What is data lifecycle management?

Data lifecycle management is a set of policies, procedures, and processes that ensure the confidentiality, integrity, and availability of information. A data lifecycle management framework optimizes the value of information and protects it from creation to end-of-life.

Data lifecycle management offers best practices in each phase that information passes through that dictate how it is collected, stored, used, shared, archived, and destroyed. Data governance, data protection, and data management are also included in data lifecycle management.

Data lifecycle management products automate many of the related processes. These products are used to apply and enforce data lifecycle management policies. Data migration between phases can also be automated.

Data lifecycle management vs information lifecycle management

Data lifecycle management phases

There are five distinct phases in a data lifecycle.

Data lifecycle management provides guidelines and governance for each phase to draw the most from information, protect it, and handle it cost-effectively.

1. Creation and collection of data
Data is generated from various sources and collected in different formats (e.g., structured data, unstructured data, and semi-structured data). Data lifecycle management processes direct what data is collected and how it is collected (e.g., user-generated or machine-generated).

2. Processing and storing data
Once data is created, data lifecycle management plans determine where it should be stored (e.g., databases, file shares, or data warehouses) based on its uses and characteristics. In addition, data lifecycle management is used to define how data is classified based on how it will be used, its value, and the level of security required to provide sufficient protection.

3. Managing data usage and sharing
Data is used by a variety of internal and external users with different workflows and requirements. Data lifecycle management ensures that data is accessible to authorized users while controlling usage based on established rules related to security, privacy, and the maintenance of data integrity. Data lifecycle management also includes tracking usage and creating audit trails.

During this phase, when users actively engage with data, data lifecycle management facilitates data-related operations, such as analytics, business intelligence, collaboration, and visualization. It also provides controls to regulate data sharing between people and applications, limiting it based on internal policies and external regulations.

4. Data archiving
Data archiving is a critical function of data lifecycle management. During the archiving phase, data is moved to long-term storage to be saved for future use or regulatory requirements.

While it is not as accessible as production data, users need to be able to reach it when needed without delays. Data lifecycle management rules help determine what data should be archived as well as make sure that it is organized in a way that it can be easily searched and retrieved.

5. Data deletion 
The final phase of data lifecycle management is deletion, when data is deemed redundant or no longer needed. Data lifecycle management provides guidelines on how and when data should be permanently deleted, along with the steps to take before doing so.

Data lifecycle management benefits

Following are four of the top benefits that are realized when a data lifecycle management strategy is implemented.

1. Enhances data usability
A data lifecycle management strategy includes developing and applying policies and procedures for how metadata is created and used to ensure consistency and optimal searchability.

2. Supports compliance and governance rules
The list of compliance rules that organizations must follow continues to grow and become increasingly complex. Data lifecycle management helps organizations adhere to the stringent data protection and data privacy requirements set forth in regulations such as the European Union’s General Data Protection Regulation (GDPR), the Sarbanes-Oxley Act (SOX), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA).

3. Improves processes across an organization
By making quality data readily accessible, data lifecycle management makes all processes run more effectively and efficiently with streamlined operations.

4. Reduces and controls costs
Implementing data lifecycle management allows teams to move data to subsequent phases without delay. This reduces the expenses associated with security and storage. Conversely, when information is needed, data lifecycle management frameworks make it easy to access, enabling users to work more efficiently and derive value from the data.

Data lifecycle management goals

The most commonly cited goals of data lifecycle management mirror the CIA (Confidentiality, Integrity, and Availability) triad, a widely used data security model. While the focus of the CIA triad is security, it has the halo effect of improving overall operations by assuring that users have access to the data they need when they need it.

Confidentiality

A data lifecycle management framework includes guidelines that keep sensitive information secure when it is stored to protect it from unauthorized access, sharing, or theft. Data lifecycle management protects the confidentiality of an organization’s data by using policies to separate data based on the sensitivity of the information. This is used to establish access controls to maintain confidentiality.

Integrity

Data lifecycle management plays a vital role in maintaining the integrity of data. To be useful, data must be accurate and reliable.

Data lifecycle management provides the controls needed to maintain data integrity regardless of how many users access it, how it is used, or where it is stored.

Another aspect of data integrity is eliminating data sprawl that results in multiple copies of the same information with no indicator of which is the authoritative copy.

Availability

Availability is a key function of data lifecycle management. Authorized users have ready access to information in a way that fits their workflows. Data lifecycle management streamlines and optimizes operations by making data accessible and searchable.

Data lifecycle management helps organizations use data effectively and securely

With data lifecycle management, organizations extract the maximum value from data while enforcing policies that make sure that it is secure and handled in accordance with compliance requirements. It enables a balance between security and operational efficiency.

Data lifecycle management supports data protection and privacy without inhibiting productivity. In fact, it increases productivity by providing ready access to information.

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