Data Lifecycle Management

Data Lifecycle Management (DLM) is a strategy in data management that focuses on securing data effectively throughout its entire lifecycle. The process aids in maintaining the privacy and confidentiality of sensitive information and ensures that the data complies with the regulatory requirements.

DLM is a business's approach to employing various methods, procedures, and DLM tools to manage its data. It is a policy-based method for controlling the data flow in an information system from the moment of creation and initial storage to the phase at which it becomes redundant and erased.

• Data Lifecycle Management (DLM) is a technique used in data management to process data securely from the beginning to the end. Its primary objectives are managing the confidentiality, integrity, and availability of information.
• The method ensures that the data conforms with regulatory requirements and helps to preserve the privacy and confidentiality of sensitive information.
• Regulations in many industries demand that organizations preserve their data for a specified period. The business may destroy or remove records that are no longer required for legal, administrative, auditing, or other operational activities once the minimum term for data retention has elapsed. The DLM process assists businesses in this process.

Data lifecycle management is a method of managing data throughout its entire lifespan, spanning from its input to its disposal. In this process, information is divided into segments based on various criteria, and it progresses through these phases when it serves the requirements or accomplishes specific tasks. A robust DLM process organizes and manages a company's data. It enables the business to achieve critical process objectives, including data security and availability. 

Businesses that have an effective DLM strategy in place will find it easier to stay ahead of the constantly evolving standards and requirements. DLM enables businesses to maximize the value of their data. Additionally, it offers more control over the data within the organization, aids in achieving archiving requirements, reduces the workload for information technology, lowers storage expenses, and facilitates the decision-making process.

The data lifecycle management process goals are:

1. Confidentiality: The DLM process ensures that the data is safe from unauthorized access, loss, sharing, and theft. It maintains the confidentiality of sensitive information.
2. Integrity: An effective DLM strategy focuses on maintaining the integrity of the data. It ensures that the data is accurate, authentic, and reliable.
3. Availability: Organizations must have easy access to the necessary data for making informed business decisions. The DLM process makes the data readily available and accessible to authorized users.

The stages of data lifecycle management are as follows:

1. Create: The first stage in the DLM process involves generating and collecting data. The data may be created internally in the organization, or the employees may collect it by employing various data collection methods.
2. Store: After data creation, the data must be processed and further managed. The created data must be stored in assigned databases, data warehouses, and file shares. The information must also be classified based on its value and sensitivity.
3. Use: In the following stages of data life cycle management, the stored information must be analyzed and visualized for further understanding. The stakeholders associated with the business, including customers, partners, business users, and regulatory frameworks, may be required to use the analyzed and visualized company data.
4. Share: The business may need to share and communicate the data to stakeholders and other parties for further collaboration.
5. Archive: The data that is no longer required for use may be archived in a separate storage. This archive may contain data that are not necessary but cannot be destroyed yet due to regulatory or business requirements.
6. Destroy: In the final stage, the data that is no longer required to be used or retained can be erased from the database.

Let us study the following examples to understand this process:

Example #1

Suppose Bloom's Finance is a financial institution that deals with a lot of sensitive information associated with its customers. The company has a robust DLM strategy in place. It stores data in secure databases that can be accessed only by authorized individuals. The sensitive data are kept more securely through encryption and password protection mechanisms. The company ensures that information is easily accessible and available for decision-making purposes. It also removes any redundant information that is no longer in use.

Example #2

Rimage, a leader in data stewardship globally, announced the release of SOPHIA, an AI-enabled digital asset management system. SOPHIA enables organizations to obtain and manage any data type from any source while also automating and enriching global digital assets in a single repository. According to Dataversity, almost 90% of the data generated within organizations is unorganized. It generates enormous amounts of orphaned data, which wastes time, increases storage costs, and raises company risk. Organizations require a basic storage infrastructure that enables easy collaboration and can be customized to meet specific business requirements and authorization constraints.

Some benefits of the data lifecycle management process include the following:

1. In some specific industries, the compliance standards require the companies to retain the data for a particular period. After the minimum period for data retention has passed, the company may delete or remove the records that are no longer necessary for legal, administrative, auditing, and other operational functions. The DLM process helps companies in this process.
2. Businesses depend on the data to enhance their operations and make informed decisions. A successful DLM method ensures that the data is always available and is accurate, consistent, and reliable. It also ensures that the data is secure and is up to date with the data privacy regulations.
3. Data security is one of the most significant concerns in organizations. A good DLM strategy helps a business safeguard its data from cyber attacks, deletion, and loss. It ensures that the possibility of data security breaches is minimized, which aids in preventing crucial information from being misused by unauthorized parties.

The differences between the two are as follows:

Data Life Cycle Management

1. DLM is concerned with the movement of data from one stage to another, starting from data collection or generation to deletion or reuse.
2. It aims to offer guidelines on when to delete specific types of data. DLM processes entire information or record files.
3. The DLM systems categorize data files according to their size, type, and maturity. They enable businesses to search the stored data for a specific type of file from a specific period.

Information Life Cycle Management (ILM)

1. ILM is a data-centric strategy that offers a centralized, uniform approach to data management. It is implemented through a policy-based system.
2. The information lifecycle management process addresses the relevance and reliability of information. It functions per the data found within a file.
3. The ILM tools enable companies to quickly search across various types of files for a specific piece of data, like a customer's email address.

This article has been a guide to what is Data Lifecycle Management. We explain in detail its goals, stages, benefits, examples, and comparison with ILM. You may also find some useful articles here –

• Data Classification
• Data Integrity
• Data Enrichment