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Extreme DAX

Extreme DAX

By : Michiel Rozema, Henk Vlootman
4.9 (44)
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Extreme DAX

Extreme DAX

4.9 (44)
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)
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Free Chapter
1
Part I: Introduction
6
Part II: Business cases
15
Other Books You May Enjoy
16
Index

Considering aggregation levels

The FTE calculation, as we have designed it, computes the FTEs needed at the level of project type. This means that we simply add the FTE numbers for all projects with the same type. It is a business consideration as to whether this is what the organization really wants.

For example, if an A1 project in Paris and an A1 project in London will run at the same time and each needs a 0.5 FTE project manager, we need 1 full-time project manager in total. While this is mathematically correct, you may argue that a single project manager will not be able to manage both projects. We may need boots on the ground, which practically excludes having someone travel between Paris and London all the time. The result would be that we need, in fact, two project managers.

The simple solution to this would be to do the calculation on a by-location basis, adding another iteration to the measure. However, even if we compute 0.5 FTE twice, the end result is still 1...

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