The organization currently relies on ad-hoc spreadsheets for reporting, resulting in manual work, inconsistent metrics, and low trust in data. The goal is to transition stakeholders to reliable BI dashboards with clear metrics, governance, and sustainable workflows. The workflow prioritizes empathy for existing processes while guiding the shift to structured, scalable reporting.

The workflow aims to help a Data Team Lead transform raw source table schemas and business requirements into well‑structured semantic models for dbt projects. It ensures that metrics have a single source of truth, models follow medallion or Kimball principles, and documentation and testing standards are consistently applied. The output includes a semantic model diagram, dbt project structure, metric definitions, and documentation guidelines.

The goal is to analyze cloud data warehouse usage and identify ways to reduce spend without compromising reliability or breaching contractual limits. The workflow must decompose cost drivers, propose safe optimization levers, and deliver a clear, ROI‑focused plan. Recommendations must include required approvals, rollback paths, and monitoring guardrails.

Teams across the organization rely on inconsistent metric definitions, causing confusion, duplicated work, and conflicting dashboards. The goal is to standardize disputed metrics into a single, trusted glossary with clear SQL definitions, filters, grain, and known caveats. The workflow must also define canonical dimensions, governance rules, and a stakeholder announcement to drive alignment.

The goal is to convert complex, role-based data access policies into a clear and plain‑English summary for internal stakeholders. The workflow must turn role descriptions, dataset categories, access rationales, and request/approval processes into an aligned, easy-to-understand policy overview. Outputs should avoid sensitive dataset names, use placeholders, and maintain a clear, responsible tone without legalistic language.