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1. Overview of Median Analysis In our previous discussion, we covered how the overall average can be distorted by a few high-value customers (Outliers). (If you missed it, please check out below first) Power BI DAX Integrated Analysis to Fix the Average Trap (Part 1-1): Using MEDIANX and MODEX to Find the 'Truth the Mean Hides' (And you can read next below) Power BI DAX Integrated Analysis to Fix the Average Trap (Part 1-3): Segment Efficiency Integrated Analysis: Strategic Decision Report Based on EI-M and EI-R (A Power BI + DAX Approach) Today, we will analyze strategies based on realistic purchasing behavior, focusing on the Median for each segment. We will also utilize Power BI’s Box and Whisker with Points chart to visualize each segment's median and variability (IQR: Interquartile Range) . 1.1 Summary of Segment Purchasing Distribution and Variability The table below shows the calculated Q1, Q2, Q3, and IQR results by segment. These metrics go beyond a simple compa...

The first step in data analysis is usually calculating the Mean (Average). This simple, intuitive figure is commonly used as a benchmark for setting KPIs (Key Performance Indicators) or strategic planning. However, this average has a fatal flaw: the 'Average Trap.' Because the mean is calculated by dividing the total sum of data by the count, its center can be significantly distorted by a few Outliers (extreme values). Especially in a business environment, a handful of VIP customers or one-off, huge transactions can unrealistically inflate the overall average, leading to the error of obscuring the realistic spending level of the majority of customers. To correct this average distortion and find the true center of the data, the following five key analysis methodologies are used: Median and Mode: Used to diagnose distortion by outliers and grasp realistic purchasing power levels (The focus of this analysis). Trimmed Mean: Used to correct the center by removing outliers. Standa...

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...