Book Image

Extreme DAX

By : Michiel Rozema, Henk Vlootman
Book Image

Extreme DAX

By: Michiel Rozema, Henk Vlootman

Overview of this book

This book helps business analysts generate powerful and sophisticated analyses from their data using DAX and get the most out of Microsoft Business Intelligence tools. Extreme DAX will first teach you the principles of business intelligence, good model design, and how DAX fits into it all. Then, you’ll launch into detailed examples of DAX in real-world business scenarios such as inventory calculations, forecasting, intercompany business, and data security. At each step, senior DAX experts will walk you through the subtleties involved in working with Power BI models and common mistakes to look out for as you build advanced data aggregations. You’ll deepen your understanding of DAX functions, filters, and measures, and how and when they can be used to derive effective insights. You’ll also be provided with PBIX files for each chapter, so that you can follow along and explore in your own time.
Table of Contents (17 chapters)
Free Chapter
1
Part I: Introduction
6
Part II: Business cases
15
Other Books You May Enjoy
16
Index

Personnel Planning

For a company selling large projects involving a great number of project members with different roles, it is important to be able to plan how many people are needed at which time. This is, of course, not about the number of individuals; we do the analysis in FTEs (full-time equivalents) instead. (Besides, needing 2.5 FTEs sounds a lot better than needing 2.5 people, right?)

This chapter discusses an example of personnel planning for a project-based business. As you will see, only a few DAX measures are needed to compute the global need for personnel. The main complexity is in the large number of context transformations occurring during the calculation.

This chapter covers the following topics:

  • The Power BI model for personnel planning
  • Calculating sales, both order intake and projected sales over time
  • Calculating FTEs needed for projects sold
  • Optimizing the Power BI model
  • Considering aggregation levels