Business Intelligence. L essons from Research and Teaching E xperience

1. Business Intelligence. Lessons from Research and Teaching Experience Prof. Celina M. Olszak, Ph. D. University of Economics in Katowice

2. AGENDA • Research Interests • Motivation for the Study and Description of the Problem to be Solved • “Business Intelligence” as a subject of teaching • Conclusions

3. Katowice - heart (the capital) of Silesia region - the most industrialized and densely populated region of the country. • PAST: heavy industry, hard coal, mining and metallurgy. • NOW:new image - new, clear sectors, high technologies, banking, finance, health care, education and smart cities (heart treatment centres, heart transplant centres; world computer games championships, e-sports, music concerts festivals, exhibitions). • However, a couple of problems like smog and polluted air have been not still solved and government will have to tackle them in the nearest future.

4. PAST: heavy industry, hard coal, mining and metallurgy

5. NOW: new, clear sectors, high technologies, banking, finance, health care, education and smart cities

6. University of Economics in Katowice • University of Economics in Katowice was founded in 1937 and is the biggest and oldest business school in the region, one of the top universities in Poland • Four faculties • 10 thousands of students.

7. Research Interest Organizational creativity, ICT-based organizational creativity support Decision support systems Organizations, Business, Stakeholders Executive Information Systems Business Intelligence & Big Data Impact of disruptive technology on innovative & sustainable development of organizations Knowledge-based systems

8. PAST NOW Automation of business operations, support of operational decisions Supporting innovative business strategies and models Source of competitive advantage Virtual organizations, e-commerce, virtual supply chain Leverage of information and intellectual resources, customer relations, product and service personalization ICT Transformation and change tool Integration of ecosystem • Re-designing processes, • development of new forms of cooperation and collaboration, • internal and external integration, horizontal and vertical integration, creating a new brand • Ergonomic and flexible working environment, exchange of products and services, providing communication and cooperation ICT as a driver for introducing changes and innovations

9. Motivation for the Business Intelligence Study • The source of organization’s power has shifted from material capital to intangible resources (Drucker, 2010). • The organizations are more and more governed by information, knowledge, intelligence, intellectual capital and wisdom (Davenport, Harris, 2007). • The development of the Internet, social media, distributed databases and a variety of mobile devices has caused a huge increase in data called Big Data (BD).Much of this diverse data has a high business value and, if properly analysed (by BI) and utilized, can become an important organizational asset (Manyika et. al., 2011; Chen, Chiang, Storey, 2012). • For innovative development, it is essential for organizations to utilize BI&BD to improve decision-making, the relationships with all their stakeholders, as well as to identify future opportunities and threats. • However, many organizations make a limited use of BI&BD (poor knowledge in organizations about BI&BD and value of BD; lack of appropriate guidance and recommendationsfor organizations how to analyze and use BI&BD).

10. Big Data Business Intelligence BI – analyzing and discovering new knowledge from different kinds of data, including BD (Goes, 2014; Cosic, Shanks, Maynard, 2012; Ularu et. al., 2012) Volume – the quantity of data measured in peta- and zettabytes Variety – the heterogenic nature of data, data can have different form VALUE– significant value hidden in data • Five Attributes (5V) - Faster and easier access to information, - Improving business processes and relationships with all stakeholders, - Identification of the opportunities and threats on the market, - Adopt to a changeable environment . Veracity – data can be inconsistent, incomplete and inaccurate Velocity – the meteoric speed of data emergence and the need to analyze it in real time

11. BI & BD Analitics3.0 Real-time analysis Machine learning Opinion mining Analysis on demand Dynamic analysis Processing feelings and emotions Analitics2.0 Optimization Statistical analysis Predictive modelling Forecasting/extrapolation Alerts Analitics 1.0 Access and reporting Query/drill down Ad hoc reports Standard reports

12. General Objective of the Study • Provide organizations a theoretical, conceptual, and applied grounded discussion of BI&BD to aid in innovative development as well as effective decision-making. This study addresses the following research questions: • What is the substance (nature) of BI&BD ? • What is the added-value of BI&BD to innovative development and decision-making process? • How to support innovative development of organizations and decision-making using disruptive technology (BI&BD)?

