This course equips students with the conceptual frameworks and practical skills to apply data-driven approaches across the major functional areas of business management and economics. Drawing on real-world business cases and a strategic management simulation, students develop the ability to harness data as a driver of competitive advantage, operational efficiency, and sustainable value creation in complex organizational settings.

By the end of the course, students will be able to: 1. Apply data-driven analytical frameworks across multiple management and economics domains, including marketing, finance, HR, supply chain, and sustainability. 2. Critically analyze real-world business cases through a data-driven lens. 3. Translate data insights into strategic and operational recommendations. 4. Design and evaluate business experiments to support evidence-based decision-making. 5. Participate effectively in team-based data analytics simulations. 6. Communicate data-based findings clearly to managerial and non-specialist audiences. 7. Identify ethical and governance considerations related to the use of data in organizational decision-making.

Data-Driven Management explores how analytics and data shape decision-making across all major areas of business. The course is structured around 12 lectures, each dedicated to a specific functional domain, anchored by a real-world case study. A business analytics simulation on strategic decision-making is integrated in Week 6. Topics covered: 1. Microeconomics: consumer behavior and market data 2. Macroeconomics and economic indicators 3. Finance: data science in financial decision-making 4. Marketing: big data and customer segmentation 5. Human Resource Management: people analytics 6. Strategic Management: data and competitive advantage + Simulation 7. Supply Chain Management: AI and visual intelligence in selected industries 8. Planning and Strategic Vision: long-term data-driven transformation 9. Entrepreneurship and Innovation: AI-first business models 10. Behavioral Economics: business experiments and consumer behavior 11. Operations and Technology Management: analytics in operations 12. Sustainability and CSR: ESG data and reporting

A dedicated case pack is available via the course platform. Required pre-class readings are assigned for each of the 12 sessions and must be completed before class. A business analytics simulation on strategic decision-making is used in Week 6 (in-class, team-based). Supplementary slides and materials will be shared on the course platform before each session.

The course adopts an active learning approach centered on the analysis of real-world business cases and a management simulation: 1. Lectures: Each session begins with a brief conceptual introduction to the relevant data-driven management framework for the week's topic area. 2. Case study discussions: A new case study is assigned for each of the 12 sessions. Students are expected to read and prepare the case before class. In-class discussion focuses on identifying the data-driven dimensions of the managerial decisions featured, applying analytical frameworks, and drawing broader lessons for management practice. 3. Team-based simulation (Week 6 - Strategic Management): A business analytics simulation is run in teams. Students act as competing managers, make data-driven decisions in a simulated business environment, and debrief collectively on the outcomes. 4. Active class participation: Students are encouraged to contribute analytical perspectives to case discussions, make connections across weekly topics, and engage critically with the data-driven frameworks presented throughout the course. Preparation requirement: Prior to each class, students must complete the assigned case study reading. Preparation questions to guide analysis are available on the course platform before each session.

The course follows the SMART educational model adopted by Luiss Graduate School for the 2025/2026 cohort onward. Assessment varies based on attendance status, recorded via the BEACON system. COMPLIANT STUDENTS (>=70% attendance): Continuous Assessment - 1/3 of the overall grade: Mandatory in-semester activities. Absence from any activity results in a grade of 0 for that component, included in the overall grade calculation. The grade obtained cannot be rejected. Activities: (i) individual case preparation checks and class participation, assessed on a rolling basis throughout the semester; (ii) one team-based analytical presentation of an assigned case study, delivered during class. Final Exam - 2/3 of the overall grade: Individual written exam administered during the officially scheduled exam dates at the end of the semester, covering all course topics. The grade obtained cannot be rejected. Note: The continuous assessment + final exam combination is valid only in the exam session at the end of the semester. In subsequent retake sessions, assessment is based on the final exam only (100%). The grade obtained cannot be rejected. NON-COMPLIANT AND/OR EXEMPTED STUDENTS: Final Exam only - 100% of the overall grade: Individual written exam starting from the exam session at the end of the semester. An extended reading list is provided to compensate for non-participation in semester activities. The grade obtained cannot be rejected. GRADE WITHDRAWAL: - Oral exam: withdrawal allowed until the professor expresses a grade, prior to registration on Web Self-Service. - Written exam followed by oral: withdrawal permitted during the written exam or the subsequent oral exam. - Written exam only: withdrawal permitted until the end of the exam. Note: "Salto d'appello" no longer applies from the 2025/2026 cohort onward. Grades cannot be rejected.

Students wishing to pursue their final dissertation in the area of Data-Driven Management may contact the course professor directly. Dissertation proposals should: - Define a clear research question related to the use of data and analytics in a specific management or economics domain covered during the course - Be grounded in real or publicly available data, ideally obtained from an organization through a collaborative arrangement - Demonstrate the ability to apply appropriate quantitative or qualitative analytical methods - Produce actionable managerial insights beyond purely descriptive analysis - Address a gap or open question in the academic or practitioner literature on data-driven management Priority in thesis supervision is given to proposals that: 1. Are based on original or proprietary organizational datasets 2. Show integration between data science methods and management theory 3. Address cross-functional topics reflecting the interdisciplinary nature of the course Eligibility: Students must have completed the course with a satisfactory grade. A minimum grade threshold may apply; please consult the course professor for current requirements. For further information or to submit a proposal, please contact the professor during office hours or via institutional email.