Znanstvena metoda: Struktura znanstvenega védenja, znanstvene aktivnosti in procesi.
Uvod: Definicija inteligence in poslovne inteligence (BI), osnovna shema BI, kriteriji, razlogi in področja za uvajanje, problemi in pasti pri uvajanju, najboljše poslovne prakse. Definicija poslovne analitike in primeri uporabe, pregled razlik med poslovno inteligenco in poslovno analitiko ter primeri iz prakse.
Upravljanje s podatki: Podatkovna skladišča, kakovost podatkov, priprava in oplemenitenje podatkov, migracija podatkov, posredovanje podatkov. Primeri največjih nevarnosti in napak.
Poslovna analitika: Odkrivanje, analiza in opredelitev poslovnih problemov, inteligentno analitično modeliranje za reševanje poslovnih/tržnih problemov, ovrednotenje rezultatov in njihov prenos v poslovno prakso. Pregled tipičnih poslovnih problemov.
Strategije trženja in neposredno trženje: Poslovne strategije, planiranje in razvoj strategij, strategije neposrednega trženja, poslovni modeli, analiza tržnih priložnosti in okolja. Analiza trga in strank, kontaktne strategije, tržni kanali, problemi integracije, personalizacija tržnih vsebin, spremljanje aktivnosti strank, upravljanje tržne učinkovitosti, trženje na osnovi dogodkov in trženje v realnem času.
Teorija iger in njena uporaba: Antagonistične igre s hkratnimi in zaporednimi potezami, Nashevo ravnovesje in njegovo iskanje, čiste in mešane strategije. Poslovna uporaba: barantanje, dražbe, pogajanja. Računalniška simulacija.
Izzivi pri razvoju programskih sistemov in implementacija projektov: Predstavitev celotnega procesa razvoja programskih projektov s poudarkom na reševanju težav, s katerimi se soočamo pri večjih projektih.
Uporaba generativne umetne inteligence v BI: Generativna umetna inteligenca (AI), kot so napredni jezikovni modeli (npr. GPT), prinaša nove možnosti za avtomatizacijo procesov analize podatkov, napovedovanja trendov in ustvarjanja poslovnih poročil. Uporaba generativne AI omogoča hitrejšo in bolj personalizirano analizo velikih količin podatkov ter kreiranje scenarijev, ki temeljijo na zgodovinskih podatkih. Poleg tega lahko sistemi generativne inteligence podpirajo poslovne odločitve z izdelavo več možnosti rešitev in predvidevanj, kar izboljšuje natančnost pri strateškem odločanju.
Orodja in rešitve: Pregled najboljših orodij in rešitev na trgu za BI/CI ter vpogled v prihajajoče tehnologije.
Scientific method: Structure of scientific knowledge, scientific activities, and processes.
Introduction: Definition of intelligence and business intelligence (BI), basic BI schema, criteria, reasons and areas for implementation, problems and pitfalls during implementation, and best business practices. Definition of business analytics and examples of its application, an overview of the differences between business intelligence and business analytics, along with real-world examples.
Data management: Data warehouses, data quality, data preparation and enrichment, data migration, data delivery. Examples of major risks and errors.
Business analytics: Identification, analysis, and definition of business problems, intelligent analytical modeling to solve business/market problems, evaluation of results and their transfer into business practice. Overview of typical business problems.
Marketing strategies and direct marketing: Business strategies, planning and development of strategies, direct marketing strategies, business models, analysis of marketing opportunities and environment. Market and customer analysis, contact strategies, marketing channels, integration challenges, personalization of marketing content, monitoring customer activities, managing marketing effectiveness, event-based marketing, real-time marketing.
Game theory and its application: Antagonistic games with simultaneous and sequential moves, Nash equilibrium and how to find it, pure and mixed strategies. Business applications: bargaining, auctions, negotiations. Computer simulation.
Challenges in software system development and project implementation: Presentation of the entire software project development process, with an emphasis on solving problems encountered in larger projects.
Use of generative artificial intelligence in BI: Generative artificial intelligence (AI), such as advanced language models (e.g., GPT), brings new opportunities for automating data analysis processes, predicting trends, and generating business reports. The use of generative AI enables faster and more personalized analysis of large datasets and the creation of scenarios based on historical data. Additionally, generative AI systems can support business decisions by offering multiple solution options and predictions, improving the accuracy of strategic decision-making.
Tools and solutions: Overview of the best tools and solutions available on the BI/CI market, as well as a glimpse into upcoming technologies.