Data Analysis

To turn raw data into decision-ready insights, start by identifying the specific data problem and the decision that would change outcomes. Apply a disciplined framework defining metric contracts, chart guidance, and decision briefs to ensure rigorous analysis and transparent assumptions.

KPI debugging is the process of investigating anomalies and unexpected changes in your key performance indicators. It fits into data analysis workflows by applying structured anomaly detection and chart guidance to quantify uncertainty and document caveats for robust recommendations.

To conduct cohort analysis and funnel analyses for executive reporting, start from an explicit decision question and specify relevant metrics. Generate a decision brief that documents transparent assumptions, quantifies uncertainty, and outlines robust recommendations.

Yes, you can apply this analysis framework across SQL, spreadsheets, BI tools, notebooks, and ad hoc data tables. It provides end-to-end guidance for defining metric contracts and chart guidance to ensure consistent, stakeholder-ready outputs regardless of your data environment.

The best way to define metric contracts is to explicitly link each metric to a concrete decision and document all underlying assumptions. This ensures consistent analysis, quantifies uncertainty, and produces decision-ready outputs that stakeholders can trust.

Data analysis often fails to produce actionable decisions because it lacks a disciplined framework starting from an explicit decision. Without defining metric contracts and documenting caveats, raw data remains descriptive rather than yielding robust, decision-ready recommendations.