BIP program: Data - Driven Decision Making
online session: 10th March 2026
6th to 10th April 2026;
Venue: Prague University of Economics and Business
Organized by:
Prague University of Economics and Business
University of Maribor
Université Grenoble Alpes
Duration: 5 days / 6th to 10th April 2026; (online session: 10th March 2026)
Overview: This course is designed as an experiential learning module focused on analytical decisionmaking in a business context. Rather than teaching a specific analytical tool in isolation, the course emphasizes the process of analyzing a problem, formulating meaningful analytical questions, and using data to support decisions.
A simulation-based environment is used to create realistic decision-making pressure and to generate data for analysis. Through repeated cycles of experience, reflection, and application, participants gradually move from intuitive decision-making to a structured, data-informed approach.
The course is suitable for participants with diverse backgrounds and prior experience. No advanced technical knowledge is required; the focus is on thinking with data, not on programming or advanced statistics.
Course Objectives:
The course aims to develop participants’ ability to make informed, data-driven decisions in complex and uncertain business environments. Rather than focusing on mastery of a specific tool, the course emphasizes analytical reasoning and problem formulation.
Specifically, the course objectives are to:
- Develop the ability to analyze business problems and translate them into meaningful analytical questions.
- Teach participants how to formulate, refine, and evaluate analytical questions based on available data.
- Demonstrate how analytical questions influence data modeling, analysis, and interpretation.
- Build awareness of the limitations of analytical tools and AI, and the role of human judgment in decision-making.
- Enable participants to use analytical outputs to support and justify business decisions.
- Foster collaborative analytical work and critical discussion within diverse teams.
Target Audience:
This course is intended for participants who are interested in understanding and improving decision-making with data, regardless of their prior technical background.
The course is suitable for:
- Students from business, economics, data science, information systems, or related fields.
- Participants with varying levels of experience in data analysis and business analytics.
- Individuals seeking to strengthen their analytical thinking and problem-formulation skills rather than tool-specific expertise.
No advanced knowledge of statistics, programming, or business intelligence tools is required. Basic computer literacy and an interest in analytical problem-solving are sufficient.
Learning Outcomes:
By the end of the course, participants will be able to:
- Analyze a business problem and distinguish between business questions and analytical questions.
- Formulate clear, relevant, and actionable analytical questions based on available data.
- Understand how analytical questions shape data modeling, analysis, and interpretation.
- Apply data analysis tools to support decision-making in a structured and repeatable way.
- Reflect on the limitations of tools (including AI) and recognize the importance of human judgment in analytical work.
- Collaborate effectively in teams when analyzing problems and making decisions under uncertainty.
Course Structure:
The course is structured as a progressive learning cycle combining simulation-based experience, reflection, and applied analysis.
Participants move through repeated phases of:
- Experiential decision-making in a simulated business environment, where decisions must be made under uncertainty and time pressure.
- Reflection and articulation, during which participants identify information gaps, assumptions, and challenges encountered during decision-making.
- Formulation of analytical questions, both individually and in teams, followed by peer review and refinement across teams.
- Data modeling and analysis, where analytical questions are translated into structured data representations and analytical outputs.
- Validation and reflection, in which participants test their analytical approach in a new decision cycle and evaluate its effectiveness.
This structure ensures that learning is iterative and cumulative, with each phase building on insights from the previous one.
Course Schedule
BIP program: Data - Driven Decision Making
online session
10th March 2026 / online
Objective:
Set expectations, explain the learning framework, and prepare participants for collaborative and experiential work.
Content (approx. 60–75 minutes):
- Introduction to the course and its learning logic (experience → questions → data → decisions)
- Role of simulation, analytical tools, and AI
- What the course is not (tool training, finding a single correct solution)
- Practical and organizational information
- Q&A
After the session:
- Distribution of a diagnostic questionnaire focusing on:
o prior experience with simulations,
o data analysis and BI tools,
o AI-supported work.
