  The landscape of analytics is changing rapidly. Much of online user
analytics, however, is based on collection of various user analytics numbers.
Understanding these numbers, and then relating them to higher numerical
analysis for the evaluation of key performance indicators (KPIs) can be quite
challenging, especially with large volumes of data. There is a plethora of
tools and software packages that one can employ. However, these tools and
packages require a quantitative competence and analytical sophistication that
average end users often do not possess. Additionally, they often do little to
reduce the complexity of numerical data in a manner that allows ease of use in
decision making and communication. Dealing with numbers poses cognitive
challenges for individuals who often do cannot recall many numbers at a time.
Here, we explore the concept of automatic analytics by demonstrating use case
examples and discussion on the current state and future of automated insights.
