Business Intelligence Lessons from Research and Teaching Experience

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

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

• 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.

PAST: heavy industry, hard coal, mining and metallurgy

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

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.

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

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 Transformation and change tool Re-designing processes, development of new forms of cooperation and collaboration, internal and external integration, horizontal and vertical integration, creating a new brand ICT Integration of ecosystem Ergonomic and flexible working environment, exchange of products and services, providing communication and cooperation ICT as a driver for introducing changes and innovations

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 recommendations for organizations how to analyze and use BI&BD).

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) - 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. Big Data Volume – the quantity of data measured in peta- and zettabytes VALUE – significant value hidden in data Variety – the heterogenic nature of data, data can have different form Five Attributes (5 V) Velocity – the meteoric speed of data emergence and the need to analyze it in real time Veracity – data can be inconsistent, incomplete and inaccurate

BI & BD Real-time analysis Dynamic analysis Machine learning Statistical analysis Opinion mining Processing feelings and emotions Optimization Predictive modelling Forecasting/extrapolation Alerts Query/drill down Ad hoc reports Standard reports Access and reporting Analitics 3. 0 Analitics 2. 0 Analitics 1. 0 Analysis on demand 11

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 decisionmaking. 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)?

Dynamic and analytical capabilities R B V Strategic Thinking: capabilities to recognize chances and opportunities; understanding business, customers and environment (present and future needs) and ICT Organizational Culture: analytical and creative capabilities; capability to manage change and risk; business partnership, collaboration, communication, cooperation networks, and trust Practices of making changes based on ICT (BI&BD) Identification & Acquisition of information Portfolio of values Strategic values Operational values Local exploitation of resources Internal integration of resources Organisational values 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 V A L U E

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/su 10103734. § 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.

FROM RESEARCH TO TEACHING

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 16

Teaching of the Business Intelligence subject Resource-based View (RBV) Design Approach (DA) 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 DA enables students to carry out projects to collect, analyze information, discover new knowledge, visualize, and use of information.

BI models Type BI&A Function Data Marts Decision support level Used techniques Ad hoc analysis, comparative Narrow, limited to unit, analysis, reporting department Operational, well structured reporting, OLAP Data warehouse Multidimensional analysis The whole enterprise Operational, tactical, strategic OLAP, data mining BI with PA Forecasting of different scenarios Narrow, limited to unit, department Operational, tactical, strategic OLAP, AP Real-time BI Monitoring of current activities, discovering irregularities Narrow, limited to unit, department Operational, well structured EII Corporative BI Corporative management, building loyalty strategy All actors of value chain Operational, tactical, strategic ETL, data mining BI portals Content management and document management, group work Selected communities Operational, tactical, strategic Internet, Web mining, CMS, work group, personalization techniques BI nets The building of expert’ nets, Global, various communities Operational, tactical, strategic social capital management Web mining, Web farming, cloud computing BI for everyone The building of social nets, social capital management Mobile, social media, semantic Web, Web mining, cloud computing 18 BI for demand Scope Global Operational, tactical, strategic

Business Intelligence Beneficiaries Retail industry n Forecasting, ordering and replenishment n Marketing, merchandising, distribution Insurance n Claims and premium analysis n Customer analysis, risk analysis Banking, finance, securities n Customer profitability analysis, Telecommunications n Customer profiling and segmentation n Credit management, branch sales n Customer demand forecasting Manufacturing industry n Sales, forecasting n Purchasing, logistics n Inventory planning

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

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

HOURS Subjects No. ECTS Lecture Labs Semester 1, Year 1 1 MANAGERIAL AND DIGITAL ECONOMY 4 30 30 0 2 INNOVATIONS AND ENTREPRENEURSHIP IN E-COMMERCE 4 45 30 15 3 FINANCING E-COMMERCE PROJECTS 3 20 20 0 4 BUSINESS INFORMATION SYSTEMS 4 50 20 30 5 QUANTITATIVE METHODS FOR ECOMMERCE 5 60 30 30 6 BUSINESS INTELLIGENCE 4 50 20 30 7 BUSINESS COMMUNICATION (English C 1) 3 30 0 30 8 BUSINESS ETHIC 30 280 145 135

Subjects ECTS Semester 2, Year 1 Lecture Lab No. Hours 1 MARKET ANALYSIS 4 50 20 30 2 ON-LINE MARKETING 2 15 15 0 3 SOCIAL MEDIA CAMPAIGNS 2 15 15 0 4 DIGITAL LAW 2 20 20 0 5 CUSTOMER PROTECTION STANDARDS 2 15 15 0 6 INTERNATIONAL BUSINESS 6 60 30 30 7 WEBSITES AND E-STORES DESIGN 4 40 20 20 8 FINANCE IN E-COMMERCE 2 35 20 15 9 BUSINESS COMMUNICATION (English C 1) 4 30 0 30 2 140 15 10 MASTER THESIS 30 295 155

Houers Subjects ECTS Semester 3, Year 2 1 CUSTOMER RELATIONSHIP MANAGEMENT 2 DATA ANALYSIS FOR ECOMMERCE DATA SCIENCE 3 No. Lectures Labs 3 50 20 30 2 15 0 15 2 15 15 0 4 DIGITAL ADVERTISING WITH GOOGLE ADS AND WEB ANALITICS 5 60 30 30 5 MASTER THESIS 9 15 0 15 ELECTIVE SUBJECTS x 3 9 45 45 0 6 -9 30 200 110 90

Hours Subject No. ECTS Semester 4, Year 2 Lectures 1 IT MANAGEMENT AND BUSINESS ALIGNMENT 5 60 30 30 2 SALES MANAGEMENT ON THE INTERNET 2 15 0 15 3 LOGISTICS FOR E-COMMERCE 3 40 20 20 4 DATA ANALYSIS WITH QLIK 2 15 0 15 5 ELECTIVE SUBJECT x 1 3 15 15 0 6 MASTER THESIS 15 15 0 15 30 160 65 95 Labs

Semester 3, Year 2 Hours ECTS No. Elective subjects Lectures Labs 1 Creativity and Complexity 3 15 15 2 E-BRANDING 3 15 15 EUROPEAN INTEGRATION FROM 3 COMMUNITY TO UNION 3 15 15 EMERGING MARKET 4 MULTINATIONALS 3 15 15 5 INTERNATIONAL ENTREPRENEURSHIP 3 15 15 6 TECHNIGUES OF SALES 3 15 15

Elective subjects No. Semester 4, Year 2 Hours ECTS Lectures Labs 1 BUSINESS PROJECTS 3 15 15 2 SUSTAINABLE ECOMMERCE 3 15 15 3 CONSUMER BEHAVIOR 3 15 15

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

CONCLUSION Business Intelligence and Big Data come with a range of challenges, trade-offs and hidden costs MULTIFACETED STRATEGY BI&BD can jeopardise user data privacy and safety. Privacy requires a multifaceted strategy, reflecting a whole ofsociety vision MULTISECTORAL PERSPECTIVE SKILLS BI&BD risk deepening inequalities for digitally marginalized groups. This requires not only improved access to digital infrastructure, but also 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

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