Code
MGFE MIS 6224
Niveau
M2
Discipline
Systèmes d’information
Langue
Anglais/English
Crédits ECTS
2
Heures programmées
16
Charge totale étudiant
40
Coordonnateur(s)
Département
- Data analytics, Économie et Finances
- Technologies, Information et Management
Equipe pédagogique
Introduction au module
This course introduces students to business intelligence systems used to support financial decision-making. It focuses on the design and implementation of a decision-support tool through a case-study project. Students learn how to analyse a company’s decision-making needs, distinguish transactional systems from decision-support systems, design a decision-oriented database environment, build a Business Objects universe, and produce dashboards that facilitate managerial and financial decision-making.
Bloc de compétences
- 1. S’approprier les usages avancés et spécialisés des outils de l’intelligence digitale en s’assurant de leur impact durable et responsable
Compétences du bloc
- 1.2 - Actionner les outils de l'intelligence digitale de manière efficiente, pour accompagner les transformations sociétale, numérique, énergétique et environnementale des organisations, en s'assurant de leur impact durable et responsable.
Objectifs d'apprentissage du cours
By the end of this PGE M2 course, students will be able to:
Distinguish transactional information systems from decision-support systems by identifying their purposes, data structures and uses in organisations.
Analyse a company’s decision-making needs in order to define the scope and objectives of a business intelligence project.
Apply the main stages of a decision-support project, from needs analysis to the design and implementation of decision-support outputs.
Design a decision-oriented database structure adapted to financial and managerial reporting needs.
Build and use a Business Objects universe to structure, organise and make data available for analysis.
Query a decision-support database and produce dashboards that support financial decision-making.
Evaluate the relevance of a business intelligence solution by assessing its alignment with user needs, data availability and decision-making objectives.
Contenu : structure du module et agenda
Introduction to Business Intelligence Systems
1.1 Purpose of business intelligence systems
1.2 Decision-support tools and financial decision-making
1.3 Differences between transactional systems and decision-support systems
Decision-Support Project Methodology
2.1 Analysing a company’s decision-making needs
2.2 Defining the scope of a business intelligence project
2.3 Identifying users, expected outputs and decision-making objectives
2.4 Main stages of a decision-support project
Design of a Decision-Oriented Database
3.1 Principles of decision-oriented data modelling
3.2 Construction of a decision-support database
3.3 Data organisation for reporting and analysis
3.4 Design of a Business Objects universe
Business Objects Suite
4.1 Introduction to the Business Objects environment
4.2 Designer module
4.3 Reporter module
4.4 Querying and manipulating data in Business Objects
Dashboards for Financial Decision-Making
5.1 Selection of relevant indicators
5.2 Construction of dashboards
5.3 Interpretation of dashboard outputs
5.4 Use of dashboards to support financial and managerial decisions
Applied Case Study Project
6.1 Analysis of a company need
6.2 Design of the corresponding decision-support universe
6.3 Development of dashboards
6.4 Presentation and evaluation of the proposed BI solution
Contribution à l'atteinte des ODD (Objets du Développement Durable)
SDG 9 – Industry, Innovation and Infrastructure: This course contributes to SDG 9 by developing students’ ability to design and use business intelligence systems that support organisational innovation, data-driven decision-making and more reliable digital infrastructures.
Nombre d'ODD abordés parmi les 17
1
Apprentissage
synchrone
Méthode pédagogique
The course combines lectures and exercises. Theoretical sessions introduce the concepts, methods and tools used in business intelligence and decision-support systems. Practical sessions include exercises and manipulations using Business Objects, allowing students to progressively apply the concepts covered in class. The course is organised around a case-study project in which students analyse a company’s decision-making needs, design the corresponding decision-support universe and develop dashboards to support financial decision-making.
Système de notation et modalités de rattrapage
The assessment evaluates students’ ability to understand and apply the stages of a business intelligence project. It measures their capacity to analyse decision-making needs, design a decision-support system, query a decision-oriented database and produce dashboards adapted to financial and managerial decision-making.
The final grade is composed of:
Team project: 40% of the final grade. The project is based on a case study and assesses students’ involvement, investment and ability to analyse decision-making needs and design an adapted BI solution.
