Devon and Cornwall Microsoft Power BI User Group

Enhance your Power BI dashboards with effective UI/UX principles! This session will cover key design strategies to improve user experience, optimize dashboard usability, and create visually compelling reports.

Learn best practices, essential design tips, and explore useful resources to take your Power BI visuals to the next level.

I’m a Data Engineer, BI and UX Consultant with a passion for building end-to-end data solutions that drive business decisions.

Whether it’s on-premise or in the cloud, I design efficient pipelines, craft insightful KPIs, and ensure businesses have the tools they need to unlock the power of their data.

My mission is to make analytics not just functional but intuitive and impactful because great insights deserve great design. 🚀

https://benixbi.com/

Profitability is a key metric; any profits can be distributed back to shareholders (owners) either directly or indirectly. A profit and loss (P&L) statement answers high level questions - such as “what was net profit last year?” - and summarises key revenue (i.e., product revenue) and expense (i.e., Research & Development) items. Unlike static reports, a P&L semantic model contains the detail; this allows end-users to ask questions such as “what was R&D spending broken down by a particular research project, fiscal period, or legal entity?.” A semantic model also allows end-users to consume information using Copilot prompts, Power BI reports, or Pivot Tables and formulas in Excel.

In this session, we will go through:
- Why you should build an income statement semantic model.
- How to use an accelerator to speed up development.
- The key questions you need to ask stakeholders.
- The challenges in building a P&L income statement.

Chris Barber is a chartered accountant (ACMA, CGMA) and Microsoft MVP. He is the author of income statement semantic models, advises Avanade/Accenture clients on building solutions using the Microsoft BI stack, and runs StarSchema.co.uk.

Building on the previous introductory session on Databricks, this talk takes things to the next level with a hands-on focus. We’ll explore how to integrate Databricks and Power BI effectively, enabling your organisation to unlock real-time analytics and create impactful data stories.

You’ll learn how to:

Leverage the Medallion architecture to design scalable and maintainable data workflows in Databricks.

Seamlessly connect Power BI to Databricks and consume data for reporting and dashboards.

Apply best practices for Direct Query vs Import, balancing real-time insights with performance and scalability.

Whether you’re a data engineer, analyst, or Power BI enthusiast, this session will provide practical techniques and lessons learned from real-world implementations to help you supercharge your analytics capabilities.

Bianca Stratulat – Databricks Champion & Chief Data Officer at UnifEye

Bianca is a Databricks Champion and Chief Data Officer at UnifEye, where she helps organisations unlock the full potential of their data using modern platforms and AI. With over 10 years of experience in data engineering, analytics, and visual storytelling, she specialises in building scalable data solutions that bridge the gap between technical teams and business leaders.

She has been a speaker at the Databricks Data + AI Summit 2025 in San Francisco and regularly presents at industry events on topics like Lakehouse architecture, real-time analytics, and best practices for integrating Databricks with Power BI.

Bianca is passionate about empowering data communities and helping teams turn complex data into actionable insights that drive innovation and growth.

https://www.linkedin.com/in/bianca-stratulat/

Microsoft added variable libraries on April 1 to markedly improve the development pipeline experience, which greatly improves the entire CICD process for releasing content in Fabric. Now to make variable libraries work, parameters need to be implemented throughout pipelines, notebooks and semantic models. These parameters will be changed when items are moved using Development Pipelines. While development pipelines were originally created for Power BI using them to deploy Fabric content were problematic, but with Variable Pipelines, they are the best way to migrate code from dev to test to prod.

We will explore the foundational concepts of parameter usage in Fabric, demonstrating how to define, manage, and adjust parameters to for different environments. The session will highlight best practices for incorporating variable libraries, showcasing their role in facilitating seamless updates across multiple notebooks and pipelines.
Attendees will gain insights into:
• The architecture and benefits of using parameters in Fabric environments.
• Step-by-step guidance on setting up and utilizing variable libraries.
• Real-world examples illustrating the impact of parameterization on workflow efficiency.
By the end of this talk, participants will be equipped with the knowledge and tools to enhance their data engineering practices, leveraging the full potential of parameters and variable libraries in Fabric to drive more efficient and scalable data solutions.

Ginger Grant is a distinguished Microsoft Data Platform MVP and Microsoft Certified Trainer (MCT), renowned for her deep expertise in advanced analytics, machine learning, AI, data warehousing, and the evolving landscape of Microsoft Fabric. As a sought-after consultant, Ginger empowers organizations to harness the full potential of their data ecosystems.

Beyond consulting, Ginger is a prolific thought leader and speaker for both keynotes and technical training. She contributes regularly as a columnist for Pure AI, authors insightful books, and shares practical knowledge on her blog, DesertIsleSQL.com. Her educational impact spans a wide range of technologies, including Azure Synapse Analytics, Python, and Azure Machine Learning, making her a trusted voice in the data community.

Whether on stage, in print, or in the classroom, Ginger’s passion for data and commitment to knowledge-sharing make her a standout figure in the world of data and AI.