So far, most of the examples we've looked at in this book assume that data is structured well and is fairly clean. Data in the real world isn't always so pretty. Maybe it's messy or it doesn't have a good structure. It may be missing values or have duplicate values, or it might be at the wrong level of detail.

How can you deal with this messy data? We'll consider Tableau Prep Builder as a robust way to clean and structure data in the next chapter. For now, let's focus on the capabilities that are native to Tableau Desktop, which itself gives a lot of options and flexibility to deal with data issues. We'll take a look at some of the features and techniques that will enable you to overcome data structure obstacles. We'll also lay a solid foundation of a good data structure. Knowing what data structures work well with Tableau is key to understanding how you will be able to resolve certain issues.

In this chapter, we'll focus on some principals for structuring data to work well with Tableau, as well as some specific examples of how to address common data issues. This chapter will cover the following topics:

• Structuring data for Tableau
• Unions and cross database joins
• Techniques for dealing with data structure issues
• Overview of advanced fixes for data problems