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Data-centric business and applications : evolvements in business information processing and management (vol. 2)
Kryvinska N., Gregus M., Springer International Publishing, New York, NY, 2020. 476 pp. Type: Book (978-3-030190-68-2)
Date Reviewed: Apr 5 2021

Data-centric business and applications are successful because they are based on historical, real-time, and predictive data, which provide insight into the behavior and minds of customers, users, and managers. They also help to identify customer demands, novel features, and user interfaces, and to reduce losses.

Data-centricity therefore means that collected and produced data are the oil of applications and business. Modern technologies like the Internet of Things (IoT) assist to collect and produce such business assets, called data. Beyond data gathering and production, data analysis and analytics using different technologies, frameworks, and methods (for example, artificial intelligence (AI), machine learning, deep learning, predictive analytics, causality check/validation, correlation, and so on) drive or support businesses to fit the market and become market leaders, providing applications to fit user experiences.

The topic is incredibly important in light of the globalization and internationalization of the world economy. To be fit for the global and international market implies knowledge of other cultures, traditions, and mentalities, including economic and political cultures and structures.

This edited book presents examples of data-centric business and applications, which means using data science, including big data, calculating correlation between business turnover and data implication, as well as the causality of data use in business outcomes. However, it seems as though some of the book’s 17 chapters were intended for journal publication--in many chapters, the expression “this paper” is used instead of “this chapter.”

Unfortunately, the book is poorly structured. The scope of data-centricity is missing. Neither the editors nor authors define the scope of data-centric business in general. By reading this book, I got the impression that the editors did not adequately set the review frame and questions for the contributing authors. This leads to the book’s big weakness: the content of the vast majority of the book chapters does not match the book title. Quantitative and qualitative data analyses, and how the data supports business and applications, are totally missing in at least 12 chapters. Ultimately, in my opinion, the book fails to present the role of data in doing business or providing applications.

The book begins with agile development documentation, software testing, globalization and internalization, employment, and so on. However, none of these chapters have anything to do with data-centric business and applications. Documenting agile projects is one example: agile development documentation has nothing to do with supporting a business or applications. Since the editors do not scope the topic, readers will face confusion about the role of development documentation and data that benefits users. Agile project documentation only benefits project members like developers. Like the first chapter, the other chapters fail to direct the report so that the role of data in business or application development is clear.

The last four chapters somehow fit the book’s title, but also fail to really present the role of data (data-centricity).

Honestly, this book has little value for data scientists, application developers, or business managers. I cannot recommend it.

Reviewer:  Thierry Edoh Review #: CR147232 (2108-0197)
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