In the previous chapter, we learned about the power of data visualizations, and the importance of having good-quality, consistent data defined with dimensions and measures.
Now that we understand why that's important, we are going to focus on the how throughout this chapter by working hands-on with data. Most of the examples provided so far included data that was already prepped (prepared) ahead of time for easier consumption. We are now switching gears by learning the skills that are necessary to be comfortable working with data to increase your data literacy.
A key concept of this chapter is cleaning, filtering, and refining data. In many cases, the reason why you need to perform these actions is the source data does not provide high-quality analytics as is. Throughout my career, high-quality data is not the norm and data gaps are common. As good data...
