Following Up on Like-for-Like for Stores: Handling PY

2026-03-25 · Source: Towards Data Science · Field: Technology & Digital — Data Science & Analytics · Depth: Intermediate, short

Summary

This article details a critical refinement to a "Like-for-Like" (L4L) solution for retail stores in Power BI, addressing user confusion arising from technically correct but inconsistent previous year (PY) sales calculations for stores with temporary or permanent closures. The core issue stemmed from PY values being assigned to different L4L states than the current year's sales, leading to misaligned user expectations. The solution involved creating a new `L4LKey_PY` column in a `Bridge_L4L` table, populated via a procedural SQL script that dynamically assigns L4L states for both current and previous years based on store opening/closing dates. In Power BI, an additional relationship was established using `L4LKey_PY`, and a DAX measure was updated with `USERELATIONSHIP()` to ensure PY values consistently align with the current year's L4L state. This approach successfully delivers consistent, user-friendly results, underscoring the paramount importance of a "user's perspective" in data solution design.

Key takeaway

A procedural SQL approach resolves inconsistent Like-for-Like (L4L) previous year (PY) reporting in Power BI for stores with dynamic L4L states. It generates a `Bridge_L4L` table with a `L4LKey_PY` column, enabling DAX measures to correctly map PY values to the current year's L4L state via `USERELATIONSHIP` after failed Power Query and direct SQL join attempts. This ensures consistent, user-centric retail analytics, highlighting the importance of user perspective over technical correctness in BI solution design.

Topics

• Like-for-Like Analysis
• Power BI Data Modeling
• SQL Procedural Logic
• DAX Measures
• Business Intelligence UX

Code references

• microsoft/Power-BI-Embedded-Contoso-Sales-Demo

Best for: Data Scientist, Data Engineer, Analytics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.