encountered by the text analytics as mentioned in “Challenges in text analytics”  on page 62. Even if you can reach 100% perfection in text analytics, the numbers  produced might still not reflect reality. For example, suppose that you want to analyze customer contact records that  involve a specific product failure. In this case, you want to know the total number  of units sold that are experiencing the failure or the total number of people who  have encountered the product failure. It is not practical to expect that all of the  customers might report the failure. Only some of them might call the customer  contact center. If the failure is serious, many people might report the problem. If  the failure is not serious, only a few people might call in. In the customer contact  center, the agents might inadvertently leave out critical information when  recording the problem as described by the customers.  Because of all of these factors, even after you make a significant effort to improve  the accuracy of your text analytics, you cannot determine the true number of  customers who experienced the problem nor the number of products that caused  the problem. Figure 4-2 illustrates this point. Even though the frequency counts might not be as reliable as you want, their  deviations often lead to valuable insights. For example, 10% of the calls this  month on product A were associated with problem X, and last month only 3% of  the calls on product A were associated with problem X. In this case, the change  or  deviation  is an indicator of a potential problem that is worth further  investigation. That is assuming that no surge in selling product A in recent  months nor more demand in using product A in recent months has occurred.  Making the example more concrete, let us use the paint pealing complaints on  toy series. If complaints on paint peeling for toy X series have been increasing  from June 2013, the company should investigate to see why there is a change in  the complaints frequencies.  64  IBM Watson Content Analytics: Discovering Actionable Insight from Your Content Figure 4-2 Frequency of textual items not matching reality Number of customers who experienced specific problem X GLYPH<g143> Number of texts that describe X Number of texts identified as describing X GLYPH<g143> Number of customers who reported X GLYPH<g143>