Data Lifecycle Management: Optimizing Data from Creation to Disposal

Last Updated on 2025-02-20

What is data lifecycle management? Learn how to manage data efficiently to maximize its benefits for your business.

In the ever-developing world of tech, there are now several data-driven business solutions to boost profit and productivity. From cloud infrastructures to artificial intelligence, all these innovations require a deep understanding of data. By optimizing data, you can better maximize the full benefits of these technologies to improve your system.

Additionally, a well-built data structure can better protect your system from cybersecurity attacks. In order to reduce risks, you must first implement a cohesive process for managing data. Let’s start by understanding the concept of data lifecycle management.

What is Data Lifecycle Management?

Data lifecycle management (DLM) is a management method that streamlines the flow of a system’s data throughout its lifecycle. The process begins from the intake of data until its deletion. DLM products can automate these procedures. They frequently split data into tiers in accordance with predetermined policies. 

Additionally, based on such standards, DLM tools automate data migration from one tier to another. Newer data and frequently accessed information are typically kept on quicker storage media that costs more. Whereas, less important data is typically stored on slower, less expensive storage media.

Benefits of Data Lifecycle Management

The following are some of the significant advantages of data lifecycle management:

1. Process enhancement

Data is essential for guiding an organization’s strategic initiatives. By maintaining data quality throughout its lifecycle, DLM improves processes and boosts productivity. A sound DLM strategy ensures that users have access to accurate and trustworthy data, allowing organizations to get the most out of their data pool.

2. Cost management

A DLM process takes care of data at every stage of its lifespan. Organizations can employ a variety of options, including data backup, replication, and archiving, to cut expenses after data is no longer useful for production environments. It could be transferred, for instance, to less expensive storage that is on-site, in the cloud, or in network-attached storage.

3. Data usability

By implementing a DLM approach, IT teams can establish guidelines and rules that uniformly mark all information which enhances accessibility when required. To ensure data value, set enforceable governance policies. The availability of accurate and valuable data improves the speed and effectiveness of business operations.

Advanced data discovery tools play a crucial role in uncovering hidden insights within the available data, further enhancing the speed and effectiveness of business operations.

4. Legal compliance

Every industry sector has its own standards and requirements for data retention, and a strong DLM strategy aids companies in maintaining compliance. While retaining compliance with data privacy rules involving personal data and organizational records, DLM enables enterprises to handle data with enhanced efficiency and security.

DLM Phases

A data lifecycle consists of several phases. Each stage is controlled by a set of rules that optimize the value of the data at every point in its lifecycle. With more data incorporation into company workstreams, DLM becomes more crucial.

Phase 1: Creation

The first step in a new data lifecycle is data gathering. However, there are many sources of data. They might be anything from web and mobile applications, IoT gadgets, questionnaires, surveys, and more. However, collecting all accessible data is not necessary for the success of your organization. It’s always wise to consider the quality and relevance of new data to your organization before incorporating it.

Phase 2: Storage

Data might be formatted differently, which can have an impact on the kind of data storage an organization utilizes. Relational databases are often used by structured data, whereas unstructured data typically uses NoSQL or non-relational databases.

Once the dataset’s storage type has been determined, the infrastructure may be examined for security holes. The data can go through various sorts of data processing, including data encryption and data transformation. This way, you can protect the company from external attacks. This kind of data munging also ensures that sensitive data complies with the legal and privacy standards for governmental policies like the GDPR, enabling firms to avoid any exorbitant fines from these kinds of legislation.

In addition, emphasizing data redundancy is another facet of data protection. When data is lost or corrupted, a copy of the data can serve as a backup, preventing both unintentional changes to the data and more intentional ones, such as virus attacks.

Phase 3: Sharing

Business users can now access the data throughout this phase. DLM gives businesses the ability to specify who can use the data and for what purposes.

After the data has been made available, it can be used for a variety of investigations, from straightforward exploratory data analysis and data visualization to more complex data mining and machine learning methods.

Each of these techniques contributes to the communication of corporate decisions to diverse stakeholders. Daily corporate activities and processes, such as dashboards and presentations, are examples of internal uses.

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. 

Phase 4: Archiving

Data loses its value for routine tasks after a while. For the sake of prospective litigation and inquiry, it is crucial to save copies of the organization’s data that are not regularly accessed. Then, if necessary, archived data can be restored to a live production environment.

The DLM strategy of an organization should specify when, where, and how long data should be archived. In this phase, data goes through an archival procedure to ensure redundancy.

Phase 5: Deletion

Data is securely deleted from the records at this point of the lifecycle. Businesses will erase data that is no longer needed to free up additional storage for data that is still active. When data surpasses the required retention duration or is no longer useful to the business, it is deleted from archives during this phase.

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