Data lifecycle management

Definition

Data lifecycle management (DLM) refers to the comprehensive process of managing data from its initial creation and storage, through its active use, and finally to its archiving or deletion. This approach helps organizations ensure that their data is efficiently utilized, secured, and compliant with regulations throughout its entire lifespan, which is crucial when dealing with big data challenges such as data volume, velocity, and variety.

5 Must Know Facts For Your Next Test

1. Data lifecycle management encompasses several phases including creation, storage, usage, sharing, archiving, and deletion of data.
2. Effective DLM can help organizations minimize storage costs by identifying obsolete or redundant data that can be archived or deleted.
3. With the exponential growth of big data, implementing DLM strategies helps organizations meet compliance requirements for data retention and privacy.
4. DLM also involves automating processes for data movement and transformations, which improves efficiency in managing large datasets.
5. Collaboration between IT and business units is essential in DLM to ensure that data is accurately classified and managed according to its value to the organization.

Review Questions

• How does data lifecycle management improve the efficiency of handling big data challenges?
  • Data lifecycle management improves efficiency by providing a structured approach to handling data throughout its lifespan. This process helps organizations identify which data is actively used and which can be archived or deleted, thus minimizing unnecessary storage costs. Additionally, it automates various data processes such as movement and transformation, allowing for quicker access and utilization of important datasets while ensuring compliance with regulations.
• Discuss the relationship between data governance and data lifecycle management in managing organizational data.
  • Data governance and data lifecycle management are interrelated concepts that together enhance an organization's ability to manage its data assets. While DLM focuses on the processes surrounding the management of data from creation to deletion, data governance sets the policies and standards that dictate how this management should occur. Effective governance ensures that DLM practices are aligned with organizational goals, legal requirements, and industry standards, fostering accountability and enhancing the overall quality of the organization's data.
• Evaluate the impact of implementing effective data lifecycle management strategies on organizational compliance and decision-making.
  • Implementing effective data lifecycle management strategies significantly impacts organizational compliance by ensuring that data is retained according to regulatory requirements while minimizing the risks associated with improper handling. By categorizing and managing data systematically, organizations can make more informed decisions based on accurate and up-to-date information. Furthermore, having a clear understanding of the data's lifecycle enables organizations to quickly respond to audits or inquiries regarding their data practices, thereby enhancing their credibility and reliability in the marketplace.

Related terms

Data Governance:

The overall management of data availability, usability, integrity, and security in an organization, ensuring that data is managed as a valuable resource.

Data Stewardship: The practice of overseeing the proper management of data assets within an organization, ensuring data quality and compliance with policies and regulations.

Data Archiving: The process of moving inactive data that is no longer needed for immediate access to a storage system for long-term retention and compliance.