpretations at each step. You will find a detailed explanation of it in the latter part of the article. Continue reading to know in detail.
Data lifecycle management refers to the process used to control the flow of the data in the business.Data passes through various databases, application systems, and storage mediums throughout its entire lifecycle, starting right from its creation to complete destruction.
Moreover, it ensures that the data is safe, protected, clean to use, and can be accessed whenever needed. As a result, it increases the effectiveness, competence, and agility of an organization’s process.
Phases Of The Data Lifecycle Management (DLM)
Every data’s lifecycle consists of 5 phases comprising data creation, storage, use, archival, and its destruction. Now, let’s look at the 5 phases of the data life cycle management to get a clear concept regarding its lifecycle from scratch to the end:
It is the 1st phase of the data lifecycle management as it is directly related to the initial creation, collection, or capture of the data. As a result, the necessary data are only taken into the record, and the irrelevant one to the company is filtered out. It is one of the valuable characteristics of this phase as a lot of time is saved from being wasted in arranging these data.
In this phase, at first, the data needs to enter through the organization’s firewall for the data retrieval process. Data retrieval means creating data that is not produced before, and anything similar like thisdoesn’t exist in the organization.
The created or incoming data can be of any format such as Word file, PDF, picture, Structured Query Language (SQL) database data. An organization creates the data following any 1 of the below 3 vital methods:
#1 Data Acquirement
It is the obtaining or use of the pre-existing data that was previously created outside the company. In most cases, contracts are included here so that the organization can control the use of the obtained data.
#2 Data Entrance
It is the manual pass of the new data item that is created within the organization.
#3 Data Capture
It is the capture of the data that is produced by the machines used in varied processes within the business.
Once the data is created, the next thing that needs to be done is protecting and storing it in a secure place with the utmost security level. The data that are stored are the data that the user and the organization need to use daily. Also, the data is being shared constantly on various platforms, and the more the data gets shared, the higher the risk gets.
That’s why the data should be stored in both the live databases and in the backup. In fact, storage of the data is very much crucial as a crash can happen at any time in a business system.
For this reason, a strong and fast backup and recovery technique must be applied and used to make sure that there is no loss of data throughout its lifecycle. Hence it is always a better way to be prepared beforehand, so the damage doesn’t affect that much.
At this phase of the data lifecycle management, the data is used to support all business activities. That is, the data can be viewed, managed, processed, altered, and stored based on your requirement.
A review or check trail can be maintained for the crucial data and information related to the organization. So all the changes made to those vital data are understandable and can be traced easily. Additionally, the data can be made available for access and use by others outside the business via data sharing.
Data archival is somehow similar to the data storage phase. It is basically the location where the data are safely stored except that no type of data maintenance or use can be done as like the storage.
If required, it can even be recovered or moved to an environment where the use of this data is needed and can be used anytime. In other words, you can copy all types of data in a safe place, and when needed, you can remove it anytime you want. It acts as another backup system that is archived, protected, and available for use.
It is the last and final phase of data lifecycle management (DLM), where the data is totally removed from the entire system. It is impossible to store all the data from the start to the end as the data continuously changes.
Moreover, if you want, it will cost a lot to maintain and store those data; you will require huge storage space. That’s why companies tend to delete those data instantly that are no longer needed by them to free up space and the expense that comes with it.
Hence, before deleting all the data, it is essential to make sure that it is of no use. Otherwise, you may face a problem later, as after destroying the data, there is no chance of recovering it (data is destroyed from both storage and archival).
Therefore, these are the 5 phases of data lifecycle management (DLM).
Hopefully, you now have got a clear concept about what is data lifecycle management. The world we live in now is highly competitive. In fact, it is challenging even to survive if you don’t know how to handle and manage your data properly. As nowadays, the data is equivalent to money and thus often known as modern money. It is where data lifecycle management can play a crucial role and help an organization maintain the flow of data successfully without any kind of flaws. It is undoubtedly a blessing for the companies in handling and dealing with the entire data flow right from the start to its end.