5 Data Lifecycle Phases – A Comprehensive Guide to DLM

Data is growing into a key asset for companies, driving innovation, enhancing decision-making, and shaping the future. But how does one manage this important resource effectively? This is where Data lifecycle management (DLM comes into play. It is an effective method of managing data throughout its lifecycle. During this management, it undergoes a few data lifecycle phases.

You will learn more about data management phases in this blog post. We will also discuss the advantages and the tools and technologies required for a successful implementation.

What is Data Lifecycle Management?

Data Lifecycle Management (DLM) is a process for managing data from the time of data entry to the point of data deletion. Data is divided into various data lifecycle phases depending on some criteria. Thus, as it completes various tasks or satisfies particular needs, it advances through these stages. A successful DLM process gives a business’s data structure and organization. It, therefore, supports important process goals including data security and availability.

Thus, the goal of DLM is to ensure that the right users have access to the right data at the right time.

Top 5 Phases of Data Life Cycle

If you work with data, it is essential to ensure data integrity at every stage of the data lifecycle. So, below are the five data lifecycle phases, along with the prerequisites for each level:

1. Data Creation

The first stage in the data life cycle stages involves collecting and creating data. The data collection can be in various forms, including images, files, or documents. Here are some ways that a company may gather data:

• Data entry: Businesses manually enter data into management systems in this method. It can be by typing it into a piece of paper or entering it into a graph or chart.
• Data capture: Businesses get data from a range of information systems placed throughout the company.
• Data acquisition: Companies acquire previously collected data from another organization.

2. Data storage and maintenance

Processing, combining, aggregation, classification, and selection of data from data storage and maintenance. It also ensures the data’s accuracy and comprehensiveness. This phase is one of the most crucial data lifecycle phases. Additionally, the way that data is formatted can vary depending on the kind of data storage an organization employs. Relational databases are often useful for structured data. Whereas, NoSQL or non-relational databases are useful for unstructured data.

The most popular method for data storing is to use a database or a Relational Database Management System (RDBMS). An RDBMS regularly retrieves data from data sources and stores and/or deletes data at set times. Thus, an RDBMS serves to securely handle and store data. Moreover, it is compatible with the majority of computer languages for query and data manipulation activities.

3. Data analysis and sharing

Data will become accessible to business users during this stage. Here, organizations may define who can use the data and for what purposes by using DLM. Once the data is available to the public, the concerned person can analyze it. The analysis stage of the data life cycle is also crucial. In this stage, analysts and data scientists employ tools and strategies for analyses.

Furthermore, data use is not always limited to internal purposes. The data is also useful to outside service providers for marketing analytics and advertising. Daily corporate activities and processes, such as dashboards and presentations, are examples of internal uses.

4. Archival

This phase of the data lifecycle phases involves copying the data into an information system. Therefore, businesses can store and access the data in the future. Companies can download older data and keep track of out-of-date data by archiving it. Additionally, companies mark their data as active, indicating that they are currently using and updating the information. If data is outdated, it is removed from all current contexts.

Furthermore, when businesses archive data, they cannot make changes to it. However, they can restore archived data to an environment where it was previously active if they need to retrieve it.

5. Data Deletion and Archiving

Data preservation and accessibility are made possible through data archiving, but this phase of data life cycle is often ignored. Organizations should adopt effective data deletion policies and action plans. These actions are essential for efficient data management. They also include the elimination of duplicate or redundant data. Businesses can manage data deletion and archiving properly to enable:

• Optimize the use and storage of their data.
• Ensure that all data retention laws are followed.
• Avoid possible legal or financial consequences.

Advantages of Following Data Lifecycle Phases

An effective DLM strategy that follows all the phases benefits in many ways:

1. In business, faster data access is essential as it helps firms make better choices. Moreover, it boosts production, efficiency, and customer happiness.
2. There are many useful outcomes of controlled data governance. They include better data quality and lower costs for data management. Additionally, it provides greater access to data for all stakeholders. Thus going through all the data lifecycle phases is very beneficial.
3. Deploying a DLM system can assist organizations in achieving compliance with regulations. Additionally, setting enforceable governance policies protects the data value. It also offers a systematic approach to data management.
4. A DLM strategy allows IT teams to create guidelines and practices to ensure all metadata is consistently tagged. Thus, the availability of accurate data improves the speed and effectiveness of business operations.
5. Businesses may use various cost-saving strategies, including data backup, replication, and archiving. Companies can also transfer the data when it is no longer useful for production situations. For instance, a company can transfer it to less expensive on-site, cloud, or network-attached storage.

Conclusion

Businesses can plan for the severe consequences in the event of data breaches, data loss, or system failure via DLM. Following the 5 data lifecycle phases may prevent some of the tragic effects on an organization. It also improves overall reputation by having an effective data recovery plan in place in the case of a crisis. Thus, all 5 DLM phases that we have discussed in the blog have many direct and indirect benefits.

Frequently Asked Questions

About The Author:

The IoT Academy

The IoT Academy as a reputed ed-tech training institute is imparting online / Offline training in emerging technologies such as Data Science, Machine Learning, IoT, Deep Learning, and more. We believe in making revolutionary attempt in changing the course of making online education accessible and dynamic.

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