After completing this module successfully, students will have the following professional, methodological, social, and personal competences:

• Professional competence: Students are able to explain the role of Business Intelligence in organizations and describe core BI components (data sources, integration, storage, analytics, reporting). They can evaluate BI use cases and BI architectures in relation to business objectives and organizational constraints.
• Methodological competence: Students can translate business questions into analytical requirements, select appropriate BI methods and KPIs, and design a structured BI solution concept (including data model logic, reporting/dashboard concepts, and data quality considerations). They can interpret BI results and derive actionable recommendations.
• Social competence: Students are able to collaborate in project teams, communicate BI insights to non-technical stakeholders, and justify analytical choices (KPIs, dashboards, data sources) in a clear and professional manner.
• Personal competence: Students strengthen analytical thinking and responsible handling of data, develop a critical mindset toward data quality and bias, and enhance self-organization in structured, project-based analytical work.

• BI fundamentals: purpose, BI vs. analytics, decision support
• BI architecture overview: data sources, integration, storage, consumption layer
• Data warehousing basics and dimensional thinking (overview)
• Data integration and preparation concepts (ETL/ELT, data pipelines – overview)
• KPI design and performance measurement (business relevance, consistency)
• Reporting and dashboards: principles of effective visualization and storytelling with data
• Self-service BI and organizational implications (roles, governance, adoption)
• Data quality, data lineage, and trust in BI outputs
• BI project lifecycle: requirements, prototyping, implementation, rollout
• Development of a practical BI project (e.g., dashboard + KPI system + decision recommendations)