Diagnostic Analytics is the process of analyzing to find the cause of the emergence of data and it is very essential since it gives detailed information about why certain things happened. This can be done after collecting data and aggregating information through descriptive analytics.
While descriptive analytics provides insights and specific trends about the past, diagnostic analytics investigates additional factors that have contributed to those trends. It can assist in identifying deviations from the mean, separating patterns, and finding data relationships and also helps in understanding why something happened to the past data*.
Diagnostic analytics (as a natural extension of descriptive analytics) employs exploratory data analysis of the existing data using tools and techniques like visualisation, drill-down, data discovery, and data mining in order to identify/discover the root causes of a given problem.* Drilling down means focusing on a certain feature of the data or a particular widget. Data mining is a process for extracting information from vast amounts of unstructured data that is done automatically.*
Diagnostic analytics (and descriptive analytics) are also called Business Intelligence, and the other two (predictive analytics and prescriptive analytics) collectively called Advanced Analytics. The logic behind calling a part of the taxonomy advanced analytics is that moving from descriptive to predictive and/or prescriptive is a significant shift in the level of sophistication and therefore warrant the label of “advanced”.*
