Figure 4-3 shows a collection of all the content analytics miner views. Each view in the content analytics miner provides its own unique kind of insight  from your content analysis. Analysis of distributions based on day of week and  month of year in the  Deviations view  often leads to interesting insights. You might  be able to find unexpected patterns of weekend activities and seasonal  differences. With the  Trends view , you can also obtain actionable insights. If  something undesirable is increasing, you might need to reverse the trend. The  Facet Pairs view  unveils remarkable associations if you select the appropriate  pairs of facets. Repeated selections of different facets to compare is crucial to  your success. Content Analytics is designed to be interactive and scalable for  repeated iterations through your data. To understand the content of the data, it is essential to read the original text with  the  Documents view . Some people misunderstand  text mining  as something that  allows you to avoid reading the actual documents. Even though text mining  allows you to avoid reading the entire amount of textual data, it still requires you   Chapter 4. Understanding content analysis  71 Figure 4-3 Content analytics miner views