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Impacting Big Data analytics in higher education through Six Sigma techniques

Chad Laux (Department of Technology Leadership and Innovation, Purdue University, West Lafayette, Indiana, USA)
Na Li (Department of Technology Leadership and Innovation, Purdue University, West Lafayette, Indiana, USA)
Corey Seliger (Department of Computer and Information Technology, Purdue University, West Lafayette, Indiana, USA)
John Springer (Department of Computer and Information Technology, Purdue University, West Lafayette, Indiana, USA)

International Journal of Productivity and Performance Management

ISSN: 1741-0401

Article publication date: 12 June 2017

Abstract

Purpose

The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles.

Design/methodology/approach

The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data.

Findings

The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization.

Research limitations/implications

The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects.

Practical implications

Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data.

Social implications

HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve.

Originality/value

This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

Keywords

Citation

Laux, C., Li, N., Seliger, C. and Springer, J. (2017), "Impacting Big Data analytics in higher education through Six Sigma techniques", International Journal of Productivity and Performance Management, Vol. 66 No. 5, pp. 662-679. https://doi.org/10.1108/IJPPM-09-2016-0194

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited