A Specification Framework for Big Data Initiatives

A Specification Framework for Big Data Initiatives

Anh D. Ta (Advanced Technology Consulting, LLC, USA), Marcus Tanque (Independent Researcher, USA) and Montressa Washington (Independent Researcher, USA)
Copyright: © 2015 |Pages: 18
DOI: 10.4018/978-1-4666-8122-4.ch015
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Abstract

Given the emergence of big data technology and its rising popularity, it is important to ensure that the use of this avant-garde technology directly addresses the enterprise goals which are required to maximize the Return-On-Investment (ROI). This chapter aims to address a specification framework for the process of transforming enterprise data into wisdom or actionable information through the use of big data technology. The framework is based on proven methodologies, which consist of three components: Specify, Design, and Refine. The recommended framework provides a systematic, top-down process to extrapolate big data requirements from high-level technical and enterprise goals. The framework also provides a process for managing the quality and relationship between raw data sources and big data products.
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Background

Big Data

As more aspects of business operations are automated, the volume of data collected is increasing exponentially. This growth introduces the field of big data-analytics (or big data). This study is also intended to provide strategic and technical approaches for rapidly leveraging large volume of data required to support the enterprise decision making process. The theory of big data is more than working with a large volume of data, but rather about exploring innovative solutions to overcome the existing constraints of conventional methods. This approach, however, is also aimed for distributing the processing and leveraging data, where it resides versus transferring the data to the processing environments. Big data-analytics is defined by Gartner as “… high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information process for enhanced insight and decision making.” (Gartner, 2013; Sicular, 2013) In today’s era, data is only useful for the decision-making process, if converted into wisdom or actionable information. Generally, the conversion of data into wisdom should follow the following hierarchy of cognition, i.e., data, information, knowledge, and wisdom (Ackoff, 1989). The insights gained from big data initiatives are contingent on the size and scope of available data. To date, IT organizations are beginning to assess different ways to leveraging external data. Typically, this concept is referred to as open data. Open data is combined with existing proprietary data to improve business insights (Chui et al., 2014). International Data Corporation (International Data Corporation, 2013) predicts that big data technology and services market will grow at a 27% compound annual growth rate (CAGR) to a high of $32.4 billion through 2017. Such prediction is a result of the gradually ascending growth rate resulting in six times more than the overall information and communication technology (ICT) market. As with any new technology, the majority of available research and publications on big data, focus on either software product features or a wide-ranging of technical implementation details. On the contrary, how big data solution must be integrated into the enterprise Governance, Risk and Compliance (GRC) process and service delivery lifecycle is paramount.

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