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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://repozitorij.uni-lj.si/IzpisGradiva.php?id=182638"><dc:title>Business inteligence and analytics in the digital transformation between big data and network data: the Ellycode model and new frontiers of decision-making in the business context</dc:title><dc:creator>Colace,	Giuseppe	(Avtor)
	</dc:creator><dc:creator>Vitale,	Maria Prosperina	(Mentor)
	</dc:creator><dc:creator>Giordano,	Giuseppe	(Komentor)
	</dc:creator><dc:subject>business intelligence</dc:subject><dc:subject>analytics</dc:subject><dc:subject>Big Data</dc:subject><dc:subject>GenAI</dc:subject><dc:subject>Social Network</dc:subject><dc:description>In the current economic scenario, characterized by the "data deluge" and increasing complexity, Business Intelligence and Analytics (BI&amp;A) have become strategic assets for organizations. However, Small and Medium Enterprises (SMEs) often face significant barriers to adoption, including technical complexity, high costs, and a lack of specialized skills known as Data Literacy. This thesis analyzes the evolution of BI from traditional reporting to the current paradigm driven by Big Data and Artificial Intelligence, exploring how these technologies are redefining corporate decision-making.
The study focuses on the "Ellycode model," an operational approach that leverages Generative AI (GenAI) and Natural Language Processing (NLP) to democratize data access. By enabling users to query corporate data through natural language, this model shifts the focus from technical extraction to strategic interpretation, promoting a philosophy defined as "The Human Side of Data." The research highlights how this approach reduces the cognitive load for decision-makers and supports the development of a data-driven culture within SMEs.
Furthermore, the work extends the analysis beyond traditional "tabular BI" towards a "relational BI" perspective by integrating Social Network Analysis (SNA). It demonstrates how GenAI can facilitate the interpretation of complex network metrics such as centrality, cohesion, and structural holes, allowing organizations to uncover invisible dynamics and leverage social capital. Finally, the thesis addresses the cognitive risks associated with automation, such as automation bias, advocating for an effective "Human-AI Teaming" configuration. The conclusion suggests that the synergy between technological democratization and advanced relational analysis transforms BI into a prescriptive tool, essential for navigating the digital transformation.</dc:description><dc:publisher>G. Colace</dc:publisher><dc:date>2026</dc:date><dc:date>2026-05-20 07:30:06</dc:date><dc:type>Magistrsko delo/naloga</dc:type><dc:identifier>182638</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>
