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 I


Information and Communication Technologies, second-level study programme


prof. dr. Matjaž Gams
dr. Aleksander Pivk


The goal of the course is to provide general knowledge of business intelligence and extend it with the knowledge and skills for strategic marketing decision-making. Firstly, the business and customer intelligence areas will be presented. Afterwards, we will focus on business and marketing strategies, their planning, development and practical application.
Multiple data sources, data quality and integration of data into data warehouses are the first major obstacle when implementing business intelligence solutions.
We will focus on preparation and integration of data, how to resolve possible problems and typical pitfalls that we want to avoid.
The main objective of the next part is to provide a deeper understanding of the nature and scope of marketing analysis and its role in strategic marketing. This includes investigating product-market opportunities, discovering unmet consumer needs, determining competitive advantage and forecasting customer behavior patterns so as to be proactive in the marketplace. Therefore, we will cover in detail problem-specific analytical methods and how they are applied in practice. The problems arising during this phase will also be indicated.


Scientific Method:
scientific knowledge structures, scientific

Definition of business intelligence (BI), definition of customer intelligence (CI), enterprise BI/CI architectures.

Marketing strategies:
Business strategies, strategy planning and development, direct marketing strategies (product, offer, media, distribution and creative strategies), business models, analysis of marketing opportunities and environment.

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 (descriptive/predictive modeling, metrics, customer centric profiling, customer scoring) for solving business/marketing problems, evaluation and business adoption of modeling results, overview of various industry examples such as credit scoring, risk scoring, churn prediction, customer retention, cross/up-sell, fraud detection etc.

Marketing automation:
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 (banking, telecommunications, retail, insurance, and manufacturing), ethics and legal aspects.

Game theory and its applications:
Simultaneous-move (static) non-cooperative games: normal-form representation, dominated strategies and their iterative elimination, Nash equilibrium and how to find it, pure and mixed strategies.
Sequential-move (dynamic) non-cooperative games: extensive-form representation, Nash equilibrium in dynamic games, constant-sum games and the minimax principle.
Other games: perfect and imperfect information, cooperative games.
Business applications: bargaining, auctions, negotiations.
Other applications: board games, biological evolution. Computer simulation.

Challenges in software engineering and project implementation:
A detailed overview of development of software project with the emphasis on understanding problems that are specific to big software projects.

Tools and Solutions:
Overview of best-of-breed BI/CI tools and solutions in the marketplace, insight into emerging technologies.

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 (50%)
Oral defense of seminar work (50%)

Students obligations:

Seminar work and oral defense of seminar work.