This portfolio artifact presents my five-stage analytical workflow for turning technical reporting into decision support:
- Start — identify the business decision behind the metric request.
- Framing — turn vague stakeholder language into one precise decision question.
- Design — define the hypothesis, KPI, analytical grain, segments, confounders, and measurement risks before writing SQL.
- Execution — produce and validate evidence with SQL, modular R/Tidyverse workflows, interpretable modeling, Excel outputs, and dashboards.
- Finish — distinguish what the evidence supports from what it does not support and recommend a proportionate next action.
The objective is not merely faster reporting. It is better decisions grounded in transparent evidence. AI assists with structure, analytical design, independent review, and quality control; human judgment remains responsible for the evidence, interpretation, and recommendation.
flowchart LR
A["Start and frame"] --> B["Design measurement"]
B --> C["Execute and validate"]
C --> D["Interpret evidence"]
D --> E["Recommend action"]
The repository contains completed documentation for all five stages:
- Stages 1–2 — Start and Framing: Three-AI Start and Framing Dialogue Framework
- Stage 3 — Measurement Design: Three-AI Measurement Design Framework
- Stage 4 — Execution, Validation, and Deeper Analysis: Three-AI Independent Validation and Analysis Framework
- Stage 5 — Interpretation and Recommendation: Three-AI Interpretation and Recommendation Framework
Together, these documents specify the complete path from an initial stakeholder request to a validated, evidence-traceable decision.
| Project | Decision supported | Evidence |
|---|---|---|
| FulfillIQ | Which sellers should enter a 30-day late-fulfillment performance plan? | reviewed MySQL specification, R validation of the committed export, Excel evidence, three-AI review, decision brief |
| Bitcoin Proxy Analysis | Which public Bitcoin proxies, if any, are preferable to owning Bitcoin directly? | scenario model, executed notebook, internal QA checks, report and presentation |
The workflow repository explains the method; the case-study repositories show the method applied.
SQL, R, Tidyverse, interpretable statistical modeling, Excel reporting and automation, dashboards, and AI-assisted analytical validation.