MPŠ MP&Scaron MP&Scaron MP&Scaron Avtorji

Jožef Stefan
Postgraduate School

Jamova 39
SI-1000 Ljubljana

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10


Course Description

Business Inteligence II


Information and Communication Technologies, third-level study programme


prof. dr. Matjaž Gams
dr. Aleksander Pivk


The goal of the course is to provide general knowledge of business intelligence and business analytics extended with the knowledge and skills for strategic (marketing) decision-making. Firstly, the business intelligence and business analytics grounds will be presented, followed by the goals, objectives, and common problems of their adoption. Strong focus is given to best practices.

The students who will successfully complete this course will master the basics of business intelligence and will be capable of applying these methods and algorithms in solving demanding business problems and evaluating their results.


Scientific Method:
scientific knowledge structures, scientific

Definition of intelligence and business intelligence (BI), basic BI schema, criteria, reasons and areas for adoption, common problems and pitfalls, best practices, definition of business analytics (BA) and some use-cases, review of differences among BI and BA, best practices.

Data handling:
Data warehousing, data quality, data preparation/enhancement, data migration, data mediation, examples of major pitfalls.

Predictive business analytics:
Business problem detection, analysis, and definition, analytical modeling for solving business/marketing problems, evaluation and business adoption of modeling results, overview of various industry.

Marketing strategies and direct marketing:
Business strategies, strategy planning and development, direct marketing strategies (product, offer, media, distribution and creative strategies), business models, analysis of marketing opportunities and environment. Customer/market analysis and research, contact strategies, marketing channels, integration aspects, creative tactics, content personalization, response tracking, marketing performance management, event-driven marketing, real-time marketing, best practice examples in various industries.

Game theory and its applications:
Simultaneous-move (static) and sequential-move (dynamic) non-cooperative games, Nash equilibrium and how to find it, pure and mixed strategies. Business applications: bargaining, auctions, negotiations.

Challenges in software engineering and project implementation.

Course literature:

Selected chapters from the following books:

• R. Sharda, D. Delen, and E. Turban. Business Intelligence and Analytics: Systems for Decision Support, 10th Edition. Prentice Hall, 2014. ISBN 978-0133050905
• A. Maheshwari. Business Intelligence and Data Mining Made Accessible. Business Expert Press, 2014. ISBN 978-1631571206
• R. Sherman. Business Intelligence Guidebook: From Data Integration to Analytics. Morgan Kaufmann, 2014. ISBN 978-0124114616
• F. Provost, and T. Fawcett. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, 2013. ISBN 978-1449361327
• J. Kolb. Business Intelligence in Plain Language: A practical guide to Data Mining and Business Analytics. CreateSpace Independent Publishing Platform, 2013. ISBN 978-1479324187

Significant publications and references:

• V. Vidulin, M. Bohanec, and M. Gams. “Combining human analysis and machine data mining to obtain credible data relations.” Information sciences, vol. 288, pp. 254-278, 2014.
• A. Pivk, O. Vasilecas, D. Kaliatiene, and R. Rupnik. “On approach for the implementation of data mining to business process optimisation in commercial companies.” Technological and economic development of economy, vol. 19, no. 2, pp. 237-256, 2013.
• D. Zupančič, M. Luštrek, and M. Gams. “Multi-agent architecture for control of heating and cooling in a residential space.” The Computer journal, ISSN 0010-4620, 2014.
• B. Pogorelc, E. Stojmenova, M. Gams et al. “Ambient bloom: new business, content, design and models to increase the semantic ambient media experience.” Multimedia tools and applications, vol. 66, no. 1, pp. 7-32, 2014.
• M. Gams, H. Gjoreski, M. Luštrek, B. Kaluža, Metoda in sistem za prepoznavanje aktivnosti na podlagi konteksta : patent SI 23356 A. Ljubljana: Urad RS za intelektualno lastnino, 28. nov. 2014. [COBISS.SI-ID 27964199] - patent


Seminar work (80%)
Oral defense (20%)

Students obligations:

Seminar work and oral defense.