From Cheese to Fondue: A Sensemaking Methodology for Data Acquisition, Analytics, and Visualization

摘要

Although Big Data are being leveraged through proprietary means by a host of private enterprises for significant financial gain, there are comparably fewer examples of how to harness the power of massive data through analytics in order to enhance societal resilience and directly serve the public good. In this paper, we present a three-layer framework for conducting Collaborative Big Data Analytics, including data selection and acquisition, steps comprising the analytic process, and considerations for informative data visualization. With regard to data selection, we discuss the primary characteristics of so-called Big Data, namely the Six Vs of data Variety, Volume, Velocity, Veracity, Value, and Volatility. Next, we discuss some of the various analytical tools and techniques available for processing data, as well as methods for effectively visualizing the products of data analytics. In order to illustrate the utility of such a framework, we summarize findings from our participation in Orange Telecom's Data for Development Challenges in the Republic of Cote d'Ivoire and Senegal. We conclude that while the field of Collaborative Big Data Analytics holds great promise, the development of open-source frameworks for conducting layered analytics, combined with the continuation of data challenges, such as those recently held in West Africa, will help to generate more and better uses of the Big Data that have come to dominate our world.