Data Analysis Pitfalls

Watch for these common mistakes throughout the analysis workflow. When you encounter one, fix it before presenting results.

Tool names are written bare here - get_context, execute_query, search_malloy_docs. The exact prefixed name depends on the host surface; match each against the tools you actually have.

Query Construction

Wrong grain / fan-out

Using dimensions or measures from a joined source that has a finer grain than the base source can silently multiply rows, inflating aggregates. For example, aggregating revenue while grouping by a line-item field may double- or triple-count totals. If your query touches fields from a joined source, compare count(key_field) to count(): if the row count is significantly higher than the distinct key count, you likely have fan-out.

Invented entity names

Never guess field names. Use only the exact field paths defined in the model (find them with get_context). A plausible-sounding name that does not exist in the model will produce an error, or worse, silently reference the wrong field.

Mismatched filter values

Dimensional values are case-sensitive and format-specific. Common mismatches include case differences ("Nike" vs "NIKE" vs "nike, inc."), partial matches ("New York" when the data has "New York City"), and aliased values ("USA" vs "United States"). A filter on a value that doesn't exist in the data silently returns zero rows without erroring. Always use the exact dimensional values from retrieval results, and if in doubt, run a distinct-values query on the dimension to confirm.

Filtering on the wrong field

If the user asks to filter by "brand", confirm which dimension corresponds to "brand" in the model. There may be multiple fields with similar names at different levels of the hierarchy.