Start with the reader, decision and metric contract
Learn the reasoning. Try the task. Compare your answer.
Prepared for DataCareerHub · Curriculum version 2026-10-15.5
By the end of this lesson
- Write a dashboard brief for a specific decision.
- Define measures with explicit populations, units and denominators.
- Separate supplied evidence from unavailable business metrics.
Verified April–May source from Module 4 · EUR · Static offline practice · Open the worked dashboard ↗
Understand the method
A dashboard is a reading and decision tool. Its first design question is who needs to understand what, and what they can reasonably do next. Northstar Shop’s fictional operations lead wants to review whether the supplied April–May extract supports expanding an initiative. The dashboard should help the reader compare completed-order value, understand channel composition and identify evidence gaps before recommending expansion. It should not imply that a higher total proves commercial success.
Reuse a checked analytical source
This module reuses Module 4’s verified file unchanged: 44 unique orders across April and May 2026. Each month contains 20 completed, one returned and one cancelled order. The earlier preparation work is already complete. Keep its scope and provenance available, rather than silently replacing the source with a plausible summary. One row represents an order, not a customer, product line or visit. Monetary values are EUR major units, not the integer cents used in Module 3.
Choose measures that serve the decision
The primary measure is completed-order value, accompanied by completed-order count and average order value. Value describes the supplied eligible total; count shows how many orders contributed; AOV helps distinguish volume from average value. Delivery measurement coverage belongs alongside the observed delivery mean so missing observations stay visible. Channel breakdowns are diagnostic context, not separate financial totals to add again to the headline.
Write a metric contract before drawing a card. Completed-order value sums order_value_eur where status is completed and the selected date/channel filters apply. Completed-order count counts those unique orders. AOV divides that value by that count and is unavailable for an empty group. Coverage divides measured completed deliveries by all eligible completed orders. The observed delivery mean divides measured duration sum by the number measured, not by all orders.
Define comparison scope
The initial view is May and all channels. Its prior-month baseline is April with the same channel filter and completed-status rule. April has no supplied March baseline. The combined April–May view has no supplied prior two-month window. Do not populate a comparison with zero when its source period is absent. A numeric zero in a known prior population is also distinct from a missing period; relative percentage change is undefined if the denominator is zero.
Keep unsupported measures out
The file has no advertising spend, product margin, customer IDs, experiment assignment or return-event timing. It cannot support marketing ROI, profit, conversion rate or causal lift. Avoid renaming order value as profit or changing a status share into a customer return probability. A request from a stakeholder does not create the missing inputs. Record the desired measure and the additional source fields needed to calculate it.
Make the brief reviewable
A useful brief names the audience, decision, source, default view, measures, filters, limits and intended next action. It also distinguishes this static learning snapshot from a live operational feed. The fixture edition identifies the material; it is not a timestamp proving a recent server refresh. A concise dashboard with a clear analytical contract is more useful than an impressive collection of unrelated indicators.
Practical exercise
Read dashboard_brief.txt and metric_dictionary.txt. Write your own brief for the operations lead, choosing no more than four headline cards and two diagnostic views. For each card specify its source fields, eligibility rule, units, denominator where relevant and unavailable behaviour. State the initial filter and comparison window.
Deliverable: a one-page decision brief and metric contract. Include two requested metrics that the source cannot support, and name the missing inputs rather than inventing substitute measures.
Open the worked solution
| Element | Worked choice |
|---|---|
| Reader / decision | Operations lead; investigate evidence before expansion |
| Default view | May 2026 / All channels / Completed orders |
| Cards | Completed value; completed orders; AOV; delivery measurement coverage |
| Diagnostic views | Monthly completed value; channel AOV with group counts |
| Missing measures | Profit needs cost/margin data; conversion needs a defined eligible visitor/customer population |
The May baseline is €3,420 across 20 completed orders, AOV €171, and measured-delivery coverage 19/20 = 95%. April’s equivalent values are €2,900, 20, €145 and 14/20 = 70%. Both channel AOVs fall despite the aggregate increase. The brief therefore asks for channel context and sparse-group warnings, not an automatic expansion recommendation.
Comparison percentage is (selected May value − April value) / April value × 100, using the same channel selection. Preserve a separate unavailable state for April or the combined window. Label this fixture a static fictional teaching extract. No authenticated access, payment or live connection is required to inspect it.
One more question.
Selected usability references: chart descriptions, meaning beyond colour and keyboard operation. These references do not certify this example.
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