Article

Integrating predictive analytics into Power BI with Microsoft Fabric

March 31, 2025 · Authored by Chris Wagner

Integrating predictive analytics into Power BI and other business intelligence tools can provide significant insights and competitive advantages in today’s data-driven ecosystem. Microsoft Fabric offers a robust solution for this integration, enabling organizations to harness the power of artificial intelligence (AI) and machine learning (ML) to make data-driven decisions. Explore how to integrate predictive analytics into Power BI using Microsoft Fabric, focusing on the data flow from raw data ingestion to enriched data analysis and visualization.

Overview of the solution

Microsoft Fabric facilitates the seamless integration of predictive analytics into Power BI through a structured data pipeline. The process involves three primary sources of data: the bronze lakehouse (raw data), the silver layer (enriched data) and the semantic layer (analytical data). These data sources are consumed in Python notebooks, where AI models generate predictive analytics. The enriched data is then fed into the data warehouse in the silver layer and subsequently passed to the semantic layer for consumption in Power BI reports.

Data sources

• Bronze lakehouse (raw data): This layer contains raw, unprocessed data ingested from various sources, serving as the foundation for all subsequent data transformations and analyses
• Silver layer (enriched data): In this layer, the raw data is cleaned, transformed and enriched. This includes integrating predictive analytics generated by AI models in Python notebooks. The enriched data is stored in the data warehouse, making it ready for further analysis.
• Semantic layer (analytical data): The final layer involves creating a semantic model that links the enriched data to Power BI reports. This layer enables users to create meaningful visualizations and derive actionable insights from the data.

Predictive analytics workflow

• Data ingestion: Raw data is ingested into the bronze lakehouse using tools like Apache Spark
• Data Exploration and Visualization: Data scientists explore and visualize the data using Microsoft Fabric notebooks to understand its structure and quality
• Model training and registration: ML models are trained and registered using the enriched data in the silver layer
• Batch scoring and predictions: The trained models perform batch scoring, generating predictions that are saved back to the lakehouse
• Data warehouse integration: The predictions are integrated into the data warehouse in the silver layer, enriching the data for further analysis
• Semantic model creation: A semantic model is created to link the enriched data to Power BI reports, enabling users to visualize predictions and derive insights

Avoiding circular references

One critical aspect to consider when integrating predictive analytics into Power BI is avoiding circular references. Circular references occur when a data model refers back to itself, creating a loop that can lead to incorrect calculations and performance issues. To avoid circular references:

• Ensure clear data flow: Maintain a clear and linear data flow from raw data ingestion to enriched data analysis and visualization
• Separate data layers: Keep the bronze, silver and semantic layers distinct and avoid cross-referencing between them
• Use unique identifiers: Ensure that each data entity has a unique identifier to prevent unintentional loops in the data model

How we can help

Baker Tilly’s digital solutions team, working in collaboration with Microsoft, can help your organization begin leveraging the Microsoft Fabric ecosystem to unlock the full potential of your data and drive informed decision-making across your organization.

Interested in learning more about Fabric?