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)

Evaluating permutations and combinations

When we are performing data analysis, there are times when we wish to determine whether items are alike or not alike and how many of these like items or unalike items we have in our data. When considering such circumstances, we often have data for things that have multiple attributes expressed in multiple columns. Sometimes it is important which values are in which attributes (columns) and sometimes this is not the case.

One can think of this in terms of combinations versus permutations. With combinations, order does not matter. With permutations, order does matter.

This recipe demonstrates how to determine how many distinct things we have in our data, based upon values in multiple attribute columns. This recipe provides calculations where the order (the values in specific columns) matters (permutation) and where it does not matter (combination...