Businesses are collecting massive amounts of data every day as a way to better understand their processes, competition, and the markets they serve. This data can be used to increase organizational productivity and performance; however, is essential that organizations collecting large data sets have the tools available to them to fully understand the data they are collecting.
Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics takes a critical look at methods for enhancing an organization’s operations and day-to-day activities through the effective use of data. Focusing on a variety of applications of predictive analytics within organizations of all types, this critical publication is an essential resource for business managers, data scientists, graduate-level students, and researchers.
The many academic areas covered in this publication include, but are not limited to:
Researchers in business analytics, management, and other business fields describe methods for analyzing and predicting productivity and performance. Their topics include structural equation modeling algorithm and its application in business analytics, new product development and manufacturability techniques and analytics, analytics overuse in advertising and promotion budget forecasting, mastering business process management and business intelligence in global business, a conceptual and pragmatic review of regression analysis for predictive analytics, and an analytical employee performance evaluation in office automation and information systems.