By Syed Hussnain Sherazi | April 8, 2025 | Microsoft Fabric
Tags: Microsoft Fabric | Decision Workflow | Power BI
A step-by-step guide to building a simple decision-support workflow using Microsoft Fabric.
A practical walkthrough from raw data to a dashboard your team can use
Many people have heard about Microsoft Fabric and know the main product names: Lakehouse, OneLake, Dataflow Gen2, Shortcuts, and Power BI. What is less obvious is how those pieces fit into a normal working analytics process.
This article walks through a simple retail decision-support workflow in Microsoft Fabric. The aim is to show the flow from raw data to a dashboard, using a scenario that is realistic enough to adapt to your own environment.
Imagine you work for a retail company. Your team needs to track sales performance across regions, understand which products are underperforming, and get early warnings when a store's revenue is trending below target.
Today, the data lives in a SQL Server database in a datacentre. Someone exports it to Excel every Monday morning. The file gets emailed around. Several people open different versions, and nobody is fully confident in the numbers.
The goal is to replace that manual workflow with one that runs automatically, uses a single source of truth, and gives decision-makers what they need without spreadsheet handoffs.
Microsoft Fabric is an all-in-one analytics platform. It brings data engineering, data warehousing, data science, real-time analytics, and business intelligence into one environment. All of it sits on top of OneLake, a unified storage layer that reduces the need to copy data between tools.
You can think of Fabric as Microsoft bringing Azure Data Factory, Azure Synapse, and Power BI closer together under a simpler shared experience.
For this retail use case, we will use: