Summary

Up until this chapter, we’d looked at data that was, for the most part, well structured and easy to use. In this chapter, we considered what constitutes a good structure and ways to deal with poorly structured data. A good structure consists of data that has a meaningful level of detail and that has measures that match that level of detail. When measures are spread across multiple columns, we get data that is wide instead of tall.

We also spent some time understanding the basic types of transformation: pivots, unions, joins, and aggregations. Understanding these will be fundamental to solving data structure issues.

You also got some practical experience in applying various techniques to deal with data that has the wrong shape or has measures at the wrong level of detail. Tableau gives us the power and flexibility to deal with some of these structural issues, but it is far preferable to fix a data structure at the source.

In the next chapter, we’ll...