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When you realize how much data organizations collect everyday, from pictures to videos to text files to emails, it can be hard to imagine having access to all the information when needed. However, with data lifecycle management, managing data throughout its entire life cycle has become easier.
In essence, data lifecycle management is responsible for keeping your data secure, storing and retaining it only as long as it has value, and then destroying it securely when that retention period has ended.
In this article, we’ll discuss what data lifecycle management is, why it’s important, and how data lifecycle management can positively impact your business’s bottom line.
Conducting exploratory research seems tricky but an effective guide can help.
Data Lifecycle Management refers to the system of processes, policies, and procedures to ensure that all data is managed effectively throughout its entire lifecycle, from acquisition to destruction. In other words, it is a process that helps an organization manage its data from creation through deletion.
The overall goal of data lifecycle management is to avoid legal and ethical issues that could potentially arise when data isn’t properly managed and to assist enterprises in providing end-users with the healthy data they require to make informed decisions.
Organizations are now dealing with more data than ever before. Managing this information has become a critical job for any enterprise. Data moves in many different ways across an organization, whether it’s from one department to another, or back and forth with a client. In order to keep track of all of these different data paths, most companies need the data lifecycle approach. This approach helps to ensure that the data being used is the most up-to-date and accurate version of the data while maintaining its security.
Data is the lifeline of business, and implementing an effective data lifecycle management strategy has become important in this data-driven world. Businesses can reap numerous benefits from an effective data lifecycle management approach, including:
Although not necessarily linear, there are five stages of data management. These are data collection, storage, processing, analysis, and dissemination.
The first stage of data management occurs when an organization collects data from various sources. It may be through manual or automated means, but in either case, it’s important to understand where the data comes from and how it can be affected by factors such as time, location, and technology. A business must create a number of guidelines for collecting data in standardized formats so that it can be accessed and managed later.
Once organizations have collected the data, they need to store it somewhere. In most cases, organizations will choose one of two options: on-premises storage or cloud-based storage. When data is collected, it becomes a powerful asset to the company, and it can be used as needed, or it can be altered, deleted, or archived depending on its intended use. At this stage, an organization should implement policies for storing it securely.
After data has been stored, it must be processed before being used for analysis purposes. Processing involves cleansing, formatting, integrating data from multiple sources, validating data, and transforming data into a more usable format.
The analysis allows users to gain insights into their data and make decisions based on those insights. When analyzing data, it’s important to use proper statistical methods and techniques.
Dissemination refers to taking any insights gained from analyzing data and presenting them in a way that allows others to learn from them. At this stage, data has its full business value.
With data lifecycle management, you can be sure that your organization will always have access to its most critical data. Organizations will also be able to make better business decisions since all the data will be easily accessible in one place. Whether you’re an enterprise or a small business, data lifecycle management will help you meet compliance standards and protect against unnecessary exposure of sensitive data to unauthorized users.