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 models
• Relationships are critical
• Filter context changes everything
• Measures and calculated columns behave fundamentally differently
• Understanding context transition is essential

Bernat 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 efficiently
• Edit measures faster
• Access advanced model properties
• Work with calculation groups
• Build reusable automation scripts
• Improve semantic model governance
• Optimize development workflows
• Automate repetitive tasks

For 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 automatically
• Build reusable semantic model patterns
• Create dynamic measures
• Standardize formatting
• Reduce manual development work
• Improve consistency
• Eliminate repetitive tasks
• Scale semantic model development

According 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 generation
• DAX UDF automation
• Calculation group templating
• Semantic model standardization
• Metadata-driven development
• Dependency analysis
• Measure reuse across reports

By 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 KPIs
• Semantic models grow rapidly
• Business logic must stay consistent
• Governance becomes more complex
• Teams struggle with duplicated logic

His 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 deeply
• Visualization design remains critical
• Human understanding is irreplaceable
• Context drives analytics value
• Semantic modeling stays foundational
• AI should augment — not replace — BI expertise

He also explains why many organizations still struggle with fundamental data organization and reporting maturity long before advanced AI capabilities become relevant.

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