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)

Creating an inverse aggregator

We are all familiar with normal aggregations, such as sum, average, maximum, minimum, and count. Usually, this is exactly what we want when analyzing data: displaying aggregated data based upon filtering that we specify. However, sometimes the desired behavior lies in seeing the aggregation of values for the items that we do not filter. In other words, we wish to instead specify the items that we do not want included in an aggregation as opposed to the items we do want included in an aggregation.

This recipe demonstrates how to implement a measure that performs an inverse sum; in other words, it sums the items that we have not selected. While this recipe performs a sum aggregation, this same technique can be used for any type of aggregation.

Getting ready...