(in Polish) Analityka biznesowa w zarządzaniu - Systemy Business Intelligence 2600-ABz2ABZBI
The aim of the course is to introduce students to the concept and practical application of business analytics in organizational management processes, as well as to demonstrate the role of Business Intelligence (BI) Systems in strategic and tactical decision-making. In particular, the topics covered during the course include:
1. Introduction to Business Analytics
- Essence and significance of analytics in management
- Data as a key organizational asset
2. Digital Transformation of Organizations
- Impact of digitalization on management processes
- Digital business models and data as a source of competitive advantage
3. Data Warehouses and Big Data
- Concept and architecture of data warehouses
- Characteristics and applications of Big Data technologies in business analysis
4. Business Intelligence Systems Architecture
- Components of BI systems
- Integration of BI with other organizational IT systems
5. Types and Characteristics of BI Software
- Overview of BI-class tools
- Criteria for selecting and implementing BI solutions in an enterprise
- Data visualization and reporting
6. Decision Support
- Application of analytics in strategic and operational decisions
- Examples of BI applications in business practice
7. Artificial Intelligence in Business Analytics
- AI business assistant – possibilities and limitations
- Barriers to AI implementation in organizations
8. Ethical and Legal Aspects
- Data privacy protection and GDPR compliance
- Ethical standards in business analytics
- Responsible use of data and AI algorithms
Classes are conducted as part of the project "Integrated Didactics Development Program – ZIP 2.0", co-financed by the European Social Fund under the European Funds for Social Development 2021-2027 (FERS) Program (agreement no.: FERS.01.05-IP.08-0365/23-00).
Type of course
Mode
Learning outcomes
K_W01: The student knows and understands in depth the research methodology and terminology in the field of management and quality sciences, in particular regarding business data analysis, as well as in complementary disciplines (economics and finance).
K_W02: The student knows and understands in depth complex processes and phenomena occurring in various types of organizations and in their surrounding environment; uses management theory to recognize, diagnose, and solve problems related to the functioning of organizations and their integration within organizational strategies based on analysis results.
K_W05: The student knows and understands complex technological, social, legal, economic, ethical, and ecological processes and phenomena, including those related to the use of numerical data, and their impact on the functioning of organizations and the economy as a whole.
K_U01: The student is able to use the theory of management and quality sciences, particularly regarding numerical data analysis, to recognize, diagnose, and solve complex and non-standard problems related to key functions in an organization, including drawing conclusions, formulating strategies, and making business decisions.
K_U03: Student is able to adapt existing methods and tools or propose new ones based on them, applying advanced information and communication technologies and appropriate selection of sources to recognize, diagnose, and solve problems related to data analysis in internal and external environments of organizations.
K_U06: The student is able to communicatively present analysis results in the field of management to diverse target groups using specialized terminology and conduct debate, also in English.
K_K01: The student is ready to critically evaluate and approach complex situations and phenomena related to data analytics and their impact on organizational functioning.
Assessment criteria
The primary assessment criterion is a single-choice test.
Participation in the study visit to PZU in Warsaw forms the basis for increasing the final grade by the Instructor, provided that a minimum passing grade (satisfactory) is achieved on the single-choice test.
Bibliography
Power D. J., Heavin C. (2017), Decision Support, Analytics, and Business Intelligence. Springer.
Foster Provost F., Fawcett T., (2023), Analiza danych w biznesie. Sztuka podejmowania skutecznych decyzji, Helion
Gutman A., Goldmeier J., (2023), Analityk danych. Przewodnik po data science, statystyce i uczeniu maszynowym, Helion
Davenport, T. H., & Ronanki, R. (2018), Artificial Intelligence for the Real World. Harvard Business Review, 96(1)
Turban, E., Sharda, R., & Delen, D. (2020), Decision Support and Business Intelligence Systems. Pearson Education
Ghosh, R., Gil, Y., & Sethi, R. (2021), Artificial Intelligence in Process Management. Communications of the ACM, 64(4)
Imhoff, C., & White, C. (2011), Self-Service Business Intelligence: Empowering Users to Generate Insights
Truhillo J., Maté A. (2012), Business Intelligence 2.0: A General Overview, Lecture Notes in Business Information Processing, vol. 96, Springer-Verlag, Heidelberg
Shollo, A., & Galliers, R. D. (2015), Towards an understanding of the role of business intelligence systems in organisational knowing. Information Systems Journal, 25(4)
Januszewski A. (2008), Funkcjonalność informatycznych systemów zarządzania, tom II, Systemy Business Intelligence, Wydawnictwo Naukowe PWN, Warszawa
Januszewski A. (2012), Funkcjonalność informatycznych systemów zarządzania, tom I, Systemy Business Intelligence, Wydawnictwo Naukowe PWN, Warszawa
Olszak C.M. (2012), Analiza i ocena dorobku naukowego z zakresu problematyki Business Intelligence – wybrane zagadnienia, Systemy inteligencji biznesowej, jako przedmiot badań ekonomicznych, red. C.M. Olszak, E. Ziemba, Uniwersytet Ekonomiczny w Katowicach.
|
Term 2026Z:
1. Power D. J., Heavin C. (2017), Decision Support, Analytics, and Business Intelligence. Springer. |