BADM 557 - Decision Intelligence with AI
Program-level details: See program/CURRICULUM.md
| Credits: 4 | Term: Summer 2027 (Weeks 1-8) |
Course Vision
Students master business intelligence–combining data analysis, visualization, and business intuition to solve real problems. Using cases from Harvard Business School + modern AI tools, students learn to ask business questions, find answers in data, and present insights clearly.
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: Explain analytical findings to non-technical stakeholders
- C4: Apply classifiers and clustering to business 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 | Project 1A: Case read + questions | BI principles - what questions can data answer | Case quiz |
| 2 | Analytics lifecycle + hypothesis testing | 2 videos | Project 1 work: Data exploration | Hypothesis testing workshop - A/B test, significance | Case write-up |
| 3 | Classification + decision-making | 2 videos | Project 1 work: Build classifier | Classification in practice - how to interpret for decisions | Project 1 due |
| 4 | Dashboard design + Power BI deep-dive | 2 videos | Project 2A: Power BI setup + design | Power BI for executives - what dashboards should show | Dashboard mockup |
| 5 | Real-world BI challenges | 2 videos | Project 2 work: Build dashboard | Data quality + governance - what goes wrong in practice | Quality assessment |
| 6 | Clustering + segmentation | 2 videos | Project 2 work: Segmentation analysis | Customer segmentation - clustering for business decisions | Segment memo |
| 7 | Business communication + storytelling | 1 video | Project 3A: Present findings | Executive communication - clear, concise, actionable | Draft presentation |
| 8 | Synthesis + case presentations | – | Final prep + reflection | Live case presentations - peer Q&A | Final presentations |
Projects (3 per course)
Project 1: Case Analysis + Classifier (Weeks 1-3, Individual, 25% of grade)
Problem Statement: Analyze a Harvard Business School case. Use data to make the decision the protagonist faced. Build a classifier if applicable.
Cases Available:
- Retailers (with sales data): Optimize inventory or staffing
- Financial (with market data): Predict credit risk or fraud
- Operations (with process data): Optimize supply chain or workflows
- Marketing (with campaign data): Predict customer response or churn
- Instructor-provided real data: Use current business problem
Deliverables:
- Case read + 2-page summary of business problem
- Data exploration notebook (visualizations, patterns, hypotheses)
- Classifier or regression model (if applicable to case)
- Memorandum (2-3 pages) with recommendation
- GitHub repo with analysis code
Rubric (5 dimensions):
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| Case Understanding | Deep understanding of context + constraints | Good understanding | Surface-level reading |
| Data Analysis | Systematic exploration, clear insights | Adequate exploration | Shallow analysis |
| Recommendation | Well-justified, considers trade-offs | Defensible | Unclear rationale |
| Model Quality | Rigorous approach if classifier built | Functional model | Model issues |
| Written Communication | Professional memo, clear recommendation | Adequate write-up | Unclear or incomplete |
Project 2: BI Dashboard + Segmentation (Weeks 4-6, Individual, 35% of grade)
Problem Statement: Design a comprehensive BI dashboard for a business problem. Include segmentation analysis to understand customer/market groups.
Problem Options:
- E-commerce: Customer segmentation + churn dashboard
- Finance: Portfolio analytics + risk dashboard
- Healthcare: Patient segmentation + outcomes dashboard
- Retail: Sales analytics + inventory dashboard
- Student choice (approved)
Deliverables:
- Problem definition (1 page: business context, key questions)
- Data exploration notebook (understand data quality, distributions)
- Power BI dashboard (5-8 visualizations showing KPIs, trends, segments)
- Segmentation analysis:
- Clustering (K-means or hierarchical) on customer/entity data
- Segment profiles (size, characteristics, behavior)
- Business implications of each segment
- 3-page analysis document:
- Dashboard overview + how to use it
- Segment descriptions + recommendations
- Technical approach + limitations
- GitHub repo with code + data
Rubric (5 dimensions):
| Dimension | Excellent (A) | Proficient (B) | Developing (C) |
|---|---|---|---|
| Dashboard Design | Professional, intuitive, actionable insights | Clear layout, good coverage | Cluttered or confusing |
| Segmentation | Meaningful clusters, clear business implications | Clusters identified | Limited insight |
| Power BI Proficiency | Advanced features (parameters, calculated fields) | Standard features used well | Basic functionality |
| Analysis Depth | Explores “why” behind segments and trends | Describes what | Surface observations |
| Documentation | Clear guide for stakeholders | Adequate explanation | Minimal docs |
Project 3: Capstone BI Project + Presentation (Weeks 7-8, Team of 3-4, 30% of grade)
Problem Statement: Design and execute an end-to-end BI solution as a team. Present recommendations to a mock leadership team.
Deliverables:
- Refined dashboard (5-8 interactive visualizations)
- 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
- Team oral defense: 15-min live presentation to mock leadership team + Q&A (25% of Project 3 grade)
- 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 |
| Oral Defense | Confident, clear, handles questions well, articulates trade-offs | Adequate delivery | Unclear or unprepared |
| Business Acumen | Understands constraints, realistic recommendations | Shows business sense | Generic or impractical |
| Team Collaboration | Clear evidence of shared work and coordination | Adequate collaboration | Uneven contribution |
AI Tools Integration
Weeks 1-3 (Case Analysis):
- Use Claude/ChatGPT to:
- Explain business context from case
- Suggest features for classification model
- Interpret classifier results
- Generate memo structure + language
Weeks 4-6 (Dashboard):
- Use AI to:
- Suggest dashboard design (what charts for what questions)
- Generate Power BI calculation syntax
- Create segment profiles and narratives
- Review dashboard usability
Weeks 7-8 (Capstone):
- 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 |
|---|---|---|
| Project 1 (Case Analysis) | 25% | Weeks 1-3, individual, case-driven |
| Project 2 (Dashboard) | 35% | Weeks 4-6, dashboard + segmentation |
| Project 3 (Capstone BI) | 30% | Weeks 7-8, team (includes oral defense) |
| Studio participation | 10% | Weekly attendance + peer feedback |
No traditional exam. Project-based with business focus.
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)
- Notebook: Jupyter, Google Colab
- Tools: GitHub for version control
Prerequisites
- Completion of BADM 554, BDI 513, FIN 550 (or equivalent)
- Comfortable with Python + data exploration
Last Updated: February 2026