Last updated: March 26, 2026
BADM 557 - Business Intelligence
Program-level details: See program/curriculum.md
Status: Under Revision Gautam is repositioning this course as frameworks-to-insights (management theory applied to data via AI), not BI tools instruction. Week-by-week outline pending from Gautam. Current page reflects prior approach and will be substantially rewritten. (Mar 25, 2026) Proposed MSBAi name: Business Intelligence with AI — pending formal rename approval
| Credits: 4 | Term: Summer 2027 (Weeks 1-8) | Instructor: Gautam |
Course Vision
Students apply management frameworks (Porter’s Five Forces, value chain analysis, RBV, etc.) to real business data using AI as the primary analytical tool. The course is frameworks-first, not tools-first: students learn to ask the right business questions grounded in theory, then use AI-assisted workflows to find answers in data and present insights to executives. AI has made traditional BI tool instruction (manual Tableau/Power BI) less central — the course focuses on conceptual frameworks and business insight generation.
Recording timeline: May 2026 and July 2026 (Gautam developing and delivering solo).
Key assumptions about students: By Summer 2027, students will have completed BADM 554, BDI 513, and FIN 550 — they are fluent in GitHub, Python, VS Code + Copilot, and Colab. No tool onboarding needed.
Learning Outcomes (L-C-E Framework)
Literacy (Foundational Awareness)
- L1: Explain what business intelligence is and distinguish BI from analytics
- L2: Identify stakeholder questions that data can answer
- L3: Recognize ethical issues in data analysis (bias, privacy, misrepresentation)
Competency (Applied Skills)
- C1: Use data to answer business questions (find root causes, forecast, segment)
- C2: Build a BI dashboard that executives would actually use
- C3: Present BI insights and recommendations in executive-ready format
- C4: Apply basic classification and K-means clustering for business segmentation decisions
Expertise (Advanced Application)
- E1: Design an end-to-end BI solution for a business problem
- E2: Combine multiple data sources into coherent insights
- E3: Recommend business actions based on analytical findings
Week-by-Week Breakdown
| Week | Topic | Lectures | Project Work | Studio Session | Assessment |
|---|---|---|---|---|---|
| 1 | BI fundamentals + case analysis | 2 videos | Team formation + domain selection | BI principles - what questions can data answer | Weekly: Case quiz |
| 2 | Analytics lifecycle + hypothesis testing | 2 videos | Data exploration for team domain | Hypothesis testing workshop - A/B test, significance | Weekly: Case write-up · Milestone: Project proposal |
| 3 | Classification + decision-making | 2 videos | Build classifier on team data | Classification in practice - how to interpret for decisions | Weekly: Classification exercise |
| 4 | Dashboard design + Power BI deep-dive | 2 videos | Power BI setup + team dashboard | Power BI for executives - what dashboards should show | Weekly: Dashboard mockup · Milestone: Draft dashboard |
| 5 | Real-world BI challenges | 2 videos | Build out dashboard + data quality | Data quality + governance - what goes wrong in practice | Weekly: Quality assessment |
| 6 | Clustering + segmentation | 2 videos | Team segmentation analysis | Customer segmentation - clustering for business decisions | Weekly: Segment memo · Milestone: Segment analysis |
| 7 | Business communication + storytelling | 1 video | Finalize deliverables + rehearse | Executive communication - clear, concise, actionable | Weekly: Draft presentation |
| 8 | Synthesis + case presentations | – | Final prep + reflection | Live case presentations - peer Q&A | Final project + Oral defense |
Team Project: Capstone BI Solution (Team of 3, spans Weeks 1-8)
Problem Statement: Design and execute an end-to-end BI solution for a real business problem. Teams select a domain in Week 1, build toward it through weekly assignments and milestones, and deliver a complete BI package with oral defense in Week 8.
