Učni načrt predmeta

Predmet:
Poslovna inteligenca I
Course:
Business Inteligence I
Študijski program in stopnja /
Study programme and level		 Študijska smer /
Study field		 Letnik /
Academic year		 Semester /
Semester
Informacijske in komunikacijske tehnologije, 2. stopnja Inteligentni sistemi in robotika 1 2
Information and Communication Technologies, 2nd cycle Intelligent Sytems and Robotics 1 2
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT2-619
Predavanja
Lectures		 Seminar
Seminar		 Vaje
Tutorial		 Klinične vaje
work		 Druge oblike
študija		 Samost. delo
Individ. work		 ECTS
15 15 15 105 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Matjaž Gams
Sodelavci / Lecturers:
dr. Aleksander Pivk
Jeziki / Languages:
Predavanja / Lectures:
slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisites:

Zaključen študijski program prve stopnje s področja naravoslovja, tehnike ali računalništva.

Student must complete first-cycle study programmes in natural sciences, technical disciplines or computer science.

Vsebina:
Content (Syllabus outline):

Znanstvena metoda: Strukture znanstvenega védenja, znanstvene aktivnosti in procesi. Uporaba znanstvene metode pri oblikovanju in analizi podatkovnih modelov za poslovno inteligenco (BI). Vloga umetne inteligence (AI) in velikih jezikovnih modelov (LLM), kot so GPT, pri avtomatizaciji in izboljšanju procesov v BI.

Uvod: Definicija poslovne inteligence (BI) in upravljanja s strankami (CI). Arhitektura BI/CI za podjetja in kako veliki jezikovni modeli (LLM) ter sistemi, kot so GPT, izboljšajo zajem, obdelavo in analizo podatkov v realnem času.

Osnove trženja: Poslovno informiranje, odločanje, strategije, planiranje in razvoj strategij. Vključitev LLM in GPT v strategije neposrednega in posrednega trženja (strategije izdelkov, ponudbe, medijev, distribucije). Uporaba LLM in GPT za analizo trženjskih priložnosti in okolja ter za podporo pri oblikovanju poslovnih modelov.

Orodja za delo s podatki: Preglednice, podatkovne baze in druga orodja za analizo podatkov, vključno z orodji, ki uporabljajo LLM in GPT za avtomatizacijo podatkovnega čiščenja, oplemenitenje podatkov in napredno analitiko. Podatkovna skladišča, kakovost podatkov, priprava in migracija podatkov, napredne rešitve za posredovanje podatkov in avtomatizirano analizo.

Poslovna analitika: Definiranje in analiza poslovnih problemov s pomočjo inteligentnega analitičnega modeliranja (kvalitativno/kvantitativno modeliranje, metrike, profiliranje, opredeljevanje strank) ter uporaba LLM in GPT za napredno analizo podatkov. Primeri vključujejo napovedovanje kreditnega tveganja, prekinitve poslovnih odnosov strank, zadrževanje strank, napovedovanje prodajnih možnosti ter odkrivanje poneverb. Ovrednotenje in prenos rezultatov v poslovno prakso.

Avtomatizacija trženja: Uporaba LLM in GPT za analizo trga in strank, razvoj kontaktnih strategij, integracijo tržnih kanalov ter personalizacijo tržnih vsebin. Uporaba GPT za trženje na osnovi dogodkov in trženje v realnem času, primeri uporabe iz različnih industrij, kot so bančništvo, telekomunikacije, maloprodaja, zavarovalništvo, proizvodnja. Obravnava etičnih in pravnih vidikov uporabe umetne inteligence v trženju.

Izzivi pri razvoju programskih sistemov in implementacija projektov: Predstavitev celotnega procesa razvoja programskih projektov s poudarkom na integraciji AI in LLM v BI rešitve. Obravnava izzivov pri uvajanju naprednih AI tehnologij in GPT modelov v obstoječe poslovne sisteme.

Orodja in rešitve: Pregled najboljših orodij in rešitev na trgu za BI/CI, vključno z LLM in GPT rešitvami za poslovanje. Primeri uporabe GPT pri avtomatizaciji analiz, generiranju poročil in podpori odločanju.

Scientific Method: Structures of scientific knowledge, scientific activities, and processes. Application of the scientific method in designing and analyzing data models for business intelligence (BI). The role of artificial intelligence (AI) and large language models (LLM), such as GPT, in automating and improving processes in BI.

