IGLOO BI

In the previous post, we explored the core concepts of Calculation Groups. (refer to below link) The Magic of Power BI: A Complete Guide to Calculation Groups December 21, 2025 Today, we’re going to take that knowledge a step further. By the time you finish this guide, your report users will be able to toggle any metric—whether it’s Sales, Profit, or Quantity—between "Current Value" and "Prior Year" with a single click in a slicer.   1. Example Data Configuration For this walkthrough, we’ll use a standard set of base tables: 1-1. Products table 1-2. Sales table 1-3. Data Modeling   2. Creating Base Measures Calculation Groups don't work in a vacuum; they need base measures to act upon. Let's create the foundation for our analysis: 1) Click [New Measure] in the top menu. 2) Create Total Sales : SUM('Sales'[Sales Amount]). 3) Create Total QTY : SUM('Sales'[Quantity]).   3. Creating and Setting Up the Calcula...

In this post, we’re going to dive deep into Calculation Groups , often called the crowning jewel of Power BI data modeling. Far more than just another feature, Calculation Groups represent the elegance of design, drastically cutting down report complexity. After reading this, you can also next post below. Mastering Time Intelligence with Power BI Calculation Groups     1. What are Calculation Groups? (A Shift in Perspective) Many people view Calculation Groups as just another tool for creating measures, but a more accurate definition would be: "Calculation Groups aren't about creating numbers; they’re about designing how we interpret those numbers." For example, if you have three measures—Sales, Cost, and Profit—and you want to calculate Year-to-Date (YTD) and Year-over-Year (YoY) for each, you would normally need nine separate measures. With Calculation Groups, you simply create one set of "interpretation rules" (YTD, YoY) and apply them dynamically to any me...

If you've ever tried to place a small visual in Power BI and frustratingly found that increasing the X-axis label font size has absolutely no effect—like setting the size to an exaggerated '30' in the formatting panel with zero change on the chart itself—you’ve run into this common issue. The root cause is Power BI’s default 'Responsive' option. Let’s dive into how to manage this setting to nail both report readability and design.   1. Why the X-Axis Label Size Stays Small Despite setting a large font size like '30' for the X-axis labels (as seen in the image), the font refuses to grow because Power BI’s automatic optimization overrides your manual settings. The 'Responsive' Feature at Work: Power BI defaults the 'Responsive' option to On to ensure visuals look consistent across different screen sizes (desktop, mobile, etc.). Space Constraint Override: When the chart size is small, Power BI determines, "There isn't enough space in...

1. Overview: The Strategic Importance of the Mode The Mode is more than just the most frequent value in a dataset; it represents the strongest customer preference —the core trend sustaining the business. Unlike the simple Average or Median, the Mode reflects the overwhelming choice of the majority. Therefore, it should be central to inventory management, marketing campaigns, and product strategy. In this piece, we’ll explore how to leverage Power BI’s real-time monitoring capabilities to implement a DAX-based alert system. This system instantly detects and notifies us the moment the primary trend (the Mode) changes within a specific filter context, such as region or age group.   2. DAX Solution Implementation: Creating the Alert Flag The heart of the notification system is creating a binary measure—the Alert Flag—that determines if the Mode calculated in the current report filter context differs from the baseline Mode of the entire dataset. Step 1:  Defining the Mode Baselin...

(If you missed it, please check out below first) The Hidden Hero of Data Analysis: The Mode (Part 1) : Moving Beyond the "Average Trap" to Read Your "Real Customers" The Hidden Hero of Data Analysis: The Mode (Part 2) Designing Patterns to Capture the Mainstream Using "DAX"   1. Data Introduction and Mode Analysis Plan This article utilizes a hypothetical ice cream sales transaction dataset (Total N=20) to identify the structural truth of customer behavior —the most common choice (the Mode)—which can be easily overlooked by focusing only on the mean. 1.1. Dataset Structure and Description This data consists of 20 hypothetical customer transaction records and includes five key fields. 1.2 Mode Analysis Plan Using Example Data To grasp the 'reality of the majority of customers' hidden behind the mean, we plan to focus the Mode analysis on three key fields of this sample dataset: A. Categorical Data Analysis: Preferred Flavor Goal: Identify the most ...