<?xml version="1.0"?>
<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Visualization of data analytics using BIM models for enhanced decision-making</dc:title><dc:creator>Silva Guilherme,	Igor	(Avtor)
	</dc:creator><dc:creator>Cerovšek,	Tomo	(Mentor)
	</dc:creator><dc:creator>Starc,	Andraž	(Komentor)
	</dc:creator><dc:creator>Bečan,	Miha	(Komentor)
	</dc:creator><dc:subject>master thesis</dc:subject><dc:subject>building information modelling (BIM)</dc:subject><dc:subject>data analytics (DA)</dc:subject><dc:subject>real-time data integration</dc:subject><dc:subject>Power BI</dc:subject><dc:subject>BIM &amp; Power BI</dc:subject><dc:subject>BIM dashboard</dc:subject><dc:description>While analytics are now fundamental for guiding investment and operations in modern infrastructure, a significant disconnect persists. Despite the widespread adoption of Business Intelligence (BI) tools, the strategic potential of Building Information Modelling (BIM) remains largely untapped within analytic workflows, particularly in the energy transmission and distribution sector. The present work focuses on tackling this implementation gap. It details the design and deployment of an original, BIM-connected analytics pipeline developed for ELES, Slovenia’s transmission system operator. The core of this work establishes a robust link between existing BIM models and enterprise data within Microsoft Power BI. This connection is forged through stable identifiers, primarily the organization's own alphanumerical tags filled into the models, supplemented where possible by the universal IfcGUID. The research operationalizes and critically compares four distinct technical pathways for this integration: VCAD, the Autodesk Data Connector, Speckle and Flinker Connectors. The main contributions of this work are many. It provides a reproducible blueprint for integrating BIM with Power BI in a live operational environment. It establishes a transparent, organization-specific mapping strategy for identifiers. The findings equip ELES and similar entities with actionable insights for embedding rich spatial context into their analytics, ultimately enabling a shift from reactive oversight to earlier, data-informed decision-making.</dc:description><dc:publisher>[I. Silva Guilherme]</dc:publisher><dc:date>2025</dc:date><dc:date>2025-09-23 08:46:21</dc:date><dc:type>Magistrsko delo/naloga</dc:type><dc:identifier>173785</dc:identifier><dc:identifier>UDK: 004:69(043.2)</dc:identifier><dc:identifier>VisID: 174762</dc:identifier><dc:identifier>COBISS_ID: 250565635</dc:identifier><dc:language>sl</dc:language></metadata>
