Descriptive Analytics
Insight into the past -“What happened”
Descriptive analysis or statistics does exactly what the name implies they “Describe”, or summarize raw data and make it something that is interpretable by humans. They are analytics that describe the past. The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.
The vast majority of the statistics we use fall into this category. (Think basic arithmetic like sums, averages, percent changes). Usually, the underlying data is a count, or aggregate of a filtered column of data to which basic math is applied. For all practical purposes, there are an infinite number of these statistics. Descriptive statistics are useful to show things like, total stock in inventory, average dollars spent per customer and Year over year change in sales. Common examples of descriptive analytics are reports that provide historical insights regarding the company’s production, financials, operations, sales, finance, inventory and customers.
Descriptive analytics condenses historical data into a story that has an overall theme that is relevant and useful. Examples include “sales rose 25% this quarter” and “failure prevention software resulted in 50% less server downtime in 2016.” Descriptive analytics is the type of business intelligence activity with which most people are familiar.
In general, descriptive analytics is used to understand what happened in the past as well as to assist with the building of predictive or prescriptive analytical models — which are two other types of analytics.