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Forecast Accuracy is a critical Key Performance Indicator (KPI) that shows, as a percentage, how closely your Forecast aligns with the Actual value. Depending on the measurement objective—whether you want to assess the direction (Bias) of the error or just the magnitude (Size) of the error—this metric is utilized in two distinct ways.   1. Directional Accuracy: Features and Calculation This method focuses on diagnosing the bias or direction of the error by looking at the ratio between the forecast and the actual value. This metric is key to identifying the error's direction: whether the forecast was higher than the actual (over-forecast) or lower than the actual (under-forecast).   Bias Analysis: By analyzing the error's direction (>100% or <100%), it is highly useful for diagnosing consistent tendencies where the forecasting model or process perpetually under- or over-forecasts. Stability Assessment: This value serves as the base data for CV (Coefficient of Var...

The ADDCOLUMNS function is a powerful tool in DAX (Data Analysis Expressions) used to add calculated columns to a table. This document provides the knowledge necessary to enhance your data modeling and analysis skills by covering the syntax, usage, and various examples of the ADDCOLUMNS function. Mastering ADDCOLUMNS will allow you to make your data models more flexible and execute complex calculations efficiently.   1. Concept The ADDCOLUMNS function is a DAX function that creates a new table by adding new calculated columns to an existing table. It returns a table with dynamically added columns without altering the original source table. You can freely apply DAX expressions to the calculated columns. It is useful in various scenarios, including multidimensional analysis, KPI calculation, and period-over-period summarization.   2. Core Syntax and Operation Principle The ADDCOLUMNS function takes a table as its first argument, followed by a repetitive list of pairs: the nam...

As we've seen, standard deviation is more than just measuring 'how spread out the data is' it quantifies that spread to evaluate the stability and risk of business performance, thus serving as a key managerial analysis metric that supports strategic decision-making. However, relying solely on standard deviation for interpretation can easily lead to misjudgment. When using standard deviation for corporate, sales/profit analysis, or forecasting, the following additional factors must be considered: If you missed 'Standard Deviation' Previous contents, Please check out the below link. Standard Deviation The Complete Guide to the Core of Business Data Analysis Standard Deviation (Part 1): Measuring Data Volatility and Using the Insights for Better Strategy   1. Considering the Relationship with the Mean (Expected Value) Standard deviation represents only the absolute degree of data 'spread.' Therefore, to grasp the relative meaning of this figure, it must be cons...

Standard Deviation is a core metric that quantifies the 'dispersion' of your data, showing—as a single number—how tightly clustered or widely spread the data points are from the mean. A larger number means the data is widely spread out, while a smaller number means it's tightly grouped. Let's explore how this concept is used strategically in analyzing sales and profits. If you missed 'Standard Deviation' first contents, Please check out the below link. Standard Deviation The Complete Guide to the Core of Business Data Analysis A nd you can also refer next contents, after reading this. Standard Deviation (Part 2): Strategic Limitations and Complementary Perspectives in Standard Deviation Analysis   1. Why We Must Read the 'Wobble' (The Trap of the Mean) It's natural to first look at average sales or profit when analyzing a company's performance. The average is the central value of the data, making it the most fundamental and useful metric for gr...

  Standard Deviation  is the metric that tells you, in the original units of your data, exactly how much your data is spread out from the  Mean. Standard Deviation  is the core statistical metric we use in data analysis to figure out the variability and risk in our data. It can be incredibly powerful when utilized through the  DAX (Data Analysis Expressions)  functions available in environments like Power BI or Power Pivot in Excel. After reading this, you can also refer below 'Standard Deviation' contents . Please check out the below link. Standard Deviation (Part 1): Measuring Data Volatility and Using the Insights for Better Strategy Standard Deviation (Part 2): Strategic Limitations and Complementary Perspectives in Standard Deviation Analysis   1. Why Standard Deviation Matters: The Crucial Reason We Need to Measure 'Spread' in Statistical Analysis" 1.1. Risk Assessment Standard deviation informs you about the potential range of fluctuation—that i...

Visual Calculations in Power BI is a feature that officially rolled out in February 2024. Its main purpose is to let you perform calculations directly within a visual (like a chart or a table) without having to write complex DAX (Data Analysis Expressions). Essentially, it lets you apply calculations immediately on the report canvas without the need to create a measure in the model layer first.   1.  Background: Why It Emerged Power BI users were repeatedly running into the following roadblocks:                     The Steep DAX Learning Curve While DAX is powerful, its syntax is difficult. Advanced functions, especially those like CALCULATE , FILTER, or SAMEPERIODLASTYEAR, were particularly challenging for beginners to approach. Even for simple tasks like finding a "growth rate" or a "running total," users had to write long, complicated expressions.   Model Registration Require...