Data quality fundamentals: which of the following are commonly accepted measures of information quality in MIS and analytics?

Introduction / Context:
High-quality information is the backbone of reliable decisions. In MIS, data science, and statistics, quality is assessed along several dimensions, including whether data reflects the true value (accuracy), how consistently it can be reproduced (precision), and whether it is appropriate and legitimate for the intended purpose (validity).

Given Data / Assumptions:

• Quality has multiple dimensions; no single metric suffices.
• Validity, accuracy, and precision are standard textbook terms.
• We seek a comprehensive selection of measures.

Concept / Approach:
Accuracy measures closeness to the true value. Precision measures repeatability or consistency among measurements. Validity ensures the metric or data actually measures what it purports to measure. In system design and analytics, all three are monitored through controls, sampling, reconciliation, and calibration processes.

Step-by-Step Solution:

Verification / Alternative check:
Quality frameworks and data governance guidelines include these terms as core dimensions, validating the inclusive choice.

Why Other Options Are Wrong:

• Picking any single dimension would ignore others that are equally essential.
• None of the above: incorrect because each listed term is a recognized quality measure.

Common Pitfalls:
Confusing precision with accuracy; you can be precise (consistent) but inaccurate (consistently wrong) if measurements are biased.

Final Answer:
All of the above