Code
MGYF STR 5941
Level
M2
Field
Stratégie
Language
Français/French
ECTS Credits
2
Class hours
28
Total student load
40
Program Manager(s)
Department
- Management, Marketing et Stratégie
Educational team
Introduction to the module
In the era of multiple transitions—digital, ecological, and geopolitical—the strategic leadership of companies must transform massive data into sustainable competitive advantages. This Strategic Data Analysis course trains future Data Strategists to diagnose disruptions, map ecosystems, and anticipate weak signals using free tools and public data. Over 4 intensive days, students will master core functions of Power BI, Data Studio, and Google Trends to apply classic frameworks—Porter's Five Forces, data-driven SWOT, quantitative PESTEL, S-curves—to concrete French mini-cases. The course aligns data analysis skills with priority SDGs (9.3 Innovation, 7.2 Affordable and Clean Energy, 13.2 Climate Action). At the end, each student will deliver an interactive dashboard and pitch their analysis and recommendations in a data-driven decision-making context. This course contributes to training hybrid managers combining strategic data literacy and transitional vision for French competitiveness in 2030.
Learning objectives/Intended learning outcomes
- 6.3 - Produce and analyse key summary documents to ensure optimal, sustainable management, ensuring alignment with the organisation's vision, mission and values.
Rubrics
Data Literacy Stratégique : Maîtrise des cadres classiques (Porter 5 Forces, SWOT, PESTEL) appliqués à des datasets réels pour diagnostiquer les transitions sectorielles.
Visualisation Executive : Conception de dashboards interactifs (Power BI, Data Studio) traduisant analyses complexes en recommandations.
Anticipation Disruption : Analyse prédictive S-curves (Google Trends), brevets (Orbis/Google Patents), écosystèmes (Gephi) pour identifier signaux faibles.
Storytelling Data-Driven : Transformer insights data (market share, R&D spend, subventions) en pitch décisionnel aligné ODD/RSE
Content : structure and schedule
Sessions 1-2: Strategic Data Foundations + Case Study: Renault vs Tesla – Electric Vehicle Battle in Europe (2020-2025) ODD 7.2
Sessions 3-4: Global Competitiveness & Ecosystems + Case Study: Airbus Strategic Capabilities (2018-2025) ODD 9.3
Sessions 5-6: Macro & Sectoral Transitions + Case Study: Renewable Energies France – Quantitative PESTEL Diagnosis (2015-2025) ODD 7.2 / 13.2
Sessions 7-8: Decision & Strategic Action + Case Study: AI Adoption in French Retail (2020-2025) ODD 9.3
Sustainable Development Goals
ODD 7, 9, 13
Justification for SDGs 7, 9 & 13
Sessions 1–2: Strategic Data Foundations + Renault vs Tesla Case (SDG 7.2)
Foundations in data strategy and the Renault-Tesla electric vehicle (EV) battle in Europe (2020–2025) directly target 7.2 ("substantially increase renewable energy share").
Data-driven Porter/SWOT analyses reveal EV ecosystem shifts, supporting clean energy adoption and energy efficiency.
This equips students to quantify transitions toward sustainable mobility.
Sessions 3–4: Global Competitiveness & Ecosystems + Airbus Case (SDG 9.3)
Focus on capabilities and ecosystems via Airbus (2018–2025) aligns with 9.3 ("increase SME access to financial services/markets, integrate into value chains").
Quantitative PESTEL/S-curves map industrial innovation, fostering resilient infrastructure for aerospace competitiveness.
Students build dashboards highlighting technological upgrading for sustainable industrialization.
Sessions 5–6: Macro/Sectoral Transitions + Renewables PESTEL Case (SDG 7.2 & 13.2)
Quantitative PESTEL on French renewables (2015–2025) supports 7.2 (renewable energy growth) and 13.2 ("integrate climate measures into policies/planning").
Trend analysis anticipates signals in energy/climate shifts, enabling data-informed adaptation strategies.
This links energy security with climate resilience.
Sessions 7–8: Strategic Decision & Action + AI Retail Adoption Case (SDG 9.3)
AI adoption in French retail (2020–2025) reinforces 9.3, emphasizing SME innovation via data ecosystems.
Interactive dashboards and pitches demonstrate how AI signals drive scalable, sustainable retail transformation.
Final outputs promote industrial agility in transition contexts.
SDGs 7, 9 & 13 anchor the course's hybrid data-strategic training: participants deliver actionable insights for France's 2030 competitiveness, turning big data into tools for clean energy (7), innovative infrastructure (9), and climate action (13).
Number of SDG's addressed among the 17
3
Learning delivery
synchrone
Pedagogical methods
Seminar / lecture(s): Yes
Case study(ies): Yes
Practical work : Yes
Presentation(s): Yes
Online learning component: Yes
Coaching: Yes
Debate(s): Yes
Evaluation and grading system and catch up exams
Group assessment: 40% Case studies
Individual Assessment: 60% (Theory questions / Mini case study)
The final grade may be adjusted based on individual participation (lateness, unexcused absences, etc.).
Resit (CF2): Theory questions / Mini case study. The grade is capped at 10/20.
In the event of failure to pass the resit (CF2), an extension of studies may be granted by the academic board.
Module Policies
Professor-Student Communication
● The professor will contact the students through their school email address (IMT-BS/TSP) and the Moodle portal. No communication via personal email addresses will take place. It is the student responsibility to regularly check their IMT-BS/TSP mailbox.
● Students can communicate with the professor by emailing him/her to his institutional address. If necessary, it is possible to meet the professor in his office during office-hours or by appointment.
Students with accommodation needs
If a student has a disability that will prevent from completing the described work or require any kind of accommodation, he may inform the program director (with supporting documents) as soon as possible. Also, students are encouraged to discuss it with the professor.
Class behavior
● Out of courtesy for the professor and classmates, all mobile phones, electronic games or other devices that generate sound should be turned off during class.
● Students should avoid disruptive and disrespectful behavior such as: arriving late, leaving early, careless behavior (e.g. sleeping, reading a non-course material, using vulgar language, over-speaking, eating, drinking, etc.). A warning may be given on the first infraction of these rules. Repeated violators will be penalized and may face expulsion from the class and/or other disciplinary proceedings.
Learners are expected to strictly adhere to the class schedule provided in their timetable.
● The tolerated delay is 5 minutes. Attendance will be declared on Moodle during these 5 minutes via a QR code provided by the teacher at each course start.
Each half-day of unexcused absence or lateness will result in a penalty of -1 point on the final course grade.
● Student should arrive on time for exams and other assessments. No one will be allowed to enter the classroom once the first person has finished the exam and left the room. There is absolutely no exception to this rule. No student can continue to take an exam once the time is up. No student may leave the room during an examination unless he / she has finished and handed over all the documents.
● In the case of remote learning, the student must keep his camera on unless instructed otherwise by the professor.
Honor code
IMT-BS is committed to a policy of honesty in the academic community. Conduct that compromises this policy may result in academic and / or disciplinary sanctions. Students must refrain from cheating, lying, plagiarizing and stealing. This includes completing your own original work and giving credit to any other person whose ideas and printed materials (including those from the Internet) are paraphrased or quoted directly. Any student who violates or helps another student violate academic behavior standards will be penalized according to IMT-BS rules.
Textbook Required and Suggested Readings
Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
Keywords
Data-Driven Strategy , Strategic Analytics , Business Intelligence , Disruption, Forecasting , Competitive Data Analysis