From its creation to its disposition, data undergoes a series of stages that require careful management and supervision. This is where data lifecycle management (DLM) steps in which acts as an overall framework that control the full lifespan of data. DLM is a crucial part of any data management strategy that should be clearly defined within organizations.
Data lifecycle management (DLM) 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.
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 strategy 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. Coupled with implementing best practices will help businesses overcome the related challenges.
What are the 3 main goals of data lifecycle management?
The three main goals of data lifecycle management are ensuring data availability, maintaining data integrity and security, and optimizing data storage and utilization. By effectively managing the entire data lifecycle, organizations can maximize the value of their data, mitigate risks, comply with regulations, and make informed decisions based on reliable information.
Let’s discuss these goals in detail
1- Ensuring data availability
There is no point to store and maintain high volume of data if it is not available when needed. Businesses spend time, resources, and money to make sure that data is always available whenever any employee need to gain insights and make informed business decision.
2- Maintaining data security
With the advancement of technology, data breaches are increasing year by year and it is becoming extremely challenging for organizations to reduce sensitive data being exposed to the public that will have negative impact on their customers trust and brand recognition.
Applying DLM will help organization provide a high secure environment where data can be shared and used without risking its exposure.
3- Optimizing data storage
Storing huge amounts of data can cost organizations tons of money from hardware to software and maintenance. That is why it is critical to store only high quality needed data and start migrating to the cloud to benefit from the flexibility and scalability provided.
What are the 5 stages of data life cycle management?
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 5 stages of data lifecycle management are:
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.
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.
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.
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.
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.