We began this chapter with a discussion of the Tableau Data-Handling Engine (DHE). This illustrated the flexibility Tableau provides in working with data. It is important to understand the DHE in order to ensure that data-mining efforts are intelligently focused. Otherwise, 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 deployment that they forget or short-change 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 cleansing data. The data-cleansing capabilities represented by the regular expression...