Book Image

DAX Cookbook

By : Greg Deckler
Book Image

DAX Cookbook

By: Greg Deckler

Overview of this book

DAX provides an extra edge by extracting key information from the data that is already present in your model. Filled with examples of practical, real-world calculations geared toward business metrics and key performance indicators, this cookbook features solutions that you can apply for your own business analysis needs. You'll learn to write various DAX expressions and functions to understand how DAX queries work. The book also covers sections on dates, time, and duration to help you deal with working days, time zones, and shifts. You'll then discover how to manipulate text and numbers to create dynamic titles and ranks, and deal with measure totals. Later, you'll explore common business metrics for finance, customers, employees, and projects. The book will also show you how to implement common industry metrics such as days of supply, mean time between failure, order cycle time and overall equipment effectiveness. In the concluding chapters, you'll learn to apply statistical formulas for covariance, kurtosis, and skewness. Finally, you'll explore advanced DAX patterns for interpolation, inverse aggregators, inverse slicers, and even forecasting with a deseasonalized correlation coefficient. By the end of this book, you'll have the skills you need to use DAX's functionality and flexibility in business intelligence and data analytics.
Table of Contents (15 chapters)

Using across then down

Normally, when displaying a table of information and providing total rows and columns, the rows in the table total into a totals column and the columns in the table total into a totals row. Essentially, each row aggregates across the columns, while each column aggregates down the rows. However, there are certain analytical packages that include what is referred to as across then down and down then across aggregation.

In across then down aggregation, the first row aggregates normally across the columns of the table. However, in the second and subsequent rows, the total value from the previous row becomes the starting value for the first column in the next row. Similarly, in down then across aggregation, the first column aggregates normally down the rows of the table but, in the second and subsequent columns, the total value from the previous column becomes...