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How to Build a Moving Average in Power BI Using DAX: Rolling Average & Trend Analysis Guide In our previous post, we looked at the speed gap compared to the past through Prior-Year Cumulative analysis (Part 3). In this Part 4, we will cover the Moving Average , a technique that filters out sudden spikes in data to reveal the true underlying health of your business. In business reporting, weekly sales often fluctuate wildly due to events or promotions. If you only look at the figures for a single week, it’s easy to fall into short-term judgments like "We failed this week" or "We hit the jackpot." What we really need is the true trend—stripping away the "data fog" or noise. However, Power BI's default filters try to show only the data for the current row. Here, the ALL function becomes the key to breaking down those filter walls and reconnecting past data to the present. "In this tutorial, you will learn how to build a Moving Average in Pow...

Analyzing Growth Gaps with Cumulative YoY (Periodic Comparison) The most frequent question that pops up in business reporting is this: "Our cumulative sales through November hit $500k... but are we actually doing better than last year?" Simple month-over-month YoY comparisons are heavily influenced by seasonality and promotion timing. However, Cumulative YoY compares the actual growth velocity, showing the true trajectory of your business much more accurately. In this tutorial, you will learn how to create a Cumulative YoY (Year-over-Year) growth analysis in Power BI using the DAX ALL function and week-based filtering.   1. Practical Example Data Structure We will use the same example from Part 2: Selective Accumulation. The accumulation baseline is set to Week 27 (W27), which marks the start of the second half of the year, rather than a specific calendar date. For a detailed look at the data structure, please refer to [Link - Part 2 Selective Accumulation]. ① 2023 Sales Dat...

In our previous post, we explored the structure of a standard Running Total that accumulates from the beginning of the year. While standard Power BI functions like TOTALYTD are convenient, they have limitations when an analyst needs to start accumulation from an "arbitrary point in time." In this guide, we will explore how to create selective accumulation in Power BI using the DAX ALL function. In practice, there are many more occasions where you need to track performance starting from the day a specific campaign or event began, such as: "What was the initial performance since the new product launch?" "How much revenue was accumulated specifically during the Black Friday campaign period?" The core of this part is understanding the calculation logic that breaks the existing flow of time and rebuilds energy from a designated Baseline by utilizing the "Table Provider" role of the ALL function.   1. Practical Example: Capturing 'Cumulative Imp...