Domain Options:
- E-commerce: Customer segmentation + churn dashboard
- Finance: Portfolio analytics + risk dashboard
- Healthcare: Patient segmentation + outcomes dashboard
- Retail: Sales analytics + inventory dashboard
- Instructor-provided real data: Use current business problem
- Team choice (approved)
Weekly Assignments (35%)
Individual assignments that build foundational skills and feed into the team project.
| Week | Assignment | Format |
|---|---|---|
| 1 | Case quiz: BI fundamentals + case analysis | Quiz (Canvas) |
| 2 | Case write-up: hypothesis testing on team’s chosen domain (2 pages) | Written memo |
| 3 | Classification exercise: build classifier on case data, interpret for decisions | Notebook + short memo |
| 4 | Dashboard mockup: wireframe team dashboard layout + KPI selection | Power BI mockup + 1-page rationale |
| 5 | Data quality assessment: audit team’s data sources, document issues + fixes | Written memo (2 pages) |
| 6 | Segment memo: individual clustering analysis on team dataset, business implications | Notebook + memo (2 pages) |
| 7 | Draft presentation: individual contribution summary + talking points | Slide deck draft |
Rubric (4 dimensions):
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| Business Understanding | Deep understanding of context + constraints | Good understanding | Surface-level reading |
| Analytical Rigor | Systematic exploration, clear insights | Adequate exploration | Shallow analysis |
| Communication | Professional, clear, executive-ready | Adequate write-up | Unclear or incomplete |
| AI Attribution | Proper disclosure of AI use with reflection | AI use noted | Missing or vague attribution |
Project Milestones (25%)
Team deliverables that build toward the final project.
| Week | Milestone | Deliverable |
|---|---|---|
| 2 | Project proposal | 1-page proposal: business problem, data sources, key questions, team roles |
| 4 | Draft dashboard | Power BI dashboard (3-5 visualizations), data exploration notebook, preliminary findings |
| 6 | Segment analysis | Clustering analysis (K-means or hierarchical), segment profiles (size, characteristics, behavior), business implications memo (2-3 pages) |
Rubric (4 dimensions):
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| Progress | On track, clear trajectory toward final deliverable | Adequate progress | Behind or unfocused |
| Technical Quality | Rigorous approach, appropriate methods | Functional work | Technical issues |
| Team Coordination | Clear evidence of shared work and planning | Adequate collaboration | Uneven contribution |
| Incorporation of Feedback | Substantively addresses prior feedback | Some adjustments | Ignores feedback |
Final Project Deliverable (15%)
The culminating team submission in Week 8.
Deliverables:
- Power BI dashboard (5-8 interactive visualizations showing KPIs, trends, segments)
- Executive brief (1 page: situation, 3 key findings, recommendation)
- Detailed analysis (5-7 pages):
- Problem statement + business context
- Segmentation findings
- Key insights + root causes
- Recommended actions + expected impact
- Limitations + risks
- Implementation roadmap
- Peer evaluation of team contributions
- GitHub repo with all code + dashboards
Rubric (5 dimensions):
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| Dashboard | Executive-ready, clear story, interactive | Functional, mostly clear | Needs polish |
| Analysis | Deep insights, data-driven recommendations | Good analysis | Shallow or missing insights |
| Business Acumen | Understands constraints, realistic recommendations | Shows business sense | Generic or impractical |
| Documentation | Clear guide for stakeholders, professional formatting | Adequate explanation | Minimal docs |
| Technical Rigor | Advanced Power BI features, robust clustering, clean code | Standard features used well | Basic functionality |
Oral Defense (20%)
Team oral defense: 15-min live presentation to mock leadership team + Q&A. Each team member must answer questions individually.
