Descriptive analytics 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.
A common example of Descriptive Analytics is company reports that simply provide a historic review of an organization’s operations, sales, financials, customers, and stakeholders. It is relevant to note that in the Big Data world, the “simple nuggets of information” provided by Descriptive Analytics become prepared inputs for more advanced Predictive or Prescriptive Analytics that deliver real-time insights for business decision making.
Examples of Descriptive Analytics
Here are some common applications of Descriptive Analytics:
- Summarizing past events such as regional sales, customer attrition, or success of marketing campaigns.
- Tabulation of social metrics such as Facebook likes, Tweets, or followers.
- Reporting of general trends like hot travel destinations or news trends