Building a Like-for-Like solution for Stores in Power BI

A walkthrough of building a Like-for-Like (L4L) store comparison solution in Power BI using Power Query and a bridge table approach. The solution classifies stores as comparable or non-comparable based on opening, closing, and temporary closure dates, then filters fact data accordingly without modifying DAX measures. Key steps include creating a Bridge_L4L table by cross-joining stores with months, applying date-based logic to assign L4L states, and linking the bridge table between the Store dimension and the Retail Sales fact table via a StoreMonthKey. Alternative approaches such as embedding L4L state in the fact table or using SCD2 historization are also discussed.