and drive improvement in non-quantitative business metrics through content  dashboards, reports, and scorecards.  With Content Analytics, you can also analyze your textual data when it is not  practical to analyze it manually. For example, if you conduct a survey with  1,000,000 people on what they do over the weekend, Content Analytics helps  you to analyze all 1,000,000 survey forms fast enough that the results may still be  pertinent to determine how best to influence the events of the weekend.  With Content Analytics, you can define many  facets  (or aspects) of your data,  with each facet potentially leading to valuable insights for various users. For  example, you might define a weekend destination facet that consists of major  places where people travel over the weekend. You might also define an activity  facet that consists of typical activities people do during their weekend travel. With  such facets, a tourist industry analyst can analyze which types of people (based  on their age, profession, gender, and other aspects) tend to travel to which  specific locations. You can further identify the types of activities they engage in  over the weekend.  In another example, you might define a shopping place facet that consists of  major places for shopping and a purchase facet that consists of items being  purchased by people. With these facets, retailers can analyze the type of people  that tend to buy particular items at a given location. Content Analytics is tool for reporting statistics and for obtaining  actionable  insights . Actionable insights is a key concept that refers to insight into data that  leads to action. Content Analytics provides far more value than just being a tool  to reduce the workload of manual analysis. Content Analytics brings the power of  business intelligence to all of your enterprise information, not just your structured  information. The result helps you achieve the most value from all your data,  regardless of its structure. Applying these same text analytics driven methodologies of natural language  process and text annotation to a search scenario further drives value into the  organization by vastly improving findability of content in a business scenario. Traditional enterprise search solutions are heavily reliant on structured metadata  about content being searched to drive faceted navigation, allowing the users to  drill down and supplement their query terms with additional filters based on this  additional information. However, many times, that metadata is insufficient or  completely missing to allow for accurate and timely searching.  1.1.3 The analytics-driven search solution 4  IBM Watson Content Analytics: Discovering Actionable Insight from Your Content