Business Analyst

What Problem It Solves

Business analytics work often stalls at the gap between raw data and decision quality: unclear metric definitions, dashboards that do not support action, weak A/B test design, or financial models that are disconnected from customer insights. This skill frames business-analysis work as a repeatable workflow, from defining goals and constraints to producing actionable recommendations.

How It Works

It starts by clarifying the business objective, success criteria, available data, and required inputs. It then organizes the analysis around modern BI tools such as Tableau, Power BI, Looker, and Qlik Sense, as well as cloud analytics platforms like Snowflake, BigQuery, and Databricks. Core capabilities include:
- KPI and strategy frameworks: North Star metrics, OKRs, balanced scorecards, metric hierarchies, and benchmarking.
- Financial and growth analysis: revenue forecasting, CLV, CAC, cohort retention, unit economics, ROI, and scenario sensitivity.
- Data storytelling and experimentation: executive dashboards, A/B tests, causal inference, time series, and hypothesis testing.
- Data governance and process optimization: master data, data warehouses, ETL/ELT, process mining, and automation assessment.

The key sequence is: define goals, assess data, design a framework, execute the analysis, visualize findings, deliver recommendations, and plan continuous monitoring. For deeper examples, the skill points to resources/implementation-playbook.md.

Boundaries and Notes

This skill is suitable for business-analysis guidance, best practices, and checklists. It is not intended for unrelated tasks, and it cannot replace access to production systems, data engineering, or statistical software execution. Output quality depends on input quality. When privacy, compliance, or ethical data use is involved, constraints such as GDPR and CCPA should be checked and validated manually.