What does RUMBA stand for in evaluating data quality?

What does RUMBA stand for in evaluating data quality?

• A. Relevant, Understandable, Measurable, Behavioral, Achievable
• B. Relevant, Useful, Measurable, Behavioral, Achievable
• C. Reliable, Understandable, Measurable, Behavioral, Achievable
• D. Relevant, Understandable, Measurable, Behavioral, Actionable

Answer: (A)

RUMBA is about ensuring data are useful and trustworthy for decision making. In this interpretation, the data quality criteria are Relevant, Understandable, Measurable, Behavioral, and Achievable. Relevant means the data directly address the question or decision at hand—no irrelevant details waste resources. Understandable ensures the data definitions, codes, and values are clear to anyone who uses them, so there’s no misinterpretation. Measurable means there are concrete, repeatable ways to quantify the data, with defined metrics or standards. Behavioral emphasizes that the data reflect actual actions or observed phenomena rather than guesses or assumptions, which helps maintain accuracy. Achievable focuses on feasibility—data can realistically be collected with the available tools, time, and resources. This combination best supports evaluating data quality because it covers usefulness, clarity, quantification, realism, and practicality.

RUMBA is about ensuring data are useful and trustworthy for decision making. In this interpretation, the data quality criteria are Relevant, Understandable, Measurable, Behavioral, and Achievable.

Relevant means the data directly address the question or decision at hand—no irrelevant details waste resources. Understandable ensures the data definitions, codes, and values are clear to anyone who uses them, so there’s no misinterpretation. Measurable means there are concrete, repeatable ways to quantify the data, with defined metrics or standards. Behavioral emphasizes that the data reflect actual actions or observed phenomena rather than guesses or assumptions, which helps maintain accuracy. Achievable focuses on feasibility—data can realistically be collected with the available tools, time, and resources.

This combination best supports evaluating data quality because it covers usefulness, clarity, quantification, realism, and practicality.