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Quality questions

Suppose there are concerns about the quality of the data to be, or being, consumed by the organization. As we eluded to earlier in this chapter, there are different types of data quality concerns such as what we called mechanical issues as well as statistical issues (and there are others).

Current trending examples of the most common statistical quality concerns include duplicate entries and misspellings, misclassification and aggregation, and changing meanings.

If management is questioning the validity of the total sales listed on a daily report or perhaps doesn't trust it because the majority of your customers are not legally able to drive in the United States, the number of the organizations repeat customers are declining, you have a quality issue:

Quality is a concern to both the data developer and the data scientist. A data developer focuses more on timing and formatting (the mechanics of the data), while the data scientist is more interested in the data's statistical quality (with priority given to issues with the data that may potentially impact the reliability of a particular study).

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