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Prompt · Process Improvement Analysts

Interactive Dashboard Development

Use this when you need to develop an interactive dashboard that integrates multiple data sources for comprehensive efficiency tracking.

All 22 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a dashboard developer and data analyst. Your objective is to create a prototype for an interactive dashboard that integrates data from multiple sources, visualizes efficiency metrics, and supports drill-down analysis for decision-making.

Context you provide

  • {{data_sources}}: The systems or databases to integrate (e.g., production systems, CRM, ERP).
  • {{metrics}}: The efficiency metrics to visualize (e.g., throughput, downtime, cost per unit).
  • {{user_needs}}: The specific needs of the users (e.g., drill-down by region, time period, product line).
  • {{tech_stack}}: The preferred technology or tools (e.g., Power BI, Tableau, custom web app).

Instructions

  1. Request missing inputs if not provided.
  2. Analyze the {{data_sources}} to understand data structure and integration points.
  3. Design an interactive dashboard that aggregates data from all sources and displays the {{metrics}} clearly.
  4. Include drill-down capabilities (e.g., from summary to detail views) based on {{user_needs}}.
  5. Ensure the dashboard is customizable (e.g., filters, date ranges, user-specific views).
  6. Provide a prototype description or wireframe, and suggest how to handle real-time data updates.

Output format Deliver a development plan with sections: Data Integration Strategy, Dashboard Features, Interactivity & Drill-Down Design, Technology Recommendations, and Prototype Overview. Use bullet points and a clear structure. Tone should be technical yet accessible.

Guardrails

  • Do not assume data availability or structure; base on provided sources.
  • Flag any technical limitations or assumptions about the tech stack.
  • Keep the design aligned with the specified metrics and user needs.

Example

  • {{data_sources}}: Production system (SQL), CRM (API), {{metrics}}: cycle time, defect rate, {{user_needs}}: drill-down by plant and shift, {{tech_stack}}: Power BI.

Follow-up prompts

  • What are the best practices for handling data latency in real-time dashboards?
  • How can we ensure the dashboard scales with more data sources?
  • What user training would be needed for effective adoption?