Solutions for Data Science

AI-driven interaction with semantic models is starting to reshape the Power BI conversation. With Microsoft introducing MCP server capabilities (Preview), AI agents can interact directly with models — reducing the gap between a business question and a data response. From the perspective of delivering Power BI solutions across multiple enterprise clients, the potential is clear — but so are the responsibilities. Where this can help • Lower barrier for business users to explore data • Faster insight cycles through conversational access • Streamlined development and analytical workflows Where caution is required 🔐 Security, governance, and guardrails When AI agents interact with live semantic models, the exposure surface expands. This isn’t just about authentication — it’s about: • Enforcing robust role-based access and data segmentation • Monitoring query behavior and usage patterns • Preventing unintended access paths to sensitive datasets • Establishing guardrails around agent c...

Power BI Version Control — What Microsoft Recommends (and Why It Matters) If you're developing Power BI content in a team, stop treating .pbix files like final artifacts. Start treating Power BI like a software product. ✅ Use .pbip (Power BI Project) instead of .pbix ✅ Integrate with Git for proper source control ✅ Work in feature branches ✅ Merge via Pull Requests ✅ Deploy across Dev → Test → Prod workspaces Why this shift? .pbix is a binary file. You can't diff it. You can't properly review changes. You can't merge it cleanly. .pbip is text-based and folder-structured, separating: Report metadata Semantic model definition DAX queries Settings and resources This enables: Version history tracking Code reviews CI/CD pipelines Automated validation Scalable team collaboration Modern BI teams should operate like engineering teams. Governance, branching strategy, environment promotion, and source control are no longer optional — especially in Microsoft...