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When building Power BI reports, there are times when we need to hide performance data that falls below a certain threshold. However, have you ever experienced a situation where the Grand Total at the bottom doesn't match the sum of the numbers visible on the screen, breaking the data integrity? This is an incredibly common issue in the field and is the primary culprit behind the sudden collapse of report credibility. Today, we will dive deep into one of the most fatal BLANK anti-patterns: the "Total Row Destroyer." I will explain why this is problematic and show you how to fundamentally solve this by redefining the Filter Context (the target of aggregation) rather than using temporary post-processing like DAX's ISINSCOPE. Core Questions Addressed in This Article: Why does the sum of visible numbers and the Total constantly deviate? Why does BLANK become a "trap" instead of just a "hiding" mechanism?   1. Basic Data for Analysis (Sales Table) To ...

In Power BI, a BLANK is far more than just a void. It is a calculated result, a signal of filter context, and a sophisticated analytical language that controls both visualization and performance. However, the moment BLANK is used without clear intent, your reports begin a silent decline. The numbers might look plausible at a glance. No error warnings are triggered. Users continue to trust the results, unaware of the underlying issues. This page serves as the central hub for Part 3, where we identify the 7 most common BLANK Anti-Patterns encountered in the field and connect you to four professional master guides to resolve them.   1. Why BLANK is the Most Dangerous Error The greatest risk of BLANK is its ability to remain invisible. It is far more deceptive than a standard error because: It doesn't stand out like a zero. It doesn't trigger system alerts. It quietly vanishes depending on the filter context. As a result, BLANK can mask serious issues—such as performance degrad...

Power BI’s Hidden Trap: Rows are OK, but Total is BLANK? Anyone who has spent a fair amount of time analyzing data with Power BI will eventually encounter this baffling scene: "The amounts for individual rows are displayed perfectly, but the crucial Total has vanished into a blank space." When first facing this phenomenon, most people fall into the same misconceptions: "Is it a visualization option issue? Did the 'Show Total' setting get turned off?" "Is this a bug within Power BI itself?" "Is there a problem with the data connection?" However, let me start with the conclusion: this is not a bug. It is the result of DAX (Data Analysis Expressions) performing exactly as intended. It is a natural (?) phenomenon that occurs when we haven't quite grasped the core DAX concept of 'Filter Context.' In this post, I will dive deep into why this happens when using conditional calculations like IF and RANKX, why this problem is particul...

Anyone working with Power BI has faced it at least once: that sharp question from the business side, "Why is this number empty?" In those moments, you might be tempted to just fill it with a 0. However, I learned the hard way—after ruining numerous projects—that such a temptation is a poisoned apple that eats away at a report's lifespan. Beyond simply meaning "no value," BLANK() in Power BI is a strategic signal used by analysts to control visualizations and calculations. Especially in large-scale models or executive reports, how you use BLANK structurally changes the report's credibility, readability, and performance. This document goes beyond simple functional descriptions to organize BLANK usage from an advanced analyst's perspective, including field-proven patterns and critical judgment points that AI-generated DAX often misses. If you missed previous article, please refer below link. Mastering BLANK in Power BI - Part 1: BLANK vs. 0 vs. NULL Also y...

  1. Why You Must Understand the Difference Between BLANK and 0 In Power BI analysis, the distinction between a BLANK and a 0 isn't just a matter of how a value looks. Most analysts start feeling the weight of this difference only after they’ve gained some real-world experience. Early on, it’s easy to think that as long as the numbers match, everything is fine. However, as reports grow and more stakeholders get involved, this becomes a factor you simply cannot ignore. This is a core design element that dictates aggregation results, visualization behavior, system performance, and the overall reliability of your decision-making. Especially in a data pipeline flowing from SQL to Power Query and then to DAX, the same "empty" spot can take on entirely different meanings at each layer. You can also read next article below link. Mastering BLANK in Power BI - Part 2: Advanced Analytical Patterns Using BLANK Intentionally Mastering BLANK in Power BI - Part 3: 7 Anti-Patterns Ruini...