What is Data Lifecycle Management? and What phases would it pass through?

Have you ever found it difficult to determine and manage data information? One of the main challenges faced by companies is managing information adequately.

This time I will discuss the solution to your problem in managing data information, using Data lifecycle management.

Well, Data Lifecycle Management (DLM) itself allows you to manage the flow of data throughout the process it experiences from the first contact point to the last. Interested in knowing that? Check out this information.

What do you think when you hear that word? or you might have a view about DLM?

Data Lifecycle Management itself refers to a definition and through structuring the steps that are followed by information within the company to maximize its useful life.

So in this data management, Will require the use of resources that have been offered by information technology for automatic processing. Through them, it is possible to collect data for analysis and trace it to the point of storage or cleaning.

What do you get when you implement DLM? What you need to know is that when a company implements data lifecycle management and ensures good handling of the information they produce in their daily operations they have several advantages, which include:

For you to truly understand what the implications of the application of data lifecycle management are for a company, it is necessary to know every phase that the data goes through during its lifetime.

The life cycle of data starts with information gathering. This allows the creation of values ​​that do not yet exist, but which are needed as part of the company’s operations.

In this case, the first experience required by a data item is to pass inside the company’s firewall. This shows Data Retrieval, which can be defined as an action to create data value that does not yet exist and does not exist in the company.

There are three main ways that data can be captured, and this is very important:

There may be other ways, but the three that have been identified above have a more significant Data Governance challenge. For example, in data acquisition, we often involve contracts that can regulate how companies are allowed to use the data they obtain in this way.

When you get data based on the first phase, then make sure it has to be kept clean. In other words, to process it so that business processes can run effectively.

After taking the data earlier, then you will usually find Data Maintenance. This can be defined as the provision of data to the points where Data Synthesis and Data Usage occur, ideally in the form most suitable for this purpose.

We will soon discuss Data Synthesis and Data Usage. Data Maintenance is processing data without obtaining any value from it for the company.

This often involves tasks such as movement, integration, cleansing, enrichment, retrieval of changed data, as well as known extract-transform-load processes.

This data life cycle phase is not common to all information that is processed but is important in cases where it is necessary to make valuable data through inductive reasoning. This type of analysis is also used in risk modeling, accounting, and investment decisions.

This is an analytic arena that uses modeling, as found in risk modeling, actuarial modeling, and modeling for investment decisions.

Decrease according to deductive logic is not part of this — what happens in Data Maintenance. An example of deductive logic is Net Sales = Gross Sales — Taxes. If I know Gross Sales and Taxes, and I know the simple equation just outlined, then I can calculate Net Sales.

In the life cycle phase, this data is characterized by the application of data collected and processed as part of company administration. Keep in mind that in many companies this information is even part of their business model.

Also, we must demonstrate that adequate management data in this phase implies knowing the potential usage restrictions that may apply to this information.

Data use has special Data Governance challenges. One of them is whether it is legal to use data the way business people want it to. This is referred to as “permitted data usage”.

What you need to know, the use of information can also be done outside the business environment itself. In other words, when used maybe your single data value can be sent outside the company. This can be defined as sending data to locations outside the company.

An example is when someone sends monthly reports to his clients. After the data is sent outside the company, it is impossible to remember it. Incorrect data values ​​cannot be corrected because they are beyond the company’s reach.

In this case, data governance may need to help in deciding how incorrect data that has been sent from the company will be handled. Unfortunately, data breaches are also included in Data Publication.

Storage is the first step taken at the beginning of the end of the data life cycle.

In this phase, data is stored without further processing. So in this phase, the data waits for its removal from the active production environment or its recovery, if necessary.

What you need to know if the Data archive is copying data to an environment where it is stored if needed again in the active production environment, and deleting this data from all active production environments.

So, data archives are just places where data is stored, but where no maintenance, use, or publication takes place.

Once the data is no longer useful in any way for the company, the data should be deleted. This process must be carried out properly to ensure good data management.

The importance of good data lifecycle management and following all phases of the data life cycle is crucial for a large number of actions taken by companies every day.

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