Objectives and competences
Students will:
• understand the concepts of business intelligence and the importance of business intelligence for business decision making.
• understand the factors influencing the successful deployment and implementation of a business intelligence system
• learn the principles and best practices of how to use data to support fact-based decision making.
• get to know the methods, techniques and technologies used in business intelligence.
Students will gain:
• ability to synthesize original ideas, concepts and solutions to certain problems from different disciplinary areas
• understanding business intelligence decision support
• in-depth knowledge of a range of decision support methods, techniques and technologies.
Content (Syllabus outline)
1. basic principles of Business Intelligence (BI),
2. the meaning of BI for business decision making and organization management
3. BI application areas, specification of these areas,
4. position in IS/ICT architecture and links to other applications
5. effect and critical success factors of BI,
6. planning and analysis of BI model,
7. BI system implementation,
8. Identification of data sources and extraction, transformation and storing of data in data warehouses and data lakes
9. design and modeling of data -- principles of dimensional modeling, relationship between measures and relevant dimensions, physical design and modeling, data quality management, data granularity problems management,
10. basic principles of Big Data
11. business analytics
12. business performance management with BI
Learning and teaching methods
• Lectures
• Problem based learning
• Case studies
• Exercises
Intended learning outcomes - knowledge and understanding
Knowledge and understanding
At the end of the course students will be able to:
• describe the basic concepts of business intelligence from a technical and organizational point of view
• understand and evaluate business intelligence concepts and their relevance to business
• nderstand the role of business intelligence in gaining business benefits
• know the technologies and systems for business intelligence
• use the ETL process to retrieve, clean, and integrate business data into a simple data warehouse
• visualize, analyze and interpret data on the dashboard for business decision making
Intended learning outcomes - transferable/key skills and other attributes
- The student should be able to identify areas of business relevance for business intelligence.
- The student should be able to understand and assess some business intelligence concepts and their business relevance.
Readings
1. Sharda, R., Delen, D., & Turban, E. (2024). Business intelligence, analytics, data science, and AI: a managerial perspective (5th ed., global ed., str. 732). Pearson.
2. Sherman, R. (2015). Business intelligence guidebook: from data integration to analytics (str. XXIII, 525). Elsevier/Morgan Kaufmann.
3. Skyrius, R. (2021). Business intelligence: a comprehensive approach to information needs, technologies and culture (str. XII, 273). Springer.
4. Nussbaumer Knaflic, C. (2015). Storytelling with data: a data visualization guide for business professionals (str. XIII, 267). John Wiley & Sons.
Prerequisits
Before taking the exam, the student must have completed the coursework and a positively assessed practical assignment.
Additional information on implementation and assessment Coursework (20%)
Practical assignment (30%)
Written exam (50%)
