Summary

We began this chapter with a discussion of the Tableau data-handling engine. This illustrated the flexibility Tableau provides in working with data. The data-handling engine is important to understand in order to ensure that your data mining efforts are intelligently focused. Otherwise, your effort may be wasted on activities not relevant to Tableau.

Next, we discussed data mining and knowledge discovery process models, with an emphasis on CRISP-DM. The purpose of this discussion was to get an appropriate bird's-eye view of the scope of the entire data mining effort. Tableau authors (and certainly end users) can become so focused on the reporting produced in the deployment phase that they end up forgetting or short-changing the other phases, particularly data preparation.

Our last focus in this chapter was on the phase that can be the most time-consuming and labor-intensive, namely data preparation. We considered using Tableau for surveying and also cleaning data. The data cleaning capabilities represented by the regular expression functions are particularly intriguing, and are worth further investigation.

Having completed our first data-centric discussion, we'll continue with Chapter 3, Tableau Prep Builder, looking at one of the newer features Tableau has brought to the market. Tableau Prep Builder is a dedicated data pre-processing interface that is able to reduce the amount of time you need for pre-processing even more. We'll take a look at cleaning, merging, filtering, joins, and the other functionality Tableau Prep Builder has to offer.