Data is a critical component of any business, but it is often difficult to maintain. It can be lost, corrupted, or unavailable when needed. A solid document lifecycle management strategy is the only way to ensure that data is properly managed within organizations.
Data lifecycle management can be defined as the process of managing, protecting and preserving data through all stages of its life cycle. This includes the collection, storage, analysis, use and disposal of data.
Data lifecycle management 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.
What is a data life cycle?
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).
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.
The data lifecycle management 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:
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.
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 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.
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.
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 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.
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.
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.