What is Data Lifecycle Management (DLM)? Why is it Important?

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Written By Haisam Abdel Malak

About: Haissam is a digital software product manager with 15 years of expertise in developing enterprise content management solutions. His core capabilities encompass digital transformation, document management, records management, business process automation, and collaboration.

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Unlock the full potential of your data with seamless Data Lifecycle Management! From creation to archiving, ensure the accuracy, security, and accessibility of your valuable information with a streamlined process. Embrace the power of data and watch your organization soar!

Data Lifecycle Management is the process of overseeing the entire journey of data, from its creation to its eventual disposal. It involves managing the storage, accessibility, and security of data to ensure it remains accurate, relevant and valuable throughout its lifecycle. DLM ensures efficient use of data resources and reduces the risk of data loss.

Data lifecycle management (DLM) has been around for many years now, but it has recently become a hot topic due to the growth in digitalization. Organizations are expected to handle more and more data every day which means they need a system in place to make sure that this data is being stored securely and efficiently.

The purpose data lifecycle management is to ensure that organizations are able to make full use of their data while ensuring it remains secure at all times.

Data lifecycle management

What is a data life cycle management?

A data life cycle is a process that starts with the collection of data and ends with the deletion of data. Data life cycles are important because they can help organizations comply with regulations, as well as to ensure that they are not wasting valuable resources storing old data.

Organizations should start by identifying what kind of data they want to collect. There are four types of data: structured, semi-structured, unstructured and personal identifiable information (PII).

Data management can be a challenging task and controlling the entire data lifecycle is regarded as one of the best practices to implement.

What are the 5 stages of data life cycle?

It is important to understand the various stages in the data lifecycle management process and how each stage affects the next. Using data management tools, organizations can make sure that this complete process is being followed properly.

The data lifecycle management (DLM) has five main phases including creation or acquisition, storage and maintenance, usage, disposition, and archival. Each phase plays a critical role helping businesses efficiently manage their data and deliver it to the right audience at the right time.

The stages of data lifecycle management are:

1- Creation

The creation stage of data lifecycle management is when the first stage of data processing starts. This includes extracting data from different sources and creating a database or repository for storing it. Data can be collected in many different ways, such as manually or by using a computer program to collect it automatically.

Data preservation is an important part of the data creation stage. It ensures that the original form of the raw data can be restored in case it’s necessary.

Data processing is another part of this stage, which includes cleaning up and formatting raw data so that it can be easily analyzed by machine learning algorithms.

Data analysis takes place during this phase as well and involves identifying trends in order to make better decisions about how to use your resources in the future.

2- Storage

Data storage is one of the most important stages of data lifecycle management. It includes storing the data on a medium that has a longer lifespan than the data itself. The medium could be tapes, hard drives, or even CDs and DVDs.

The storage stage is crucial for many reasons. One of them is that it provides protection against any kind of disaster, like natural disasters or human errors. If you have your data in multiple copies in different locations, it will be easier to recover from a disaster situation.

Always remember, a good DLM strategy prioritizes data protection and disaster recovery.

3- Usage

The usage/processing stage is the third stage in the DLM. The goal of this stage is to prepare data for use by other applications or people. This includes filtering, reformatting and aggregating the data into a form that can be easily consumed by an application or person.

The main objective of this stage is to make it easier for other applications or people to consume the data in a format that they can understand and use.

4- Disposition

The disposition stage is the fourth step in the data lifecycle management process. Data can either be archived or disposed at this point depending on what it’s being used for. If it’s no longer needed or if it needs to be stored elsewhere then it should be archived at a later stage. However , if it’s still being used and is needed in the near future then it can be retained.

At a higher level, data must also be kept confidential, protected, and secure at all times. This is key in both business and personal settings.

5- Archival

The archival stage of DLM is the last stage in which data is backed up and stored for future use.

It is an important part of the DLM process because it ensures that the data collected from previous stages will be available for future use.

The archiving stage can take many forms such as backup tapes, external hard drives, or even printed material.

Why is data lifecycle management important?

The main benefits of data lifecycle management (DLM) are risk reduction, cost reduction associated with data challenges, security improvement and reduction of data breaches, compliance with various regulations, operational performance improvement, accessibility improvement, and increased agility.

Let’s check them in details

1- Reduced risks

Reducing data risks is considered among the top DLM benefits and an essential part of any DLM strategy.

When data is properly managed, your organization should be able to comply with various rules and regulations, lowering the risk of fines and penalties that could cost your company a fortune.

Furthermore, by preserving and serving only high-quality data to employees and managers, you improve the decision-making process, lowering the risks of making uninformed business decisions.

Reducing risks should be part of an overall data risk management plan

2- Cost savings

Quick data retrieval, backups, and archival are among the top data lifecycle management benefits as it can help your organization save money that it can use to improve services or introduce new products.

Did you know that 30 percent of employees’ time is spent looking for data? Employees will be able to use saved searching for more important tasks if the proper data management strategy is implemented.

DLM can help you reduce storage costs by moving data to less expensive storage options as it ages.

3- Improved security

By understanding where your data is and how it is being used, you can better protect it from unauthorized access and misuse. By understanding where your data is and how it is being used, you can better protect it from unauthorized access and misuse.

A DLM strategy should ensure that sensitive data is secure and only authorized access are given.

4- More effective governance

DLM may add managerial consistency and controls that benefit the organization. It can provide the extra benefit of enhanced data management vs data governance for the entire organization.

It also can help you better comply with regulations by ensuring that data is properly managed throughout its lifecycle.

5- Improved performance

DLM can help you improve system performance by moving data to faster storage options as it becomes more active.

6- Increased agility

Increasing agility is considered one of the top data lifecycle management benefits as it can help you respond more quickly to changing business needs by making it easier to access and use the right data at the right time.

What are three main goals of data lifecycle management?

The three main goals of data lifecycle management (DLM) are to ensure and enhance data accuracy and integrity, make data available to the right audience at the right time, and make sure that data is managed and stored effectively throughout its entire lifecycle.

Who is responsible for data lifecycle management?

The responsibility for data lifecycle management depends on the type of data. If it is structured, then the responsibility falls on the IT department. But if the data is unstructured, then it falls on the business unit that generates that type of information.

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