# Starbucks Operations Case

> **Independent educational case.** This project is not affiliated with, sponsored by, or endorsed by Starbucks Corporation. Starbucks is used nominatively to identify the company discussed. Maya Rodriguez and the engagement are fictional.

## Status

- **Local capstone status: ready.** The case, BRD, four labs, templates, dashboard, Worked Solutions, rubric, and validation gates are complete for local course use.
- **Public companion status: ready.** This student route provides the approved capstone materials and hash-pinned course inputs.

## Purpose of the Case

This case turns the disciplines in *The Defensible Decision: A Guide to AI-Assisted Business Analytics* into one connected BI assignment.

The book's premise is simple: principles first, AI later, judgment always. The Starbucks case puts that premise under pressure. You will work with observed public samples, simulated workflows, and missing operational evidence. Your job is to produce a useful recommendation without letting a polished dashboard, a convenient join, or an AI-generated explanation claim more than the data supports.

The executive decision is whether to fund **Centralized Analytics**, **Unrestricted Self-Service**, or **Governed Self-Service** for store health. The analytical challenge is to decide what each evidence source earns, what it cannot establish, and what the organization must measure next.

## Overall Objectives

The case has four overall objectives:

1. **Frame the decision before opening the tool.** Define the audience, decision, question, comparison, time window, and success condition in a Business Requirements Document.
2. **Build an evidence chain rather than a collection of charts.** Move from operations workflow, to customer signal, to join boundary, to a bounded pilot.
3. **Direct and critique AI-assisted analysis.** Use AI to accelerate exploration and drafting while keeping metric choice, interpretation, and decision rights with the analyst and reviewer.
4. **Defend an executive recommendation.** State the preferred operating model, trade-offs, risks, missing measurements, refused claims, and evidence that would change the decision.

## Learning Objectives

After completing the case, you will be able to:

1. Write a decision-ready BRD using the book's context before any chart discipline.
2. Turn a business question into a comparison, a defensible visual form, and a one-sentence takeaway.
3. Apply focus, hierarchy, and accessible labeling so evidence class, source, period, and denominator remain visible.
4. Distinguish observed evidence, simulated workflow findings, and missing data before making a recommendation.
5. Audit source relationships by grain, key, time, and selection mechanism before joining tables.
6. Use the CSAR loop to crystallize the question, scope the evidence, assemble a draft, and refine it through critique.
7. Detect misleading defaults, false causal language, unsupported AI narratives, and other reasons to ship, iterate, or discard an output.
8. Design a governed analytics model with certified measures, local investigation rights, Copilot guardrails, and consequential human review.
9. Write a provenance statement that names data sources, simulations, analytical choices, AI assistance, reviewers, and decision boundaries.
10. Present a defensible decision to a skeptical executive and state what evidence would justify revisiting it.

## How the Book Guides the Case

| Book discipline | Case application |
| --- | --- |
| **Context before any chart** | Complete the BRD before opening Power BI or Copilot. |
| **Story from data to argument** | Give every visual one decision-relevant takeaway and carry one bounded finding per lab into the executive brief. |
| **Question-to-form matching** | Choose each chart from the analytical question and comparison, not from a default gallery. |
| **Focus and accessibility** | Remove decoration, preserve hierarchy, and keep labels readable across desktop and mobile. |
| **Principles first, AI later** | Understand the measure and evidence boundary before asking AI to explain or compose it. |
| **CSAR direction** | Crystallize, Scope, Assemble, and Refine AI-assisted work instead of accepting the first draft. |
| **Prep for AI** | Define stable keys, trustworthy measures, verified answers, and AI instructions before expecting reliable responses. |
| **Dashboard composition** | Lead with the decision, limit the executive surface, and move investigation detail behind the headline view. |
| **Failure-mode review** | Check for wrong measure, wrong grain, wrong narrative, misleading scale, and unsupported certainty; then ship, iterate, or discard. |
| **Responsible AI** | Preserve provenance, examine fairness risks, disclose AI assistance, and keep consequential decisions under human authority. |

## Evidence Contract

| Evidence class | Valid use | Not valid for |
| --- | --- | --- |
| **Observed public data** | Bounded descriptions of the named sample, period, and selection mechanism | Starbucks-wide or causal conclusions |
| **Simulated data** | BI workflow practice and testable hypotheses | Claims about actual Starbucks customers or operations |
| **Missing evidence** | Defining the field, grain, key, denominator, or history needed next | Filling gaps with persuasive narrative |

Every headline finding must identify its evidence class. Every recommendation must state what the evidence earns and what it does not earn.

## Choose Your Path

| Path | Start here | Purpose |
| --- | --- | --- |
| Understand the decision | [Case](index.html?area=overview&doc=case-plan) | Read the narrative, Big Idea, learning objectives, four-lab argument, and success criteria. |
| Audit provenance | [Sources & Licenses](index.html?area=overview&doc=source-notes) | Review source, license, sample, period, evidence class, and publication boundary. |
| Complete the assignment | [Student Path](index.html?area=student-path) | Work through the BRD, data package, Labs 01-04, synthesis, and blank executive brief. |

> **Student-work boundary:** Complete the Student Path independently. A defensible submission explains its evidence, limits, and recommendation without relying on an answer key.

## Completion Standard

The case is complete when the executive can answer:

- What should I approve?
- Why does the evidence support it?
- What is observed, simulated, or still missing?
- Which comparison is useful but not causal?
- What may local teams and Copilot do, and what remains a human decision?
- Who acts next, what will they measure, and what result would change the recommendation?

The strongest submission must not disagree with its own evidence.