Objectives and competences
- a deep understanding of data visualisation and analytics and data engineering, specifically in the business domain
- ability to use databases effectively,
- achieving cohesion of unstructured massive data for optimal use,
- analytics of product usage and consumer behaviour for an improved user experience,
- analytical modelling skills using complex financial data sets,
- designing appropriate solutions for key financial business decisions such as investment decisions, risk management and sources of finance,
- identifying ways to add value based on the knowledge acquired,
- Communication, organisational and professional skills, and the problem-solving attitude to operate successfully in a global environment.
Content (Syllabus outline)
The course Artificial Business Intelligence answers the question of how to integrate AI in the business environment, by enhancing the business user's capacity to utilize business intelligence tools.
The course involves reporting personalization, intelligent Key Performance Indicators (KPI) application, integration of production and reporting environments, and business users experience (B-UX) utilization in both real and virtual settings.
Students will learn the concepts and practical applications of integrating AI in Business intelligence by designing adaptive KPIs, interactive dashboards, how to integrate predictions in reporting, and to adapt user environment for improved business user experience and communication. They will employ visualization technologies on desktops, mobile devices, and virtual reality environments.
In the course students will experience a combination of reporting visualization tools for dashboards design, predictive analytic tools, prescriptive alerts, hyper intelligence, and virtual reality tools.
After completing the course, students will be capable of designing a productive business intelligence space and working closely with the software developers which are providing the underlying support.
Learning and teaching methods
- lectures,
- case analysis with computer exercises,
- group project work
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
Students will develop understanding of
- Knowledge base: will develop great depth and systematic understanding of business predictive analytics
- Ethical issues: will develop the capacity to analyse and manage the implications of ethical dilemmas of smart digitalisation transformation and work pro-actively with others to formulate solutions
- Disciplinary methodologies: will gain a comprehensive understanding of techniques / methodologies to conduct business research projects
Intended learning outcomes - transferable/key skills and other attributes
Cognitive/Intellectual skills:
- Analysis: with critical awareness, can undertake analysis, managing complexity, incompleteness of data or contradictions in the area of Artificial business intelligence
- Synthesis: can synthesise new approaches, in a manner that can contribute to the development of methodology or understanding in that discipline or practice
- Evaluation: have a level of conceptual understanding that allow independent evaluation of research and methodologies. Can argue alternative approaches
- Application: can act independently and with originality in problem solving, is able to lead in planning and implementing tasks of artificial business intelligence projects at a professional level.
Key/Transferable skills
- Group work: work effectively in a group and lead small groups. Can clarify task, managing the capacities of group members, negotiating and handling conflict with confidence
- Learning resources: is able to use full range of learning resources
- Self-evaluation: is reflective on own and others’ functioning in order to improve business prediction analytics usage
- Management of information: can undertake innovative research tasks competently and independently
- Autonomy: is independent and self-critical as learner; supports the learning of others and can manage own continuing professional development
- Communication: can communicate complex information clearly and effectively to specialists / non-specialists, understands lack of understanding in artificial business intelligence by others. Can act as a consultant
- Problem solving: can continue own professional study independently, can make use of others professionally within/outside the artificial business intelligence.
Readings
• - Bidgoli, H. (2021). Management information systems. Cengage Learning. ISBN - 978-0-357-41869-7
COBISS.SI-ID - 144315139
Additional
• Felix Weber (2023) Artificial Intelligence for Business Analytics: Algorithms, Platforms and Application Scenarios
• Bill Hibbard (2015) Ethical Artificial Intelligence. Pridobljeno 26 aprila 2023: https://arxiv.org/ftp/arxiv/papers/1411/1411.1373.pdf.
• Igor Perko (2021) Hybrid reality development - can social responsibility concepts provide guidance?. Pridobljeno 26. aprila 2023: https://www.emerald.com/insight/content/doi/10.1108/K-01-2020-0061/full/html.
Additional information on implementation and assessment - written examination,
- practical work
- project
