Explicit Measures Podcast

Discussion on Power BI adoption, fabric usage, and system oversight in Power BI. The importance of monitoring and decentralization in Power BI management. Challenges of balancing growth and feature limitations. Considerations for launching Power in the fabric ecosystem. Significance of REST APIs in Power BI governance.

Mike, Seth, and Tommy discuss managed self-service in Power BI, including the balance of control and agility in BI, dataset certification implications, and strategies for fostering a robust data culture.

The podcast covers topics such as optimizing data warehouses and reports, using Power BI reports server for cloud-averse companies, and developing an organizational policy for Power BI workspace creation. They also discuss new functions for table creation in a lake house, challenges of bringing custom code and libraries into Microsoft fabric, optimizing partition strategy and file size in Microsoft Databricks, and moving data to the cloud with Power BI.

Brent Dykes, Author of Effective Data Storytelling, discusses low user adoption rate in Power BI and the importance of user enablement. The podcast covers challenges of measuring success and engagement, approaches to increase user adoption, and the need for a monitoring solution for Power BI.

The hosts discuss underused features of Microsoft fabric, including OneLake File Explorer, Data Wrangler, and Shortcuts. They also explore the benefits of Azure Synapse and fabric in data engineering and analytics. The episode concludes by highlighting the importance of shortcuts in data engineering workflows and encouraging listeners to share and recommend the episode.

The podcast discusses the major updates to Git and the Power BI Project type format. They explore the use of Git workspaces and the introduction of Power BI Git Projects. They also discuss the advantages and challenges of using multiple environments in software development and the value of innovation and collaboration in business intelligence. The hosts highlight the benefits of centralized reporting and Git integration in Azure DevOps for Power BI.

Mike Ebbers, spoof video creator, joins the hosts to discuss his funny video about ordering food as a metaphor for ordering data. They explore misconnect in expectations, issues with timelines, trust issues in data analysis, challenges of handling accurate data, building a strong data culture, and the importance of structured teams and processes.

Microsoft Fabric and Workspaces. Mike, Seth, & Tommy have dived into this quite a bit already. However, what about the role based permissions in workspaces, in relation Fabric Artifacts? Link to Resources: https://learn.microsoft.com/en-us/fabric/get-started/roles-workspaces Get in touch: Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page. Visit PowerBI.tips: https://powerbi.tips/ Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitips Subscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVv Subscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎ Check Out Community Jam: https://jam.powerbi.tips Follow Mike: https://www.linkedin.com/in/michaelcarlo/ Follow Seth: https://www.linkedin.com/in/seth-bauer/ Follow Tommy: https://www.linkedin.com/in/tommypuglia/

The podcast discusses the impact of star-schema design in Analytics, Lakehouses, and Data Science. It also explores the utilization of calculation groups in Power BI, the importance of naming standards and schema in systems, and the evolution of Power BI and the Power BI Fabric Ecosystem.

Delving into the world of data visualization, the trio explores the balance between visually appealing visuals and clarity of insights. They discuss the allure of complex visuals, audience-centric visualization, challenges in data culture and governance, and the fine line between showcasing technical prowess and delivering clear messages through data visualization.