Individual exercise during the final session: 60% of the final grade. This exercise assesses students’ capacity to design and query a decision-oriented database.
Unjustified absences will result in a penalty equal to 20% of the module grade.
The second-chance assessment, CF2, consists of an individual project to be completed in case of resit.
Règlement du module
. Professor–Student Communication
The professor will communicate with students through their institutional school email address (IMT-BS/TSP) and/or the Moodle portal. No communication will be sent to personal email addresses. Students are responsible for regularly checking their IMT-BS/TSP mailbox and Moodle announcements.
Students may contact the professor by email using the professor’s institutional email address. When necessary, students may meet the professor during office hours or by appointment.
2. Students with Accommodation Needs
Students who have a disability or any specific accommodation need that may affect their ability to complete the required work must inform the professor during the first class, in order to facilitate the necessary arrangements in accordance with the school’s applicable procedures.
3. Class Attendance and Behaviour
Students are expected to attend class, arrive on time and behave respectfully throughout the session.
Unless explicitly authorised by the professor, the use of electronic devices, including computers, mobile phones and tablets, is prohibited during class. Students are not allowed to take photos, videos or audio recordings in the classroom without the professor’s explicit consent.
Students must avoid disruptive or disrespectful behaviour, including arriving late, leaving early, sleeping, reading non-course material, using inappropriate language, speaking over others, eating or drinking during class, or engaging in any behaviour that disturbs the class. A warning may be given for a first violation. Repeated violations may lead to penalties, exclusion from the class and/or disciplinary proceedings.
A delay of up to 10 minutes is tolerated at the beginning of class. Attendance will be recorded on Edusign during this 10-minute period using a QR code provided by the professor at the start of each session.
Leaving the classroom before the end of the session without the professor’s approval will be considered an absence.
In the case of remote learning, students must keep their camera on unless instructed otherwise by the professor.
4. Exams and Assessments
Students must arrive on time for exams and other assessments. A delay of up to 10 minutes is tolerated.
No student may continue the exam once the allocated time is over. No student may leave the room during an examination unless they have finished the exam and handed in all required documents.
Only the following items are allowed during exams:
pens;
student card;
a non-programmable calculator, or a programmable calculator with activated EXAM mode.
All other items are prohibited.
Possession of any unauthorised electronic device, even if turned off, will be considered cheating.
Possession of a programmable calculator without activated EXAM mode, even if turned off, will be considered cheating.
A random check may be carried out after students are instructed to activate EXAM mode. Failure to prove that EXAM mode has been activated after this instruction will be considered cheating.
Students are responsible for knowing how to activate EXAM mode on their calculator before the exam.
Any violation of the instructions given by the professor or the examination supervision team will be reported to the Discipline Committee.
Références obligatoires et lectures suggérées
JM GOUARNE, « Le projet décisionnel : enjeux, modèles, architectures du Datawarehouse » - Eyrolles, 1998
SANDOVAL, « L'informatique décisionnelle » - Edition Hermès, 1997
Site Web de Michel VOLLE : http://www.volle.com/rapports/siad.htm
Sharda, R., Delen, D. and Turban, E., Business Intelligence, Analytics, and Data Science: A Managerial Perspective.
Kimball, R. and Ross, M., The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling.
Kimball, R., Ross, M., Thornthwaite, W., Mundy, J. and Becker, B., The Data Warehouse Lifecycle Toolkit.
Inmon, W. H., Building the Data Warehouse.
Moss, L. T. and Atre, S., Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications.
Shmueli, G., Bruce, P. C., Gedeck, P. and Patel, N. R., Data Mining for Business Analytics.
Howson, C., Successful Business Intelligence: Unlock the Value of BI and Big Data.
Few, S., Information Dashboard Design: Displaying Data for At-a-Glance Monitoring.
Mots-clés
Business intelligence; decision-support systems; financial decision-making; transactional systems; decision-oriented database; data warehouse; Business Objects; universe design; dashboards; reporting; data querying; financial indicators; case study; BI project methodology.
Prérequis
None