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.
Preface
Thinking in DAX
Tangling with Time and Duration
Transforming Text and Numbers
Evaluating Employment Measures
Processing Project Performance
Calculating Common Industry Metrics
Solving Statistical and Mathematical Formulas
Applying Advanced DAX Patterns
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Working with date intervals

There are many scenarios involving dates where you have two or more sets of dates as items or transactions move through a process. It is often desirable to know how many items or transactions are in one state or another at any given time. Unfortunately, this is something that is not straightforward to present in a report given that, with just the raw data, it is difficult, if not impossible, to depict how and when items and transactions transitioned from one state to another.

This recipe presents a simple scenario where help tickets are opened on one date and then closed. This recipe demonstrates how to see how many tickets are in process (open) at different date intervals, such as by year, month, and day.