An analytical model describes tables, relationships and the grain of each dataset. Decide what one row represents before defining relationships or measures.
Before you begin
Understand rows, keys and basic aggregation. Use a fictional dataset and define the report's audience and intended decision.
Work through the idea
Write metric definitions and the reporting period before styling the page. Reconcile a selected total with source records and explain active filters. Include data freshness and a route to supporting detail. Evaluate the report with a reader who did not build it, then revise confusing labels or interactions.
- State table grain
- Identify unique keys
- Distinguish facts from descriptive dimensions
Practice task
Model fictional sales transactions with product and date dimensions.
Check your result
Show that the product dimension has unique keys and that sales totals do not multiply after a join.
A mistake to avoid
Do not join tables at incompatible grains and trust the resulting totals.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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