The data lifecycle is the sequence of stages. It is the process by which a specific piece of data moves from being created or captured to being archived and/or deleted at the end of its useful life. The data lifecycle is referred to, as a “cycle” because each data project typically benefits from the lessons and insights gained from the previous one. The last phase of the process feeds back into the first in this manner.
The best practices from the several stages of the data lifecycle are Data Production, Data Cleansing, Data Management, Data Protection, and Data Governance which make up the process of Data Lifecycle Management (hereinafter referred to as “DLM”). At each stage of the data lifecycle, it specifies how the data is collected, prepared, transmitted, managed, analyzed, and governed.
DATA LIFE CYCLE MANAGEMENT
DLM is a method for managing data from the point of data entry to the point of data deletion. Data is divided into phases depending on various criteria, and as it completes various tasks or satisfies particular needs, it advances through these stages.
A successful DLM process lists out structuring and organizing the data of an organization, enabling important process goals including data security and availability. These objectives are essential to corporate success and become more significant over time. Because of DLM policies and practices, organizations may prepare for the worst-case scenario of data breaches, data loss, or system failure.
Data Protection and disaster recovery should be given top priority in a DLM strategy, especially given the influx of harmful actors into the market brought on by the increasing growth of data. This will prevent some of the catastrophic consequences on a brand’s bottom line and overall reputation by having an effective data recovery plan in place in the case of a crisis.
STAGES OF DATA LIFE CYCLE
Although there are many ways to interpret the many stages of a typical data lifecycle, they can be summed up as follows:
Data must be generated initially in order for the data life cycle to start.
Even if people are not aware of it, data generation happens all the time, especially in increasingly digitized environment. This information is produced in part by your company, by your clients, and by the third parties that you may or may not be aware of.
Every transaction which includes sales, purchases, hires, communications, and interactions, generates data. When properly analyzed, this data can frequently produce insightful conclusions that help in providing better customer service and performing the job more successfully.
Data is typically created by an organization can be in one of 3 ways:
- Data Acquisition: acquiring the already existing data which has been produced outside the organization;
- Data Entry: manual entry of new data by personnel within the organization; and
- Data Capture: capture of data generated by devices used in various processes in the organization.
Not all of the daily generated data is gathered or utilized. The organization should decide what information needs to be recorded and the most effective way to do so, as well as what information is superfluous or unrelated to the project at hand.
There are many techniques to gather data, including:
- Forms: Some of the most popular methods firms produce data are through web forms, client or customer intake forms, vendor forms, and human resources apps.
- Surveys: Surveys can be a useful tool for collecting a lot of data from a lot of individuals.
- Interviews: Focus groups and interviews with consumers, users, or job candidates provide chances to collect qualitative and subjective data that could be challenging to obtain through other methods.
Organizations secure the data throughout this phase using a variety of backup techniques, ensuring security from lost or stolen data. Information systems frequently come with built-in backup mechanisms, or organizations can manually download the backup software.
Organizations also have a recovery procedure in place in the event that data is lost or stolen, which typically entails downloading and restoring data. Data must be preserved for later use after it has been gathered and processed. The most popular method for accomplishing this is by building databases or datasets.
Then, these datasets may be kept on servers, in the cloud, or on a physical storage device like a hard drive, CD, cassette, etc. In order to ensure that a copy of your data will be safe and accessible, even if the original source is corrupted or compromised, it is crucial to consider redundancy when deciding how to store data for your organization.
Data are used to support organizational operations throughout the consumption phase of the data lifecycle. It is possible to examine process, modify, and save data. To ensure that all data updates are fully traceable, an audit trail should be kept for all critical data. Organizations can specify who can use the data and for what purposes by using DLM. Additionally, data may be made accessible for sharing with others outside the organization.
Once the data is made public, a variety of investigations can be performed on it, ranging from straightforward exploratory data analysis and data visualization to more complex data mining and machine learning methods. All of these techniques are used in the decision-making and stakeholder communication in an organization.
Furthermore, data utilization isn’t always limited to internal purposes. The data might be used, for instance, by outside service providers for marketing analytics and advertising. Daily corporate activities and processes, such as dashboards and presentations, are examples of internal uses.
- Archive and Data Lifecycle Management
One of the most significant tasks in the data lifecycle, data archiving is a vital component of the data management lifecycle. In actuality, testing, cleaning, and archiving of data are lifecycle activities.
Data archiving guarantees that data is safeguarded, with the information being saved and available for future access. When data is archived, business users can regain access to it by going through the Information Technology Department rather than attempting to recover it directly from the various servers.
The requirement for access to archival data has grown increasingly urgent, as firms struggle to derive more value from their data as they expand. One of the most crucial phases of the DLM is that many organizations believe more in the practice of archiving data. In a data disaster, this could result in the loss of important data or only lengthen the time it takes to recover.
- Data – Destruction
The removal of all copies of a data item from an organization is known as data destruction or purging. Usually, it is carried out from a storage facility for archives. During this stage of the lifespan, the hardest part is making sure that the data has been properly removed. It is crucial to confirm that data items have outlived their required regulatory retention time before discarding them.
A vital component of ensuring that Data Governance can be implemented successfully inside any organization is having a properly defined and documented DLM procedure.
The permission to remove data is assigned and implemented throughout the final stage of the DLM. The function of a Data Deletion Committee must be carefully established in order for it to achieve its goal.
The right to erase data is established with data deletion, and the data management must make sure that it is respected. An efficient data deletion policy and an action plan that spells out how to accomplish the goal of data deletion must be put into place as part of a deletion procedure.
Data Lifecycle is the process of creating the data, using and deleting that data. The data lifecycle involves managing of data judiciously, storing data, archive and destructing the data to make space for another new data.
DLM process is the key to better Data Governance in the working of any organization. More and more companies understand how important it is to stay on top of the constantly evolving data management standards, which can be very difficult in today’s fast-paced technological world. The fact that data is frequently disorganized is perhaps one of the biggest problems businesses have with data management.
– Team AMLEGALS assisted by Ms. Bhavika Lohiya (Intern)
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