Introduction: Definition of business intelligence (BI) and customer intelligence (CI). BI/CI architecture for businesses and how large language models (LLM) and systems like GPT enhance data capture, processing, and real-time analysis.

Marketing Basics: Business information, decision-making, strategies, planning, and strategy development. Integration of LLM and GPT into direct and indirect marketing strategies (product, offer, media, distribution strategies). Use of LLM and GPT for analyzing marketing opportunities and environments, and supporting the creation of business models.

Data Tools: Spreadsheets, databases, and other data analysis tools, including tools that use LLM and GPT for automated data cleansing, enrichment, and advanced analytics. Data warehouses, data quality, data preparation and migration, and advanced solutions for data transmission and automated analysis.

Business Analytics: Defining and analyzing business problems through intelligent analytical modeling (qualitative/quantitative modeling, metrics, profiling, customer identification) and the use of LLM and GPT for advanced data analysis. Examples include credit risk prediction, customer churn forecasting, customer retention, sales opportunity prediction, and fraud detection. Evaluation and transfer of results into business practice.

Marketing Automation: Use of LLM and GPT for market and customer analysis, development of contact strategies, integration of marketing channels, and personalization of marketing content. GPT applications in event-based marketing and real-time marketing, with case studies from various industries such as banking, telecommunications, retail, insurance, and manufacturing. Discussion of ethical and legal aspects of AI in marketing.

Challenges in Software System Development and Project Implementation: Overview of the entire process of developing software projects with a focus on integrating AI and LLM into BI solutions. Addressing challenges in implementing advanced AI technologies and GPT models into existing business systems.

Tools and Solutions: Review of the best tools and solutions on the market for BI/CI, including LLM and GPT solutions for business. Examples of GPT use for automating analyses, generating reports, and supporting decision-making.

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
R. Sharda, D. Delen, E. Turban. Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, ISBN-10 0134633288, 2021.
S. Russel, and P. Norvig. Artificial Intelligence: A Modern Approach, 4th Edition. Pearson Education Limited, 2021. ISBN-13 978-0-13-461099-3.
R. Hurley. Business Intelligence: The Ultimate Guide to BI, Artificial Intelligence, Machine Learning, Big Data, Cybersecurity, Data Science, and Predictive Analytics. ISBN-10 1659796954, 2020.
R. Akerkar. Artificial Intelligence for Business. Springer, ISBN 978-3-319-97435-4, 2019.
G. S. Miñan Olivos, J. A. Estrada Espinoza, A. A. Cruz Aguilar, J. A. Moreno Ramos, & C. B. Cisneros Hilario. Business Intelligence as a Competitive Advantage in Organizations: A Systematic Review of the Literature Between 2012-2022. Proceedings of the 21th LACCEI International Multi-Conference. DOI: 10.18687/laccei2023.1.1.758, 2023.
M. Hanqing Hu, & L. Jianling. Evolution of Business Intelligence: An Analysis from the Perspective of Social Network. Tehnicki vjesnik - Technical Gazette. DOI: 10.17559/tv-20210819071232, 2022.
F. Gurcan, A. Ayaz, G. Gokce Menekse Dalveren, & M. Derawi. Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data from 2003 to 2023 Using Machine Learning. Sustainability. DOI: 10.3390/su15139854, 2023.
D. Singh, A. Singh, A. Omar, & S. Goyal. Business Intelligence and Human Resource Management. DOI: 10.4324/9781003184928, 2022.
Y. Chen, C. Li, & H. Wang. Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021). Forecasting. DOI: 10.3390/forecast4040042, 2022.

Cilji in kompetence:
Objectives and competences:

Cilj predmeta je podati osnovno znanje o poslovni inteligenci in o strateškem marketinškem odločanju. Uvodoma so predstavljeni temelji področja poslovne inteligence, nato pa so obdelane poslovne in tržne strategije, njihovo načrtovanje in razvoj ter možnosti in načini prenosa v prakso.
Cilj je tudi naučiti študente uporabljati osnovna poslovna orodja, kot so preglednice in podatkovne baze.
Osredotočili se bomo na pripravo in migracijo podatkov ter reševanje tipičnih problemov in nevarnosti, katerim se poskušamo izogniti.

