What Is Descriptive Analytics?

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Descriptive Analytics

Descriptive analytics, or business intelligence, uses historical information to answer the question “What Happened?” Think of it as a rear-view mirror into business performance or a summary view of facts and figures in an understandable format to either inform or prepare data for further analysis. Observations, case studies, and surveys form the basis of descriptive analytics.

Descriptive Analytics Examples Include:

  • Summarizing past events such as regional sales, customer attrition, or success of marketing campaigns.
  • Tabulating social metrics such as Facebook likes, Tweets, or followers.
  • Reporting of general trends like hot travel destinations or news trends.
  • Discussing and debating what happened in the last iteration during a Sprint Retrospective.

Beware that descriptive analytics age rather quickly and have a sensitive time frame. As Scott Sanders of Information Services Group states, “You cannot retain and upsell customers who have already left your company.”

Other Definitions of Descriptive Analytics Include:

  • “Big data in small nuggets of information.” (Lithosphere)
  • “The examination of data or content, usually manually performed, to answer the question ‘What happened?’ (or ‘What is happening?’)” (Gartner IT Glossary)
  • The utilization of “information generated as a byproduct of operational systems.” (Kimberly Nevala)
  • “Helps answer the question ‘What happened?’ in all its forms. [It is] frequently associated with data visualization via reports, dashboards, and scorecards.” ( IBM)

Businesses Use Descriptive Analytics To:

  • Optimize supply chains.
  • Get a simple, real-time view of operations, sales, financials, customers, and stakeholders.
  • Provide inputs for predictive or prescriptive analytics.
  • Present data that can be understood by a wide variety of business readers.
  • Gauge customer preferences.

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