Znanstvena metoda: Strukture 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 uvajanja, najboljše poslovne prakse, definicija poslovne analitike in primeri uporabe, pregled razlik med poslovno inteligenco in poslovno analitiko, 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 definiranje poslovnih problemov, inteligentno analitično modeliranje za reševanje poslovnih/tržnih problemov, ovrednotenje in prenos rezultatov v poslovno prakso, pregled tipičnih poslovnih problemov.
Strategije trženja in neposredno trženje: Poslovne strategije, planiranje in razvoj strategij, strategije neposrednega trženja, poslovni modeli, analiza trženjskih 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,
trženje v realnem času.
Teorija iger in njena uporaba: Antagonistične igre s hkratnimi in zaporednimi potezami, Nashevo ravnovesje in kako ga najti, 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 problemov, na katere naletimo pri večjih projektih.
Uporaba generativne umetne inteligence v BI.
Orodja in rešitve: Pregled najboljših orodij in rešitev na trgu za BI/CI, vpogled v prihajajoče tehnologije.
Scientific Method: Scientific knowledge structures, scientific activities/processes.
Introduction: Definition of intelligence and business intelligence (BI), basic BI schema, criteria, reasons and areas for adoption, common problems and pitfalls, best practices, definition of business analytics (BA) and some use-cases, review of differences among BI and BA, best practices.
Data handling: Data warehousing, data quality, data preparation/enhancement, data migration, data mediation, examples of major pitfalls.
Predictive business analytics: Business problem detection, analysis, and definition, analytical modeling for solving business/marketing problems, evaluation and business adoption of modeling results, overview of various industry.
Marketing strategies and direct marketing: Business strategies, strategy planning and
development, direct marketing strategies (product, offer, media, distribution and creative
strategies), business models, analysis of marketing opportunities and environment. Customer/market analysis and research, contact
strategies, marketing channels, integration aspects, creative tactics, content personalization,
response tracking, marketing performance management, event-driven marketing, real-time marketing, best practice examples in various
industries.
Game theory and its applications: Simultaneous-move (static) and sequential-move (dynamic) non-cooperative games, Nash equilibrium and how to find it, pure and mixed strategies. Business applications: bargaining, auctions, negotiations.
Challenges in software engineering and project
implementation: A detailed overview of development of software project with the emphasis on understanding problems that are specific to big software projects.
Applications of generative AI in BI.
Tools and Solutions: Overview of best-of-breed BI/CI tools and solutions in the marketplace, insight into emerging technologies.