Data & Analytics Prompts to Manage Your Team and Business

You are a data analyst building the KPI definition and measurement framework. Business context: [DESCRIBE: Company stage, key business functions (sales/product/CS/marketing/engineering), current reporting state (spreadsheets/BI tool/ad hoc), known measurement inconsistencies, stakeholder reporting needs] Build the framework: 1) KPI hierarchy — company-level OKRs → functional KPIs → team-level metrics; everything rolls up 2) KPI definitions — for each KPI: precise definition (what is counted, what is excluded) / data source / owner / update frequency 3) Single source of truth — one authoritative source for each KPI; end the "whose numbers are right?" debate 4) Metric tiers — tier 1 (board-level) / tier 2 (leadership) / tier 3 (operational team); different audiences need different views 5) Governance — who approves a new KPI? How are definition changes communicated? Output: KPI framework. Definitions document. Data source mapping. Governance process. Implementation priority.

You are a data leader building the data governance policy. Context: [DESCRIBE: Company stage, data sensitivity (customer PII/financial/health), current data management practices, regulatory environment, team responsible for data governance, any prior data incidents or compliance findings] Write the policy: 1) Data classification — levels (public / internal / confidential / restricted) with definition and handling requirements for each 2) Data ownership — each data domain has an owner responsible for quality, access, and usage decisions 3) Access controls — who can access what data? Approval process for sensitive data access 4) Data retention — how long is data kept? When and how is it deleted? 5) Acceptable use — what is data allowed to be used for? What is prohibited? Output: Data governance policy. Classification schema. Ownership matrix. Access control procedure. Retention schedule. Acceptable use statement.

Write a data governance policy. Org size: [employees] Data types: [customer PII / financial / employee / analytics] Regulatory requirements: [GDPR / CCPA / HIPAA / SOC 2] Current problems: [duplicates / inconsistent formats / unclear ownership] Tools: [CRM, database, warehouse, BI tool] Policy covering: 1. Data ownership: who is responsible for which datasets 2. Quality standards: what 'good' data looks like 3. Data entry rules: formats, required fields, naming conventions 4. Retention: how long to keep each data type 5. Access controls: who can see and edit what 6. Cleaning cadence: how often to audit 7. How to handle a data quality issue