Abstract—Analytics corresponds to a relevant and challenging phase of Big Data. The generation of knowledge from extensive data sets (petabyte era) of varying types, occurring at a speed able to serve decision makers, is practiced using multiple areas of knowledge, such as computing, statistics, data mining, among others. In the Big Data domain, Analytics is also considered as a process capable of adding value to the organizations. Besides the demonstration of value, Analytics should also consider operational tools and models to support decision making. To adding value, Analytics is also presented as part of some Big Data value chains, such the 'Information Value Chain’ presented by NIST among others, which are detailed in this article. As well, some maturity models are presented, since they represent important structures to favor continuous implementation of Analytics for Big Data, using specific technologies, techniques and methods. Hence, through an in-depth research, using specific literature references and use cases, we seeks to outline an approach to determine the Analytical Engineering for Big Data Analytics considering four pillars: Data, Models, Tools and People; and three process groups: Acquisition, Retention and Revision; in order to make feasible and to define an organization, possibly designated as an Analytics Organization, responsible for generating knowledge from the data in the field of Big Data Analytic.
Index Terms—Analytical Engineering, Analytical Organization, Big Data Analytics.
R. Rossi and K. Hirama are with the Department of Computer and Digital System Engineering, Polytechnic School of University of São Paulo, SP Brazil (e-mail: rogeriorossi8@gmail.com, kechi.hirama@usp.br).
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Cite: Rogério Rossi and Kechi Hirama, "Towards a Conceptual Approach of Analytical Engineering for Big Data," International Journal of Engineering and Technology vol. 12, no. 1, pp. 9-17, 2020.
