IGLOO BI

Simply listing past performance is no longer enough to drive executive decision-making. According to search trends frequently monitored via Google Search Console, BI professionals are increasingly seeking robust solutions for 'Practical Sales Forecasting' that go beyond simple linear trends. In this final part of our series, we unlock the full potential of the DAX ALL function to construct a sophisticated 'Rolling Forecast' model. We will move past the pitfalls of simple averages to design a trajectory that reflects the true 'run-rate' of your business. From visualizing target gaps to establishing strategic decision ranges, discover the precise DAX patterns used by top-tier data analysts to map out the future. In our previous Part 4, we used the Moving Average to filter out data noise and identify the "true health" of the business. Now, it is time to answer the most c...

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...