Managing Big Data Projects with Six Sigma Methodology
Presenter: Senthilkumar Thiyagarajan, Graduate Research Assistant, Purdue University, West Lafayette, IN, USA
Co-Presenter: Dr. Chad Laux, Associate Professor, Computer and Information Technology Department, Purdue University, West Lafayette, IN, USA
Co-Authors: Dr. Elizabeth Cudney, Associate Professor, Engineering Management and Systems Engineering Department, Missouri University of Science and Technology, Rolla, MO, USA; Dr. John Springer, Associate Professor, Computer and Information Technology, Purdue University West Lafayette, IN, USA.
Keywords: Big Data, Six Sigma, DMAIC Framework.
Industry: Education/Training, Financial Services, Manufacturing
For the past two decades, the Six Sigma methodology has been widely used by researchers and organizations to drive business improvements. During this time, Six Sigma was integrated with Lean Management, Supply Chain, and various other process improvement models to achieve synergized benefits. Six Sigma projects are conducted in a systematic approach to resolve business problems and sustain the achieved results via DMAIC framework (Define, Measure, Analyze, Improve, and Control). Big Data Analytics is the most widely discussed topic in current research and business environment. Companies are investing in resources and deploying technologies to adopt Big Data into their company culture for making data-driven strategic decisions. Many researchers have investigated the significance of utilizing big data to improve performance of organization. However, little focus has been given on defining a framework for handling big data projects in an efficient way. In this paper, Big Data and Six Sigma, two breakthrough paradigms, are integrated through a proposed structured framework for managing Big Data Projects. The framework aims to combine tools from Six Sigma, Big Data, and Predictive Analytics and define its application in each phase of the project. In this conceptual model, this paper will discuss on how to utilize various tools and manage each phase of Big Data project to solve major business issues in a structured approach.