Acceptance Criteria For Reports

Why this matters

Clear acceptance criteria turn fuzzy requests into buildable, testable BI reports. They reduce rework, accelerate sign-off, and protect data quality. As a BI Developer, you will:

• Translate stakeholder goals into specific, testable report behaviors.
• Align metric definitions across teams to avoid conflicting numbers.
• Define data constraints, performance thresholds, and refresh expectations for reliable dashboards.
• Guide UAT (user acceptance testing) with unambiguous pass/fail checks.

Concept explained simply

Acceptance criteria describe what "good" looks like for a report so everyone can agree it’s done. They are specific, measurable conditions covering data, logic, visuals, and operations.

Given Region = "North America" and Date = last full month,
when the dashboard loads,
then Total Revenue equals the finance ledger for that period within 0.1% variance.

Mental model: 4 layers to cover

• Data: Sources, freshness, joins, filters, currency/time zone, identity rules, exclusions.
• Logic: Metric formulas, aggregation levels, handling of nulls/outliers, rounding/tolerances.
• Presentation: Layout, sorting defaults, labels/units, drill-through behavior, accessibility.
• Operations: Refresh schedule, performance SLAs, access/roles, auditability, error states.

Core acceptance criteria checklist (adapt as needed)

• Data source(s) named and versioned.
• Data recency: last refresh timestamp and schedule (e.g., hourly at :15).
• Record scope: included/excluded entities (e.g., test users, refunds, internal orders).
• Identity keys and deduplication rules.
• Time handling: timezone, calendar type (fiscal/ISO week), last full period rules.
• Currency handling: base currency, conversion timing, rounding rules.
• Metric definitions with formulas and level of aggregation.
• Nulls and outliers handling (e.g., treat null price as 0? exclude?).
• Default filters and slicers; allowed filter values.
• Sorting defaults for tables and charts.
• Drill-through/drill-down rules and expected fields.
• Labels, units, number formats, and thresholds/conditional formatting.
• Accessibility: contrast, keyboard navigation, alt text/aria labels when applicable.
• Performance: load under X seconds with dataset size Y.
• Security: roles/row-level security expectations.
• Validation: reference queries or files and acceptable tolerance.
• Error states: what users see if data is stale or missing.
• Sign-off: who approves and how success is verified.

Worked examples

Steps to write strong acceptance criteria

1. Clarify the goal: What decision or action will this report support?
2. Fix definitions: Write metric formulas, keys, time and currency rules.
3. Design behavior: Defaults, filters, drill paths, sorting, and error states.
4. Set operations: Refresh cadence, performance, access, validation method.
5. Make it testable: Add exact references, tolerances, and who signs off.

Goal:
Data sources & scope:
Metric definitions:
Time & currency rules:
Filters & defaults:
Presentation rules:
Performance & refresh:
Validation & tolerance:
Security & access:
Sign-off:

Practice exercises

Note: The quick test is available to everyone. Only logged-in users have their progress saved.

Self-check for your exercises

• Are metric formulas and time rules explicit?
• Can each criterion be tested with a clear pass/fail?
• Do you cover data, logic, presentation, and operations?
• Is there a validation step with a reference and tolerance?
• Did you specify defaults, filters, and handling of nulls/outliers?

Common mistakes and how to self-check

• Vague metrics: "show active users" without definition. Fix by writing the formula and scope.
• No time rules: Missing timezone, last full period rules. Fix by stating both.
• Ignoring edge cases: Refunds, test data, duplicate keys. Fix by listing exclusions and dedup rules.
• Untestable criteria: "looks clean" is subjective. Fix by adding measurable thresholds.
• Missing validation: No reference query/file. Fix by naming the source and tolerance.
• Overlooking performance or refresh. Fix by adding SLA and schedule.
• Security gaps: PII exposed without roles. Fix by masking and defining access.

Mini challenge

Write 8–12 acceptance criteria for a Marketing Funnel dashboard (Impressions → Clicks → Sign-ups → Paying Users) for the last 90 days, with daily refresh and drill-down to campaign.

Who this is for

• BI Developers and Analytics Engineers creating or maintaining reports.
• Analysts and PMs defining report requirements and UAT.
• Data leaders seeking consistent, testable report standards.

Prerequisites

• Basic SQL and familiarity with your warehouse tables.
• Understanding of core business metrics in your domain.
• Comfort with your BI tool’s filters, visuals, and roles/security.

Learning path

1. Gather stakeholder goals and key decisions.
2. Draft metric definitions and data scope.
3. Write acceptance criteria across data, logic, presentation, and operations.
4. Create validation queries and sample datasets.
5. Run UAT with the criteria; iterate and finalize sign-off.

Practical projects

• Retrofit acceptance criteria to an existing dashboard and run a UAT dry run.
• Create a one-page acceptance criteria sheet for a new KPI tile set.
• Build a validation notebook (SQL + notes) linked to your criteria.
• Define performance SLAs and measure current dashboards against them.
• Draft RLS/PII masking rules as acceptance criteria for a support report.

Next steps

• Do the exercises above and compare with the sample solutions.
• Take the quick test below to check your understanding.
• Apply this to your next stakeholder request—start by writing the criteria first.