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

A Like-for-Like (L4L) solution is built in Power BI to enable fair comparisons of business elements like stores over different time periods. This approach ensures that comparisons only include entities that were active in both periods, correctly handling scenarios such as new store openings, closures, or temporary shutdowns. The implementation involves creating specific data tables and a bridge table in Power Query to assign a comparability status to each store on a monthly basis. This new structure is then integrated into the Power BI data model, allowing users to dynamically filter reports for comparable data without altering the core calculation measures.