Employing the Agile Approach in Big Data Analytics using Iterative and Adaptive Model Technique (ITAM)

Abstract

Colossal quantities of data get generated every day, which can yield valuable business information. Big Data analytics projects are aimed to leverage this data generated and glean it of business insights. As more and more businesses start to invest in big data analytics, project management techniques tailored for analytics projects may be devised, to save cost, time and effort. Consequently, a new branch called Agile big data analytics has developed which focuses on using agile techniques on analytics projects involving Big Data or otherwise. Thus, the agile approach which can be extended to managing projects may also be used to efficiently manage analytics projects. Iterative discovery is the key to lending agility to such Analytics and Intelligence based projects. In this article, we explore a method to extend the agile approach to big data called ITAM- Iterative and Adaptive Model Technique. Here the projects are completed in phases which consist of ideation, development of proof of concepts; analysis of the model; implementation and scaling wherein the models proposed undergo iterative improvements to provide greater project success. Keywords: Agile Analytics; Big Data Management; Agile Manifesto, Business Intelligence, Data warehousing; big data modeling