From DAX to Community: The Power BI Journey with Bernat Agulló Roselló (MVP)

Behind every great Power BI solution is more than just dashboards and data models. There is logic, automation, storytelling, optimization, architecture, and most importantly — community. In this episode of the m365.fm podcast, Mirko Peters sits down with Bernat Agulló Roselló, Microsoft MVP, Senior BI Developer Partner at Sabrina, Tabular Editor contributor, organizer of the Power BI & Fabric Barcelona User Group, and one of the most passionate voices in the Power BI community today. From DAX optimization and semantic model automation to community building and multilingual collaboration, this conversation explores the technical depth and human side of modern Business Intelligence. Bernat shares his journey from Excel macros and reporting automation to becoming a recognized expert in DAX, Tabular Editor scripting, semantic modeling, and enterprise Power BI development. But this episode is not just about technology. It is also about curiosity, learning, international experiences, and the incredible role that community plays in shaping careers, opportunities, and innovation across the Microsoft Data Platform ecosystem.THE JOURNEY FROM EXCEL TO POWER BI Bernat’s BI journey started long before he officially realized he was working in Business Intelligence. While working with Excel macros inside manufacturing environments like Nissan, he was already building reporting automation, aggregating data from multiple sources, and solving business reporting challenges long before terms like “semantic modeling” or “data warehousing” became part of his vocabulary. Eventually, after reading Kimball’s Data Warehouse Toolkit and diving deeper into BI concepts, Bernat recognized that he had already been practicing many foundational Business Intelligence principles for years. This realization sparked a deeper passion for analytics, Power BI, DAX, automation, and semantic modeling that continues today. WHY DAX CHANGES EVERYTHING One of the strongest technical themes throughout the episode is DAX — Data Analysis Expressions — the language behind Power BI calculations and advanced analytics. According to Bernat, one of the biggest misconceptions people have about DAX is assuming it behaves like Excel formulas. In reality:DAX depends heavily on semantic modelsRelationships are criticalFilter context changes everythingMeasures and calculated columns behave fundamentally differentlyUnderstanding context transition is essentialBernat explains how learning the foundations of DAX and semantic modeling completely changes how developers approach Power BI solutions. He strongly recommends that anyone serious about Power BI eventually studies “The Definitive Guide to DAX” by Marco Russo and Alberto Ferrari — a book that fundamentally shaped his own understanding of the platform.THE POWER OF TABULAR EDITOR Another major focus of the discussion is Tabular Editor and why it has become one of the most important tools for advanced Power BI and semantic model development. Bernat explains how Power BI Desktop works well for getting started, but as enterprise semantic models become larger and more complex, development workflows quickly become difficult to manage. Tabular Editor enables developers to:Manage large semantic models efficientlyEdit measures fasterAccess advanced model propertiesWork with calculation groupsBuild reusable automation scriptsImprove semantic model governanceOptimize development workflowsAutomate repetitive tasksFor advanced BI developers, Tabular Editor becomes a critical productivity multiplier.AUTOMATION IS THE FUTURE OF POWER BI DEVELOPMENT One of the most exciting parts of the episode focuses on automation using C# scripting, Tabular Editor, and semantic model tooling. Bernat shares how his background in Excel macros naturally evolved into Power BI automation and eventually into advanced Tabular Editor scripting. Through automation, developers can:Generate calculation groups automaticallyBuild reusable semantic model patternsCreate dynamic measuresStandardize formattingReduce manual development workImprove consistencyEliminate repetitive tasksScale semantic model developmentAccording to Bernat, automation does not just save time — it dramatically improves developer experience and mental health by removing repetitive, error-prone tasks. He estimates that automation can realistically save BI teams up to 40% of their development time.WHY REPETITIVE TASKS SHOULD DISAPPEAR One of the most practical insights from the conversation is Bernat’s philosophy around repetitive work. He strongly believes developers should spend less time copying logic, recreating measures, and manually repeating patterns — and more time solving meaningful business problems. This includes:Dynamic measure generationDAX UDF automationCalculation group templatingSemantic model standardizationMetadata-driven developmentDependency analysisMeasure reuse across reportsBy reducing repetitive tasks, teams become faster, more accurate, and more creative.THE NEXT GENERATION OF SEMANTIC MODEL AUTOMATION Bernat also shares fascinating insights into one of his latest projects: a system designed to automatically analyze semantic model dependencies and help organizations transfer KPIs, measures, and semantic logic between Power BI models safely. This becomes increasingly important in enterprise environments where:Reports share common KPIsSemantic models grow rapidlyBusiness logic must stay consistentGovernance becomes more complexTeams struggle with duplicated logicHis approach combines notebooks, DAX queries, metadata analysis, and automation to dramatically simplify enterprise BI management.AI, FABRIC, AND THE FUTURE OF BUSINESS INTELLIGENCE The discussion also explores Microsoft Fabric, AI, semantic models, and the future of analytics. Bernat remains both curious and pragmatic about AI in the BI world. While he sees strong potential in automation and AI-assisted workflows, he is also cautious about overhyping “talk to your data” experiences without proper semantic understanding and contextual design. According to Bernat:Reports still matter deeplyVisualization design remains criticalHuman understanding is irreplaceableContext drives analytics valueSemantic modeling stays foundationalAI should augment — not replace — BI expertiseHe also explains why many organizations still struggle with fundamental data organization and reporting maturity long before advanced AI capabilities become relevant.Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365--6704921/support.