Which best describes data lifecycle management in AIS?

Which best describes data lifecycle management in AIS?

• A. Manage data from creation to deletion, including storage, usage, sharing, archiving, and disposal with governance policies.
• B. Only store data securely and delete when not needed.
• C. Focus on processing transactions without governance.
• D. Ensure data is never archived.

Answer: (A)

Data lifecycle management in an AIS treats data as an asset that moves through stages from creation to disposal, with governance guiding every step. This means managing how data is created and integrated, stored securely, used for processing and decision-making, shared with appropriate access controls, archived for long-term retention, and finally disposed of when no longer needed, all under defined policies. The governance aspect is crucial because it sets who can access data, how long it is kept, how privacy and security are maintained, and how data quality is preserved, ensuring compliance and auditability throughout the data’s life. The other options are too narrow or contradictory. Focusing only on secure storage and deletion misses how data is used and shared, and how it should be archived for future needs. Leaving governance out and concentrating on processing transactions ignores the policy framework that protects data quality, privacy, and compliance. Saying data should never be archived conflicts with retention policies and legal or regulatory requirements that often mandate archiving data for specified periods.

Data lifecycle management in an AIS treats data as an asset that moves through stages from creation to disposal, with governance guiding every step. This means managing how data is created and integrated, stored securely, used for processing and decision-making, shared with appropriate access controls, archived for long-term retention, and finally disposed of when no longer needed, all under defined policies. The governance aspect is crucial because it sets who can access data, how long it is kept, how privacy and security are maintained, and how data quality is preserved, ensuring compliance and auditability throughout the data’s life.

The other options are too narrow or contradictory. Focusing only on secure storage and deletion misses how data is used and shared, and how it should be archived for future needs. Leaving governance out and concentrating on processing transactions ignores the policy framework that protects data quality, privacy, and compliance. Saying data should never be archived conflicts with retention policies and legal or regulatory requirements that often mandate archiving data for specified periods.