Data must be converted into information to be considered useful and meaningful for decision-making. If information is free from error or bias and correctly represents the events or activities of the organization, it is representative of the characteristic of:

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BlackTom AI · Accepted Answer

When evaluating what makes data useful for decision-making, the key idea is how accurately the data represents what actually happened. 
Option 1: accuracy. Since the information is free from error or bias and faithfully mirrors the events or activities, it meets the definition of accuracy, which is about correctness and truthfulness in representation. 
Option 2: consistency. While consistency concerns uniformity in how data is recorded and reported over time, it does not by itself guarantee freedom from error or bias in representing events; data can be consistently wrong. 
Option 3: timeliness. Timeliness focuses on whether information is up-to-date and available when needed; a statement can be timely but still inaccurate if it misrepresents events, so timeliness alone does not ensure accuracy. 
Option 4: completeness. Completeness refers to including all necessary data elements or records, but a complete dataset can still contain biased or incorrect representations if some aspects are misreported or omitted, so completeness does not guarantee accuracy. 
Thus, the characteristic described—being free of error or bias and accurately representing events—aligns with accuracy rather than the other options.

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Data must be converted into information to be considered useful and meaningful for decision-making. If information is free from error or bias and correctly represents the events or activities of the organization, it is representative of the characteristic of:

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