Use Cases and Outlooks for Automatic Analytics
This work targets end users in analytics who struggle with data complexity, but it is incremental as it focuses on reviewing existing concepts rather than introducing new solutions.
The paper addresses the challenge of interpreting large volumes of user analytics data and relating them to KPIs, which is difficult for average users due to cognitive and skill barriers, by exploring automatic analytics through use cases and discussing its current state and future.
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.