Understanding data lifecycle management (DLM): What is data lifecycle management, and what are its stages?
One of the biggest challenges that many industries face is adequate management of information. If you are also the one who is finding it challenging to manage data information, then this article is for you. In this article, we will discuss the Data Lifecycle Management and the main goals of Data Lifecycle Management. DLM allows managing the flow of data from the first contact point to the last.
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An Overview of Data Lifecycle Management (DLM)
The word Data Lifecycle Management itself is self-explanatory. It refers to the thorough structuring of all the steps that the information follows within the company. In data management, different resources are required for automatic processing. By using these resources, data tracking, collection, and storage becomes possible.
What Are The Benefits of Data Lifecycle Management?
When companies implement Data Lifecycle Management and handle the information correctly, they enjoy several benefits, such as:
- It ensures a good data infrastructure in place and improves data safety in case of emergency or risk.
- Each industry sector applies different data storage requirements, so DLM allows you to meet these requirements.
- When information is maintained throughout the data cycle, it ensures that always updated information is available.
- DLM ensures the availability of clean, accurate, and useful data is available to users. Thus it improves the overall efficiency and agility of the company’s processes.
Stages of Data Lifecycle Management
You can understand the importance of the implication of Data Lifecycle Management for a company by knowing about all the stages of DLM. We will discuss all the phases one by one.
Data Collection
The data life cycle starts with the collection of information. In this process, data values are created that are important for the company’s operations. Data can be captured in three ways, such as:
- Data Acquisition: In this step, existing data is used that is produced by the organizations from outside the company.
- Data Entry: In this step, new data values for a company are created by either humans or electronic devices.
- Signal Receipt: In this step, the device makes data retrieval, which is important in the control system. When the Internet of Things (IoT) is involved, it becomes more important for information systems. Companies might use other data gathering methods as well, but these three ways play a crucial role in data collection.
Data Maintenance
After collecting data in the first stage, it becomes essential to keep that data clean. When the data is kept clean, it allows business processes to run effectively. After data collection, you do data maintenance, and in this phase, data is supplied to the points where data synthesis and data usage occurs. This step is all about processing the data without collecting any useful value from it for the company. Data maintenance involves movement, cleansing, integration, and enrichment of data.
Data Synthesis
This Data Lifecycle Management stage doesn’t process all the gathered information. In fact, it is important to get valuable data through inductive reasoning. Such analysis is also used in accounting and risk modeling. This analytic arena is used for making investment decisions. Deductive logic isn’t the part of data maintenance. A perfect example of deductive logic is:
Net Sales = Gross Sales (Minus –) Taxes. When you know the taxes and gross sales, you can easily calculate the net sales.
Data Usage
During this data life cycle stage, data is processed and characterized as a part of company administration. Remember, this information is critical and even part of the business model of many companies. We must do adequate data management in this phase because some limitations and restrictions are applied to this information. This phase has different challenges, such as is it legal to use the business data in the way people are using it?
Data Publication
The next stage of Data Lifecycle Management is to know how to use this information outside the business environment. Data publications mean sending data to outside locations means outside the company.
For example, a user share monthly report to client, when the data is sent outside the company, it’s not easy to remember it. Moreover, companies can’t correct the incorrect values because these values are beyond their reach. The biggest problem with data publication is data breaches. Here data governance becomes handy to tell how much incorrect data has been sent from the company.
Data Storage
Data collection was the first step of the Data Lifecycle, and data storage is the last step. In this step, no further processing is performed, and just data is stored. During this stage, data waits for either recovery or removal from the production environment. Companies need to know that data is copying to an active production environment. Data archives are the places used for data storage, but no data maintenance, publication, and usage occur.
Data Cleaning
Once the data is no longer important for the company, and can’t be used, it should be deleted. In order to ensure good data management, data cleaning must be carried out properly. Data Lifecycle Management is crucial, and companies need to perform several actions every day.
Main Goals of Data Lifecycle Management
Today everything is run by data, and data lifecycle from creation to deletion is carried out in Data Lifecycle Management. In the past, a piece of information was discarded, but now it is stored for years. As the data increases, the need for proper data management has also been increased. The main goals of Data Lifecycle Management are:
- Data Security
- Data Availability
- Data Integrity
Data management isn’t an easy task, and you have to consider the main goals of Data Lifecycle Management.
See also: Getting started with Data Privacy
Data Security
When there is massive data in use, there is always a risk that data will be misused. Data security is extremely important. Data security ensures that third-party unauthorized users don’t access data. Moreover, it involves data protection against malware.
Data Availability
In this digital era, data is the driving force, so the data should be available when needed. If the data isn’t available by the time it is needed, it can result in the failure of many processes that are dependent on the data. Thus data availability is the main goal of Data Lifecycle Management.
Data Integrity
The recorded data will be in use again and again, especially in day-to-day operations. There will be multiple changes and revisions whenever it is in use. Moreover, with the growing popularity of cloud computing and the Internet of Things, data integrity has become essential. In a company, many users will use the data, and they will make changes as well. Data Lifecycle Management needs to ensure data integrity. For example, same information must be visible to all users.
Conclusion
As the technologies and data are growing quickly, data management has become difficult. Data is processed through different stages and then stored. The main goals of Data Lifecycle Management are data security, integrity, and availability. Moreover, companies should implement the Data Lifecycle Management system to protect and store the data. In this digital era, data has become crucial. By using DLM, there are risks of data breaches as well, so companies must take all these factors into account.
