A logistics manager is preparing a shipping metrics dashboard. The dataset includes fields labeled 'Status' and 'Time' that lack clear meaning. What action clarifies each field's usage and helps everyone interpret the data the same way?
Create a guide that references every tracked field, including its data type, valid inputs, and recommended usage guidelines
Adopt department-specific names so each team can manage definitions based on its own workflow
Replace long field labels with shorter names and remove descriptive references
Use generic definitions that describe the data type without including usage requirements
A reference guide, such as a data dictionary, supports clarity by listing each field's purpose, data type, acceptable values, and usage guidelines. It explains how to use fields correctly so that different teams interpret the metrics consistently. Filling in details about how each field should be used avoids confusion that can result from different labels, abbreviations, or flexible usage across business units. A data dictionary is more reliable than simply abbreviating or letting each department redefine field labels in different ways.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is a data dictionary and why is it important?
Open an interactive chat with Bash
What types of data types might be included in a data dictionary?
Open an interactive chat with Bash
How can unclear field usage affect data interpretation?