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Showing posts with the label Power BI - DAX

Standard Deviation The Complete Guide to the Core of Business Data Analysis

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  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.   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 is, how far performance metrics (like sales, margin, or production volume) could deviate from the average, all expressed in their native units. A large standard deviation means there is significant  uncertainty (risk)  that future performance could be  much lower or much higher  than the average. In c...

DAX CALENDAR Function Deep Dive and Practical Usage Guide

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The CALENDAR function is essential for creating a date table in a data model, and it plays a vital role when performing time intelligence calculations. Through this document, you'll be able to get a detailed understanding of the CALENDAR function's syntax, how it operates, and how you can leverage it in real-world data analysis scenarios.   1. What Exactly is the CALENDAR Function? The CALENDAR function in DAX is basically your go-to tool for building a dedicated date table based on a specific date range. All you do is give it a start date and an end date, and it spits out a single-column table containing every single date within that range. This generated table is what we commonly call a 'Date Dimension Table. ' It's absolutely critical for your data model because you'll use it to set up relationships with other tables (like sales, orders, or inventory) and, more importantly, it becomes the foundation for all your time-based analysis.     1.1. Syntax ...