V naslednjem sklopu je glavni cilj zagotoviti poglobljeno razumevanje narave in obsega tržne analize in njene vloge pri strateškem marketingu. Ta obsega analizo poslovnih problemov in priložnosti, odkrivanje nezadoščenih potreb strank, odkrivanje konkurenčne prednosti in napovedovanje vedenjskih vzorcev strank, kar omogoča organizaciji proaktivno nastopanje na trgu. V ta namen si bomo podrobno ogledali primerne analitične metode za posamezne probleme in proces vpeljevanja v prakso, kjer bodo nakazane tudi težave, ki se tipično pojavljajo.

The goal of the course is to provide basic 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.
Students should get acquainted with basic business tools like spreadsheets and databases.
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.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- Sposobnost analize, sinteze in predvidevanja rešitev ter posledic.
- Obvladanje raziskovalnih metod, postopkov in procesov, razvoj kritične in samokritične presoje.
- Sposobnost uporabe znanja v praksi.
- Avtonomnost v strokovnem delu.
- Razvoj komunikacijskih sposobnosti in spretnosti, posebej komunikacije v mednarodnem okolju.
- Etična refleksija in zavezanost profesionalni etiki.
- Kooperativnost, delo v skupini (in v mednarodnem okolju).
- Poznavanje področja poslovne inteligence in upravljanja s strankami.
- Poznavanje tržnih strategij, planiranja in razvoja strategij za boljše poslovno odločanje.
- Poznavanje upravljanja s podatki za potrebe poslovne inteligence.
- Poznavanje analitičnih metod primernih za poslovno uporabo ter njihove uporabe v realnih situacijah.
- Poznavanje strategij neposrednega trženja, upravljanja in analize učinkovitosti, taktikah kontaktiranja, etiki in pravnih vidikih.
- Poznavanje teorije iger.
- Zmožnost predstavitve poslovnih situacij z vidika teorije iger.
- Poznavanje razvoja in vodenja softverskih projektov.
- Poznavanje tržnih orodij in rešitev za BI.

Students successfully completing this course will acquire:
- An ability to analyse, synthesise and anticipate solutions and consequences.
- To gain the mastery over research methods, procedures and processes, a development of the critical judgement.
- An ability to apply the theory in to a practice.
- An autonomy in the professional work.
- Communicational-skills development; particularly in international environment.
- Ethical reflexion and obligation to a professional ethics.
- Cooperativity, team work (in international environment).
- Knowledge of business intelligence and customer intelligence area.
- Knowledge of marketing strategies, strategy planning and development for strategic decision- making.
- Knowledge of data handling issues for BI purposes.
- Knowledge of predictive modeling for business purposes and its real-life applicability.
- Knowledge of direct marketing strategies, marketing performance management, creative and contact tactics, ethics and legal aspects.
- Knowledge of game theory.
- The ability to view business situations in game- theoretic terms.
- Knowledge of design and management of software projects.
- Awareness of BI solutions in the marketplace.

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo.

Lectures, seminar, consultations, individual work.

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminarska naloga
60 %
Seminar work
Ustni zagovor seminarske naloge
40 %
Oral defense of seminar work
Reference nosilca / Lecturer's references:
1. M. Gams, T. Kolenik. Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules, Electronics 2021, 10(4), MDPI, DOI 10.3390/electronics10040514
2. M. Gams, I. Yu-Hua Gu, A. Harma, A. Munos, V. Tam. Artificial intelligence and ambient intelligence. Journal of ambient intelligence and smart environments, ISSN 1876-1364, 2019, vol. 11, no. 1, str. 71-86, doi: 10.3233/AIS-180508.
3. M. Shulajkovska, M. Smerkol, E. Dovgan, M. Gams. A machine-learning approach to a mobility policy proposal, Heliyon 9, 2023, DOI 10.1016/j.heliyon.2023.e20393
4. M. Shulajkovska, M. Smerkol, G. Noveski, M. Gams. Enhancing urban sustainability : Developing an open-source AI framework for smart cities. Smart cities. 2024, vol. 7, no. 5, pp. 2670-2701. ISSN 2624-6511. DOI: 10.3390/smartcities7050104. [COBISS.SI-ID 208050691].
5. T. Kompara, J. Perš, D. Susič, M. Gams. A one-dimensional non-intrusive and privacy-preserving identification system for households. Electronics, ISSN 2079-9292, 2021, vol. 10, no. 5, str. 559-1- 559-21, doi: 10.3390/electronics10050559.