  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 Analytics.