13. Dynamic and analytical capabilities Practices of making changes based on ICT (BI&BD) Portfolio of values Strategic Thinking:capabilities to recognize chances and opportunities; • understanding business, customers and environment (present and future needs) and ICT Strategic values Identification & Acquisition of information R B V VALUE Operational values Local exploitation of resources Internal integration of resources Organisational values • Organizational Culture: • analytical and creativecapabilities; capability to manage changeand risk; business partnership, collaboration, communication, cooperation networks, and trust • Values for customer Exploration of external resources Redesigning of business processes, networks and areas • Values for stakeholders Values for environment Framework for innovative development of organizations based on BI&BD

14. Published Results of the Last Studies • Olszak C.M., Bartuś T., Lorek P. (2018), A Comprehensive Framework of Information System Design to Provide Organizational Creativity Support, „Information & Management”, Vol. 55, pp. 94-108, https://doi.org/10.1016/j.im.2017.04.004. • Olszak C.M., Kisielicki J. (2018), A conceptual framework of information systems for organizational creativity support. Lessons from empirical investigations, „Information Systems Management”,Vol. 35, No. 1, pp. 29–48, https://doi.org/10.1080/10580530.2017.1416945. • Olszak C.M., Mach-Król M. (2018), A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data, „Sustainability”, Vol. 10(10), 3734, https://doi.org/10.3390/su10103734. • Olszak C. M. (2016), Toward better understanding and use of Business Intelligence in organizations,„Information Systems Management”, Vol. 33, No. 2, pp. 105-123, http://dx.doi.org/10.1080/10580530.2016.1155946. • Olszak C.M., Zurada J. (2019), Big Data-driven Value Creation for Organizations, Proceedings of Hawaii International Conference on System Sciences (HICSS-52), January, 8-11, pp. 164-173, http://hdl.handle.net/10125/59457.

15. FROM RESEARCH TO TEACHING

16. Teaching Aims for Advanced Analytics (Business Intelligence)at E-Commerce major The aim of the subject is to provide students with comprehensive knowledge and skills related to analytics, KM, using IT The subject allows students to improve their analytical and managerial competences During the studies students acquire the knowledge which enable them to search for, gain, collect, apply knowledge and to carry out BI, information audits and BPM

17. Teaching of the Business Intelligence subject RBV enables students to better know how to manage information resources and what should be done with them in order to improve decision –making . It gives also a sound basis to illustrate what benefits can be provided for organizations Resource-based View (RBV) DA enables students to carry out projects to collect, analyze information, discover new knowledge, visualize, and use of information. Design Approach (DA)

19. Business Intelligence Beneficiaries

20. Business Intelligence Applications • Analysis that supports cross selling and up selling • Customer segmentation and profiling • Analysis of parameters importance • Survival time analysis • Analysis of customer loyalty and customer switching to competition • Credit scoring • Fraud detection • Logistics optimisations • Forecasting of strategic business processes development

21. LECTURES • Theoretical Introduction to BI • Discussion • Analysis of different cases • Invited speakers • LABS • Projects • Teamwork, Workgroup • Computer games Active and Collaborative Teaching

28. The students gain competences in order to: Plan, design, implement and use analytical applications for e-commerce Manage organization’s information resources A Graduate of E-Commerce Supervise an information policy Design and carry out reengineering of business processes

29. CONCLUSION Business Intelligence and Big Data come with a range of challenges, trade-offs and hidden costs BI&BD risk deepening inequalities for digitally marginalized groups. This requires not only improved access to digital infrastructure, but also skills • BI&BD can jeopardise user data privacy and safety. Privacy requires a multifaceted strategy, reflecting a whole of-society vision MULTIFACETED STRATEGY SKILLS Without a multi-sectoral perspective, BI&BD can undermine legal frameworks that protect users, support efficient taxation, and ensure fair competition. Regulation and taxation must keep pace with technological change MULTI-SECTORAL PERSPECTIVE

30. THANK YOU FOR YOUR ATTENTION celina.olszak@ue.katowice.pl