- Published
- 8 October 2026
- Last reviewed
- 8 October 2026
How to use this glossary
Use this glossary when defining a report, checking a dataset or discussing a business metric. Product-specific meanings can differ; the measure entry uses the Power BI calculation context explicitly.
Browse the alphabetical term index below. Select a term to jump to its definition, example and common mistake. Use the return link after each entry to browse again. You can also use your browser’s Find command to locate a word on this page. Definitions are original RDE plain-language explanations. All examples are fictional teaching scenarios.
Term index
12 terms, listed alphabetically.
- Dashboard
- Data quality
- Denominator
- Dimension
- Duplicate record
- Grain
- Key performance indicator (KPI)
- Measure
- Metadata
- Missing value
- Numerator
- Semantic model
Definitions and practical examples
Dashboard
Meaning: A concise view of important measures used to monitor a situation.
Example: A coordinator checks booking volumes and unassigned requests in one overview.
Common mistake to avoid: In Power BI, a dashboard is specifically a single-page service view; other products use the term differently.
Data quality
Meaning: The suitability of data for its intended use.
Example: A booking report needs accurate course IDs and sufficiently complete booking dates.
Common mistake to avoid: A dataset can be suitable for one question and unsuitable for another.
Denominator
Meaning: The quantity used as the base when calculating a ratio or rate.
Example: With 8 completed bookings out of 32 eligible bookings, 32 is the denominator.
Common mistake to avoid: Use the correct eligible population; division by zero is undefined.
Dimension
Meaning: Descriptive information used to group, filter or label facts.
Example: Course, location and booking month provide categories for a booking report.
Common mistake to avoid: A category is not automatically a reliable unique identifier.
Duplicate record
Meaning: A record that repeats an entity or event under a defined duplication rule.
Example: Two rows share the same booking ID and represent the same booking.
Common mistake to avoid: Similar names alone do not prove duplication; distinct people may share a name.
Grain
Meaning: The level of detail represented by one record.
Example: One table has a row per booking; another has a row per booking-status change.
Common mistake to avoid: Write the grain before joining tables or deciding which rows to count.
Key performance indicator (KPI)
Meaning: A selected measure used to assess progress towards an important objective.
Example: An enquiry team tracks its agreed response-time measure against a service objective.
Common mistake to avoid: A number becomes useful only when its purpose, definition and owner are clear.
Measure
Meaning: In a Power BI semantic model, a calculation evaluated within the relevant filter context.
Example: A booking-count measure changes when a reader filters the report to one course.
Common mistake to avoid: Do not confuse the displayed result with an unchanging value stored in every row.
Metadata
Meaning: Information describing the meaning, structure or handling of data.
Example: A data dictionary explains a booking-date field, its format and its source.
Common mistake to avoid: A column heading alone rarely documents all relevant assumptions.
Missing value
Meaning: An absent or unknown value for a field.
Example: A booking exists, but the attendance outcome has not yet been recorded.
Common mistake to avoid: Missing is not automatically zero, false or a negative result.
Numerator
Meaning: The quantity counted or measured above the division line in a ratio.
Example: For 8 completed bookings out of 32 eligible bookings, 8 is the numerator.
Common mistake to avoid: Its definition must match the population used in the denominator.
Semantic model
Meaning: An organised model of data, relationships and calculations used for analysis.
Example: A Power BI model links bookings to course descriptions and defines reusable measures.
Common mistake to avoid: Check relationships and definitions; a model does not automatically correct bad source data.
A distinction worth keeping clear
A dimension provides descriptive categories for grouping data. A measure provides a calculation. Grain states what one record represents. Combining tables with different grains can multiply rows and distort totals even when the chart looks convincing.
Practise using the terms
A table has one row for each booking, while another has one row for each booking-status change. A report needs the number of bookings by course. Identify the grain of each table and explain why counting joined rows could overstate bookings. Write your answer in your own notes before reading the explanation. No answer is submitted or stored by this page.
Suggested answer and explanation
The first table has booking grain. The second has booking-status-change grain, potentially several rows per booking. Joining them can repeat each booking once for every status change. Define the booking-count measure and relationship carefully, or aggregate to the intended grain before counting.
Sources and scope
The sources below support the terminology and relevant context. The definitions, examples, cautions and practice tasks on this page are original RDE teaching material, not verbatim extracts or a replacement for an official standard or certification syllabus. Reviewed 8 October 2026. Author: Jefferson Otonbara Imgbi.
Cover image: AI-generated illustrative scene created specifically for this resource.