- The questionnaire is used only for team formation, not for assessment.
BIP program: Data - Driven Decision Making
6th to 10th April 2026
Venue: Prague University of Economics and Business
6th April 2026/ Monday (18:00 – 20:30)
Introductory to the course
18:00–20:30 | Introductory Academic and Social Session
Objective:
Build trust, establish heterogeneous teams, and reduce cognitive load before the first teaching day.
- Context and Course Framing (approx. 20 minutes)
- Official opening of the BIP
- Purpose of the course and expected learning approach
- Importance of diversity in analytical decision-making
- Academic Introduction Across Institutions (approx. 60 minutes)
- Guided activity in mixed temporary groups
- Participants introduce:
o their country and city,
o home university,
o field of study,
o typical approach to data or decision-making (if applicable).
- Focus on different academic and cultural contexts, not formal presentations.
- Team Formation and Collaboration Framework (approx. 45–60 minutes)
- Announcement of final course teams (based on the questionnaire)
- Reflection on team diversity
- Agreement on basic principles of collaboration and communication
- Closing (approx. 10 minutes)
- Overview of Tuesday’s activities
- Emphasis that the first simulation is about experience, not performance
7th April 2026/ Tuesday (9:00 – 16:00)
Experience, AI, and Analytical Questions
9:00–10:00 | University Tour
10:00–10:30 | Course Kick-off
- Reiteration of the course framework
- Explanation of AI usage in the first simulation (freestyle, unrestricted)
- Emphasis on learning through experience rather than winning
10:30–12:30 | Simulation 1 – AI supported
Objective:
- Create an initial decision-making experience
- Allow intuitive and AI-supported strategies
- Expose uncertainty, assumptions, and lack of structure
12:30–13:30 | Lunch Break
13:30–14:30 | Reflection on Decision-Making and AI Support
Focus:
- How was AI used during the simulation?
- In what ways did AI help?
- Where did AI fail or mislead?
- Which assumptions were implicitly introduced by AI?
- What could not be verified or repeated?
Objective:
- Make the limitations of AI explicit in the absence of clear analytical questions
- Distinguish between answers, understanding, and responsibility
14:30–16:00 | Formulating Analytical Questions (Work → Team → Exchange)
- Individual formulation of analytical questions
- Team-level synthesis
- Exchange of questions between teams and peer feedback
8th April 2026/ Wednesday (9:15 – 16.30)
From Questions to Data and the Second Simulation
9:15–11:00 | Revision of Questions and First Generalization
- Discussion of question quality
- Identification of common strengths and weaknesses
- Initial formulation of principles for good analytical questions
11:15–12:45 | Problem Analysis and Data Modeling
- Translating questions into:
o facts,
o dimensions,
o relationships
- Preparation of an analytical framework
12:45–13:45 | Lunch Break
13:45–15:45 | Simulation 2 – Questions and Tools
Objective:
- Test the quality of analytical questions
- Validate data models and analytical tools
- Observe how analysis supports decision-making
15:45–16:30 | Short Reflection on the Second Simulation
- Which questions worked in practice?
- Which failed or were insufficient?
- What did not scale or transfer well?
9th April 2026/ Thursday (9:15–12:30)
Operationalization and Synthesis
9:15–12:30 | Power BI and Dashboards
- Translating analytical questions into dashboards
- Focus on clarity, repeatability, and decision support
- Preparation for final validation
Afternoon | Tour to the Prague castle
- Guided tour to the Prague castle
10th April 2026/ Friday (9:15–12:30)
Validation and Reflection
9:15–11:15 | Final Simulation – Validation
Objective:
- Validate the stability of the analytical approach
- Test transfer of learning to a new situation
11:15–12:30 | Final Reflection and Generalization
- What changed in participants’ approach to data and decisions?
- Which principles are transferable beyond the simulation?
- Summary of key learning outcomes
