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)

Working with periodic revenue (reverse YTD)

Tracking revenue per month is extremely common for many businesses and organizations. Often, this revenue tracking takes the form of either simply tracking the revenue for each month or tracking the accumulated revenue for each month since the beginning of the year. Various business systems track or report revenue in different ways. Some systems provide revenue that's already been accumulated since the beginning of the year. This recipe demonstrates how to reverse engineer the accumulated revenue and the revenue year to date into the individual periodic revenue for each month.

Getting ready

To prepare for this recipe, do the following:

  1. Open Power BI Desktop.
  2. Use an Enter Data...