Rubric (4 dimensions):
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| Presentation Clarity | Confident, clear narrative, well-structured | Adequate delivery | Unclear or disorganized |
| Q&A Handling | Handles tough questions well, articulates trade-offs | Reasonable responses | Unprepared or evasive |
| Individual Contribution | Can speak to any part of the project with depth | Knows own section | Limited understanding |
| Business Judgment | Realistic recommendations, acknowledges limitations | Shows business sense | Generic or impractical |
AI Tools Integration
Weekly Assignments (Weeks 1-7):
- Use Claude/ChatGPT to:
- Explain business context from cases
- Suggest features for classification model
- Interpret classifier results
- Generate memo structure + language
- Suggest Power BI calculation syntax
Project Milestones (Weeks 2, 4, 6):
- Use AI to:
- Suggest dashboard design (what charts for what questions)
- Create segment profiles and narratives
- Review dashboard usability
- Refine proposal framing
Final Project + Oral Defense (Weeks 7-8):
- Use AI to:
- Refine recommendations for clarity
- Draft executive brief language
- Generate presentation slides
- Practice Q&A scenarios
Studio Session Topics:
- Week 1: BI principles + analytics lifecycle
- Week 2: Case analysis strategy + asking the right questions
- Week 3: Classification for business decisions
- Week 4: Dashboard design best practices + Power BI architecture
- Week 5: Real-world BI challenges + data quality issues
- Week 6: Customer segmentation + actionable clusters
- Week 7: Executive communication + presenting to leadership
- Week 8: Live presentations + peer feedback
Assessment Summary
| Component | Weight | Notes |
|---|---|---|
| Weekly assignments | 35% | Individual; case quizzes, write-ups, mockups, memos |
| Project milestones | 25% | Team; proposal, draft dashboard, segment analysis |
| Final project deliverable | 15% | Team; dashboard + executive brief + full analysis |
| Oral defense | 20% | Team; 15-min presentation + individual Q&A |
| Studio participation | 5% | Weekly attendance + peer feedback |
No traditional exam. One major team project with weekly individual assignments building toward it.
AI Usage Levels (AIAS)
| Assessment | AIAS Level | AI Permitted |
|---|---|---|
| Weekly assignments | 2 | AI for business context, classifier suggestions, memo structure, Power BI syntax — with attribution |
| Project milestones | 2 | AI for data exploration, dashboard design suggestions, segment narrative drafting — with attribution |
| Final project deliverable | 3 | AI as collaborator for executive brief and recommendation refinement — with full disclosure |
| Oral defense | 0 | No AI |
| Studio participation | 1 | AI for exploration during exercises |
Technology Stack
- BI Tool: Power BI Desktop (academic license)
- Analytics: Python (scikit-learn, pandas, seaborn)
- Data: HBS cases datasets + public business data
- Clustering: scikit-learn (K-means, hierarchical)
- IDE: VS Code with GitHub Copilot; Google Colab (browser alternative)
- Notebooks: Jupyter Notebooks (via Colab or VS Code)
- Version Control: GitHub
Prerequisites
Bridge Module: Case Method Primer (Pre-Course, ~2 hours)
Complete before Week 1. Available in Canvas as a self-paced module. Designed for students who have not previously analyzed Harvard Business School cases or similar business case studies.
| Unit | Topics | Format | Self-Check |
|---|---|---|---|
| 1. What Is a Business Case? (30 min) | How cases differ from textbooks, the role of the protagonist, why there’s no “right answer,” how to read a case efficiently | Short video + annotated sample case | Quiz: identify the protagonist, decision point, and key constraints in a sample case |
| 2. Structuring Your Analysis (45 min) | Frameworks for case analysis (situation-complication-resolution, MECE), separating facts from assumptions, identifying what data you need | Worked example with a short practice case | Quiz: write a 1-paragraph problem statement for a provided case |
| 3. Writing a Business Memo (45 min) | Memo structure (recommendation first, then supporting evidence), professional tone, how to present data findings to executives | Template + before/after examples | Quiz: rewrite a poorly structured memo into professional format |
Readiness check: Students who pass all 3 self-check quizzes (70% threshold) are ready for Week 1. Students with MBA or business case experience may skip this module.
| Course Sequence: ← General Elective | Next: BADM 576 — Data Science and Analytics → |