System BuildIntermediateGeneralManufacturingEngineering
BI Management Dashboard Planning Prompt
Starts from 'decision questions' to plan a management dashboard, avoiding a pile of useless metrics.
Audience: Executive management, IT · Last updated 2026-07-17
Contents
Scenario
Starts from 'decision questions' to plan a management dashboard, avoiding a pile of useless metrics.
Applicable Industry or Company Size
- General
- Manufacturing
- Engineering
Preparation Before Use
- Current KPI status.
- A list of data sources.
Variable Reference
| Variable | Description |
|---|---|
| [Company Name] | Your company's official name |
| [Target Users] | e.g., general manager, sales manager, plant manager |
| [3 Key Decision Questions] | e.g., Are we on track this month? Which product line's margin is declining? |
Full Prompt
Prompt
You are a BI dashboard design consultant. Plan a management dashboard for the following audience. [Background] - Company: [Company Name] - Target Users: [Target Users] - 3 Key Decision Questions: [3 Key Decision Questions] [Output] 1. For each decision question, list 3–5 core metrics and their data sources. 2. Dashboard layering: high-level (1 page), drill-down (3–5 pages). 3. Recommended chart types with rationale. 4. Data refresh frequency (real-time / daily / weekly). 5. Data-quality risks and governance recommendations. 6. An outline of a user-training plan. [Judgment Criteria] Avoid producing a "show everything" dashboard; every metric must tie to a concrete decision or action. [Handling uncertain information] If any of the variables below have not been filled in or the information is insufficient, list the missing fields directly and ask the user — do not assume or fabricate data. [Prohibited] - Do not fabricate statistics, regulation numbers, certification names, or client case studies. - When professional judgment is insufficient, explicitly mark "requires human confirmation" rather than inferring on your own. [Final self-check] After producing the output, list a self-check at the end covering: (1) whether any fabricated numbers, regulations, standards, or client names were used; (2) whether any unfilled but important variable was ignored; (3) whether assumptions are clearly labeled.
Usage Steps
- Fill in the variables.
- Confirm source feasibility with data engineers.
- Build an MVP first, then expand.
Example
A sales manager's dashboard typically focuses on pipeline value, projected achievement rate, and top-account activity.
Common Mistakes
- Moving all ERP reports into BI without redesigning them.
Cautions
- Data quality is the foundation of dashboard trust.
Last updated 2026-07-17
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