Science has changed. The shift from craving for datasets to feeling overwhelmed by the amount of available data is a modern phenomenon with dramatic consequences for scientific, political, and economic developments. The issue demands a highly specified scientific discipline for optimal data management and analysis.

This nicely integrated collection of contributions is an attempt to familiarize readers with this challenging aspect of science in the 21st century. The editors draw a picture of the future of scientific data production along the lines of the grand challenges identified by the National Academy of Engineering. The chapters focus on four major fields of research: imaging analytics and in silico biomedicine, astrophysics, material sciences, and climate sciences. The editors want to enable readers to easily identify the common elements and challenges of modern research based on this introduction, so they are prepared to understand the current capabilities and future solutions for data management and analysis.

This book is elegantly written, and intended for decision-makers. However, I believe its true value would be best measured in terms of another book [1], a collection of essays edited by Tony Hey and colleagues, which set the cornerstone in data-intensive science. That highly praised and visionary book addresses the same scientific problems as this one. This raises a question regarding the usefulness of this book.

I believe that the answer to that question could be simply given by defining the book’s appropriate readership. In my opinion, it is too technical for politicians (in contrast to Hey’s book) and probably too superficial for data generators and analysts. Instead, it achieves a good balance between technical and strategic thinking. This makes it a good choice for scientific decision-makers such as directors of institutes and universities, who are in fact in a position to shape the future networking structures for global data management.