Data minimization in data journalism involves:

Data minimization in data journalism involves:

• A. Collect as much data as possible
• B. Keep data forever
• C. Share data widely
• D. Collect only necessary data

Answer: (D)

Data minimization means collecting only what is strictly necessary to tell the story or answer the question at hand. In data journalism, this focuses on identifying the exact data that will help verify facts, support analysis, or illustrate the narrative, and avoiding extra data fields or extraneous information. This approach protects privacy, reduces risk, and makes handling the data simpler and safer. For example, when analyzing housing prices, you’d typically use aggregated or anonymized figures by neighborhood rather than pulling every listing with names, addresses, or sensitive details. Collecting as much data as possible runs counter to this, since it increases privacy risks and the burden of managing large datasets. Keeping data forever also defeats minimization because the point is to store only what you need for the task and for as long as it’s necessary. Sharing data widely isn’t about limiting collection; it’s about openness or distribution, which may be important for transparency but doesn’t address reducing the amount of data you actually collect.

Data minimization means collecting only what is strictly necessary to tell the story or answer the question at hand. In data journalism, this focuses on identifying the exact data that will help verify facts, support analysis, or illustrate the narrative, and avoiding extra data fields or extraneous information. This approach protects privacy, reduces risk, and makes handling the data simpler and safer. For example, when analyzing housing prices, you’d typically use aggregated or anonymized figures by neighborhood rather than pulling every listing with names, addresses, or sensitive details.

Collecting as much data as possible runs counter to this, since it increases privacy risks and the burden of managing large datasets. Keeping data forever also defeats minimization because the point is to store only what you need for the task and for as long as it’s necessary. Sharing data widely isn’t about limiting collection; it’s about openness or distribution, which may be important for transparency but doesn’t address reducing the amount of data you actually collect.