Predictive Analytics

Introduction

Predictive analytics, which is also referred to as predictive modeling techniques, are used in a variety of disciplines and fields of study such as business, management, engineering, marketing, technology, actuarial science, information systems, health informatics, and education. Predictive modeling methods are quantitative statistical techniques that are used most often to make future business predictions and decisions based on past historical data (Evans & Lindner, 2012). Kuhns and Johnson (2013) defines predictive modeling as “the process of developing a mathematical tool or model that generates an accurate prediction” (p. 2). Their uses become more common place due largely to

• 1.

  Collecting massive amount of data, which is referred to as “big data”; and

• 2.

  Increasingly complex nature of related predictive research and problems.

The main objective of predictive modeling is to predict an unknown value of a dependent (outcome) variable from known values of a set of exploratory independent (predictor) variables by analyzing and capturing the relationships between the dependent and independent variables in any research problem (Maisel & Cokins, 2014; Siegel 2014). The results and findings of these analyses are then used to make future predictions such as predicting specific future trends, risks, and behavior patterns. Consumer purchasing patterns, consumer loyalty, credit risks, credit limits, tax fraud, unemployment rates, and consumer attrition are examples of such predictions of future trends and consumer behavior patterns that the businesses and organizations often deal with.

Generally, predictive modeling is a complex data analytic process, of which predictive model building is only a part of the analytic process. The predictive analytics process includes:

• 1.

  Understanding the predictive research problem and the massive data to be analyzed.

• 2.

  Managing the data, preparing the data for analysis.

• 3.

  Analyzing the data and building the analytic models.

• 4.

  Evaluating the results and accuracy of the predictive modeling.

• 5.

  Deploying and tailoring the final models to directly addressing the original predictive research problem.

One of the most difficult tasks for predictive analysts, researchers and students conducting predictive modeling is identifying the most appropriate predictive analytic technique that can be utilized to answer a particular research oriented business question. In addition, the type and number of the dependent variables are two important primary factors to determine the appropriateness of the predictive modeling technique that can be used for a particular business problem. Provost and Fawcett (2013) stated that the success in today’s data-driven businesses requires being able to think about how to correctly apply the principles, concepts, and techniques of predictive modeling to particular predictive business problems.

With the rise of using the internet and electronic devices (e.g., smart phones) to collect massive amount of data as well as technological advances in computer processing power and data storage capabilities, the demand for effective and sophisticated knowledge discovery and predictive modeling techniques has grown exponentially over the last decade. These knowledge discovery and predictive analytics techniques help business executives and policy makers make informed decisions to solve complex organizational and business problems. For example, the survival of businesses and organizations in a knowledge-and-data driven economy is derived from the ability to transform large quantities of data and information to knowledge (Maisel & Cokins, 2014; Siegel 2014). This knowledge can be used to make smart and informed data-based decisions and predictions using predictive analytics techniques. In fact, a decade ago, most such data was not collected or entirely overlooked as a key resource for business and organizational success because lack of knowledge and understanding of the value of such information (Hair, 2007).