I’ve been reviewing computing literature for almost two decades, have submitted over 100 reviews, and the survey format used by this paper is very common. However, at 42 pages, this is the largest survey I ever remember reading. Given how much I dislike overly long text of any sort, it’s a measure of this paper’s quality that I both enjoyed reading it and found it quite succinct and not at all bloated. It’s a history, forecast, ontology, and glossary; somewhere in here you will find something you didn’t know, and a reference you want to follow.

The best service I can provide in this review is to include an outline for your analysis. This visual hierarchy is sorely missed in the paper itself, but tables of contents (TOCs) aren’t provided for journal papers. This simple outline is a better description of the survey than any prose I can muster.

1. Introduction

2. From Data Analysis to Data Science

(a.) The Data Science Journey

(b.) Online Search Interest Trends

(c.) What Is Data Science?

3. The Era of Data Science

(a.) Datafication and Data Quantification

(b.) Data Initiatives by Governments

(c.) The Scientific Agenda of Data Science

(d.) Data Science Disciplinary Development

(e.) New Data Economy and Industry Transformation

(f.) Data Professional Community Formation

(g.) The Open Model and Open Data

4. Data Analytics: A Keystone of Data Science

(a.) Data-to-Insight-to-Decision Whole-of-Life Analytics

(b.) Explicit-to-Implicit Analytics Evolution

(c.) Descriptive-to-Predictive-to-Prescriptive Analytics Paradigm Shift

5. Data Innovation: Challenges and Opportunities

6. Data Economy: Data Industrialization and Services

(a.) Data Industry

(b.) Data Services

7. Data Education: Capabilities and Competency

(a.) Data Scientists in a Sexy Profession

(b.) What Does a Data Scientist Do?

(c.) What Makes a Good Data Scientist?

(d.) Tools for Data Scientists

8. The Future of Data Science

9. Conclusions

The text is followed by an eight-page “References” section containing hundreds of citations. If you are involved in data science, big data, data analytics, and so on, on any level, you need to read this survey.