Big data, with various connotations of “big” spanning volume, veracity, velocity, and so on, is a “big” word today. Scores of books exist on various aspects of this field, from the business side to the technical side. The related field of machine learning also has a lot of literature readily available. Therefore, one expects something distinct when opening a new book on this topic. The title indicates that this book’s uniqueness is its focus on the human element of big data.

The book has four parts. Part 1, “Introduction to the Human Element of Big Data: Definition, New Trends, and Methodologies” has four chapters on topics like taming big data analytics, the fast analytics stack, big data in the context of the Internet of Things, and an analysis of costing issues. Visibly what is missing is the positioning of these chapters related to the section’s chosen title. This is the case throughout the book. I found the combination of definitions, new trends, and methodologies very odd, and the chosen topics seem to have little to do with this theme either. Most importantly, what and where is the “human element” that one does not find in other expositions on this topic?

Part 2, “Algorithms and Applications of Advancement in Big Data,” has four chapters: “An Analysis of Algorithmic Capability and Organizational Impact,” “Big Data and Its Impact on Enterprise Architecture,” “Supportive Architectural Analysis for Big Data,” and “Clustering Algorithms for Big Data: A Survey.” Look at the last chapter on clustering algorithms. Even this starts with an “introduction to big data” and devotes a good part to the Hadoop ecosystem, with no connection to clustering. Is clustering the only algorithm of concern in big data?

Part 3, “Future Research and Scope for the Human Element of Big Data,” has chapters on smart everything, social media, privacy and security, and governance. Part 4, on case studies, has chapters on “Interactive Visual Analysis of Traffic Data,” big data prospects in healthcare, big data for market prediction, and “Big Data Architecture for Climate Change and Disease Dynamics.” All these domains are well known in the big data space.

The biggest drawback I found in the book was a lack of coordination with the chapter authors. While they all follow a common format in terms of visual appearance, it appears as though very little direction was given. This leads to every chapter starting from wherever they are comfortable, including an “introduction to big data,” as well as following different conventions in terms and pictures. This spoils the continuity of the book. Above all, the editors have not attempted to build an introduction chapter that knits the various sections and chapters together. One is left to discover what each chapter is aiming at. The choices of topics for the various sections and chapters are also not explained. For example, why bring up the fast data analytics stack right in chapter 2? Chapter 4 has almost no references to “costing issues” despite its title.

However, the book does look at all kinds of things: various systems, architectures, frameworks, and trends can be found scattered throughout the book, if one has the patience to wade through it. I did not find a serious connection to the human element, and hence would prefer to classify this as a typical big data book. Although various aspects are covered, the book, due to its very organization, loses focus; hence one does not get a feel of being on top of any of these aspects. Important topics like analytics algorithms, data cleaning requirements and data quality, visualization, and so on do not receive adequate attention to make it a good resource for big data. Aspects of architecture are scattered throughout the various sections. One of the uncomfortable things about the book is the lack of margins, making reading difficult.

On the whole, there is a lot of information here; however, the book works only as an add-on, if at all.