Data-Enabled Analytics: DEA for Big Data

This book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. DEA is a widely used technique for performance and productivity measurement, covering a wide range of challenges and debates within the modeling framework.

Format: Paperback / softback
Length: 364 pages
Publication date: 18 December 2022
Publisher: Springer Nature Switzerland AG

This book delves into the innovative applications and vast potential of Data Envelopment Analysis (DEA) in the realm of big data. These fields hold immense interest for researchers and practitioners alike. Given the extensive literature on DEA, it can be argued that DEA has been and remains a widely employed technique for measuring performance and productivity, encompassing a wide range of challenges and discussions within the modeling framework.

DEA has proven to be a versatile tool in various industries and sectors, enabling organizations to evaluate their efficiency and effectiveness. Its ability to handle large datasets and provide insights into complex decision-making processes has made it a staple in performance analysis.

One of the key advantages of DEA is its ability to handle multiple inputs and outputs, making it suitable for a wide range of applications. It can be used to evaluate the performance of individual units, departments, or entire organizations, and can even be applied to non-profit organizations and government agencies.

Despite its widespread use, DEA has faced criticism and debate over its limitations and assumptions. Some critics argue that DEA may not be suitable for all types of data or situations, and that its assumptions may not hold true in all contexts. Additionally, there have been concerns about the accuracy and reliability of DEA results, particularly when dealing with small sample sizes or complex data structures.

To address these challenges, researchers have developed various extensions and modifications of DEA, such as the CCR model, the BCC model, and the DEA-Malmquist model. These models incorporate different assumptions and methodologies to improve the accuracy and reliability of DEA results.

In conclusion, this book provides a comprehensive exploration of the novel uses and potentials of DEA in the context of big data. It highlights the importance of DEA in measuring performance and productivity, and discusses the challenges and debates surrounding its application. By introducing various extensions and modifications of DEA, the book aims to enhance the accuracy and reliability of DEA results and further expand its applications in various industries and sectors.

Weight: 575g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030751647
Edition number: 1st ed. 2021