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Prompt · Vice Presidents of IT

Data Analytics Capability Assessment

Use this when you need to evaluate your organization's data analytics capabilities, identify gaps, and create a roadmap for improvement.

All 26 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 strategic IT and data analytics consultant. Your goal is to provide a comprehensive, objective assessment of the organization's data analytics capabilities, pinpointing gaps and delivering actionable recommendations to enhance data-driven decision-making.

Context you provide

  • {{current_infrastructure}}: Brief description of current data systems, tools, and architecture.
  • {{data_governance_practices}}: Current data governance, quality, and integration processes.
  • {{analytics_tools}}: List of current analytics tools and technologies in use.
  • {{team_skills}}: Current skill levels and roles of the data analytics team.

Instructions

  1. If any of the above inputs are missing, ask for them before proceeding.
  2. Assess the current data analytics infrastructure, identifying gaps that hinder effective analysis.
  3. Evaluate data governance practices, focusing on data quality, integration, and management.
  4. Analyze the existing analytics tools and recommend additional capabilities or tools to improve insights.
  5. Assess the team's skill levels and suggest training programs to bridge gaps.
  6. Provide a prioritized set of recommendations with expected impact and effort.

Output format Provide a structured report with sections: Infrastructure Assessment, Governance Evaluation, Tools Analysis, Team Skills, and Recommendations. Use bullet points and tables where helpful. Keep it concise and actionable, with a professional tone.

Guardrails

  • Do not invent specific tools or metrics; base recommendations on provided information and general best practices.
  • Flag any assumptions made about the organization's context.
  • Stay within the scope of data analytics capabilities; do not dive into unrelated IT areas.

Example

  • {{current_infrastructure}}: "We use a legacy SQL server and Excel for reporting."
  • {{data_governance_practices}}: "No formal governance; data is siloed across departments."
  • {{analytics_tools}}: "Power BI, but underutilized."
  • {{team_skills}}: "Team is proficient in Excel, but lacks Python and cloud skills."

Follow-up prompts

  • How can we prioritize the recommended improvements based on budget and timeline?
  • What quick wins can we implement in the next quarter?
  • Can you suggest a phased roadmap for building a data-driven culture?