Descriptive Analytics

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The indirect mechanism of descriptive analytics may be in operation, and that the value of descriptive analytics is as a device that allows firms to assess the value of other technologies*.

Descriptive analytics is the type of analytics that is most understood and widely used. Being the earliest type of analytics to be introduced, it is by far the simplest to implement, since it provides a description of the data “as is” without any complex calculations. When compared to other models, descriptive analytics is more data-driven. Most businesses deploy descriptive analytics to use data to understand past and current business decisions and make fact-based decisions*.

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The adoption of descriptive analytics was associated with adoption of additional prospecting and personalization technologies. It was potentially associated with a decrease in advertising spending*.

Descriptive analytics deploys visualization to a great extent. The functionality extends to obtaining standard or customized reports, drilling down into the data, and using queries on the data to better understand the trends and patterns. By categorizing, characterizing, aggregating, and classifying data, descriptive analytics convert the data into useful information for analyzing business decisions and outcomes. The summarized information can be displayed either as reports/charts or as responses to queries using SQL.

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Descriptive analytics answers such questions as;

How many customers purchased a particular product in the past five years?
What was our cost and revenue for the last quarter?
What types of products, and how many of each type did we service?
Which of the products offers the highest cost–profit margin ratio?
Which customers should we target for special discounts?
Which are the customers with high overhead cost?*

Descriptive analytics has historically been active in organizations; however, recent developments in technology and machine learning capabilities have enabled its widespread application for enhanced knowledge discovery. The research suggest that this has the potential to provide new generation applications based on modeling. Traditional machine learning is divided into logical representations and statistical ones. This uses historical data to build a predictive model.*

Business entities can build on the functionalities of descriptive analytics to move onto diagnostic analytics and predictive analytics, to explore and forecast future events using what-if analyses and models. Soon, these same entities will utilize prescriptive analytics that can help in forecasting, as well as proactive decision-making*.