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Data science and digital business
Márquez F., Lev B., Springer International Publishing, New York, NY, 2019. 316 pp. Type: Book (978-3-319956-50-3)
Date Reviewed: Sep 9 2019

To quote the editors, “This book combines the analytic principles of digital business and data science with business practice and big data.” To this end, the 16 chapters, each written by a different group of authors, include several data science applications, some popular algorithms, the data environment, and a mathematical treatise on analytics techniques. Each chapter presents a unique topic in the data science and analytics domain.

Following the editors’ introductory chapter, chapter 2, “Data Management and Visualization Using Big Data Analytics,” presents two analytics case studies. The first examines a logistics problem in Dutch harbors with an eye toward predicting arrival time. The second examines individual lifestyles via social media data across two US cities. Big data applications for healthcare, finance, social media, and telecom are also included.

Chapter 3, “Data Science and Digital Business,” describes an application that models hurricane damage in coastal areas. Storm surge and flood risk damage is modeled using Katrina and Rita hurricane data and their impact on the US Gulf Coast region. Of particular interest is a regression model for risk prediction.

Chapter 4, “Data Science and Conversational Interfaces: A New Revolution in Digital Business,” focuses on Alexa- and Siri-like communication, as well as telcom in commercial businesses. The authors provide a good discussion on natural language processing (NLP), voice and message generation, and classification models used with NLP.

Chapter 5, “After 2017: Managers Exit and Banks Arise,” discusses potential and actual changes at a Japanese bank as a result of blockchain technology.

Chapter 6, “Synergies Between Association Rules and Collaborative Filtering in Recommender System[s],” is a general discussion of recommender systems and collaborative filtering. The authors then provide an illustrative model for a recommender system that targets Chinese auto buyers.

Chapter 7, “Information Security Research Challenges in the Process of Digitizing Business: A Review Based on the Information Security Model of IBM,” examines research publications in six top-tier information systems journals and compares these academic studies to industry requirements.

In chapter 8, “Deploying a Scalable Data Science Environment Using Docker,” the authors use published definitions for data science, the data science skill set, and the data science stack. From here, they lead you into containers for data science and introduce Docker containers. Finally, they focus on the data science stack project, an integration of languages, libraries, and platforms. A meteorological case study is presented.

Chapter 9, “Clean Up CHAOS and Use E-CRM (a Digital Concept) to Expand the Business,” focuses on customer relationship management (CRM) with a short commentary on its application in Pakistan.

Chapter 10, “Furthering Big Data Utilization in Tourism,” reports on a study that used mobile phone tracking to capture visitor information at different tourist locations in Japan.

Chapter 11, “C-LASSO Estimator for Generalized Additive Logistic Regression Based on B-Spline,” presents the mathematics for this classifier model.

Chapter 12, “An Efficient Bundle-Like Algorithm for Data-Driven Multi-Objective Bi-Level Signal Design for Traffic Networks with Hazardous Material Transportation,” presents a min-max model for the transport of high-consequence hazmat materials.

Chapter 13, “Advanced Regression Models: Least Squares, Nonlinear, Poisson, Binary Logistics Regression Using R,” illustrates each of the regression models stated in the title via R. A brief explanation of each model is provided, along with multiple illustrations. The R code required is also presented for each.

Chapter 14, “Student Desertion: What is and How Can It Be Detected on Time?,” discusses a study of student attrition in Chile. The authors provide a description of the study, a literature review, and model options. The study was conducted in a University of Chile business school program.

Chapter 15, “Bioelectric Potential Plant,” describes a Japanese study using plants that emit a low electrical signal to track and monitor an individual in a small living space. Japan has a large number of seniors living alone. The chapter describes the study, the experiment, the neural classifier model, and the results.

The International Society of Automation standard ISA-18.2 defines an alarm standard. Alarms can be false, nuisance, missed, or chattering alarms. Chapter 16, “False Alarms Management by Data Science,” discusses alarms, data from alarms, the management of alarms, and the analysis of industrial site alarm data, including tech equipment alarm data.

The broad range of applications covered makes for interesting reading. However, the applications largely focus on business. Several chapters require a more than casual acquaintance with mathematics. Ditto for certain technologies. Having knowledge of classifier models such as clustering, neural nets, Bayesian analysis, and logistic regression can be helpful. Each chapter includes a lengthy bibliography and many are referenced directly in the text.

This is not a textbook or technical reference manual. If you have an interest in analytics and visualization, and would like to review several applications of classification models, you may find it an interesting read.

Reviewer:  Robert M. Lynch Review #: CR146686 (1912-0431)
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