Working with Amazon QuickSight Q topics

Q Topics are collections of one or more datasets that represent a subject area that your business users can ask questions about.

With Amazon QuickSight automated data prep for Q, you get an ML-powered assist to help you create a Q topic that is relevant to your end users. The first process begins with automated field selection and classification, something like this:

This automatic set of fields help the author quickly get started with natural language analytics. Authors can always exclude fields, or include additional fields, as needed by using the Include toggle.

Next, Automated data prep for Q continues with the process by automatically labeling fields and identifying synonyms. Automated data prep for Q updates field names with friendly names and synonyms using common terms. For example, a SLS_PERSON field might be renamed to Sales person, and assigned synonyms including: salesman, saleswoman, agent, and sales representative. Although you can let automated data prep for Q do much of the work, it's worthwhile to review the fields, names, and synonyms to further customize them for your end users. For example, if the users refer to a sales person as a "rep" or a "dealer" in casual conversation, then you support this term by adding rep and dealer to the synonyms for SLS_PERSON.

Finally, automated data prep for Q detects the semantic type of each field, by sampling its data and examining the formats applied to it by the author during analysis. Automated data prep for Q updates the field configuration automatically, setting formats for values used for each field. Answers to questions are thus provided in expected formats for dates, currencies, identifiers, Booleans, persons, and so on.

To learn more about working with Q topics, continue on to the following sections in this chapter.