Building Efficient Budgeting and Forecasting Models with Microsoft Fabric – Part 2

In our recent session, we continued our exploration of building comprehensive budgeting and forecasting models using Microsoft Fabric. Here are the key insights and methodologies discussed, focusing on version control, test-driven development, and effective data integration.

Key Concepts: Budget vs. Forecast vs. Actual

Understanding the differences between budget, forecast, and actual data is crucial:

Budget: Planned financial metrics set at the beginning of the fiscal year.

Forecast: Updated projections based on current trends, adjusted quarterly or semi-annually.

Actuals: Real financial data collected from operational activities.

Our objective is to construct a model that accurately compares these datasets to perform variance analysis and derive actionable insights.

Steps to Build the Model

Define the Business Process:
  • Focus on a business process such as profit and loss (P&L) reporting.
  • Use standard processes from sources like APQC to guide the model structure.

Data Collection:
  • Gather three years of P&L data for actuals.
  • Create budget data for the upcoming financial year using Excel.
  • Develop a forecast for the next six months to one year, initially in Excel, with potential AI integration for advanced forecasting.

Data Modeling:
  • Establish the granularity of the data (e.g., monthly).
  • Create separate fact tables for budget, forecast, and actuals.
  • Include dimension tables for date, account, and department.

Implementation Using Microsoft Fabric:
  • Use Excel for budget and forecast creation due to its robust features for financial planning.
  • Integrate the data into Microsoft Fabric for seamless analysis.
  • Consider using AI tools for forecasting if the project scope allows.

Data OPS and DevOps Integration

Integrating Data OPS and DevOps practices ensures the model is built efficiently and accurately:

  • Data OPS: Focuses on optimizing the data pipeline from entry to insight. Ensure data quality, transformation, and standardization are maintained throughout the process.
  • DevOps: Streamlines the development process from idea to working code. Implement version control, automated testing, and continuous integration to manage updates and improvements.

Practical Application

Creating the Model in Power BI:
  • Start with a basic report layout, including refresh dates and metadata.
  • Use Power BI’s features to build interactive dashboards and reports.
  • Validate the model with real data to ensure accuracy.

Optimizing the Process:
  • Use Git repositories for version control.
  • Set up dev, test, and prod workspaces to manage the lifecycle of the report.
  • Automate deployment pipelines for efficient updates and maintenance.

Advanced Techniques:
  • Experiment with AI-driven forecasting models for more accurate predictions.
  • Incorporate user feedback to refine and improve the model continuously.

Test-Driven Development

Test-driven development (TDD) is an approach where tests are written before the actual code. In the context of Power BI:

  • Create Test Measures: Define expected outcomes for specific data points.
  • Validate with Real Data: Use the defined tests to validate the accuracy of your measures.
  • Continuous Improvement: Adjust and refine based on test results to ensure data integrity.


Building a budgeting and forecasting model using Microsoft Fabric involves careful planning, data collection, and the integration of advanced tools and techniques. By following these steps and leveraging Data OPS, DevOps, and TDD principles, organizations can create efficient, accurate, and insightful financial models that drive better decision-making.

For more detailed insights and practical examples, stay tuned for future posts!