I found this to be a very interesting and insightful article on data science as it is practiced in the real world. However, your impressions of this short e-book will strongly depend on your expectations. If you are looking for an detailed and technical how-to book, then you will be severely disappointed. I think that people who will most appreciate this e-book article are either those who have very little to no experience with data science, or potentially the high-level experts and veterans of the field. For the first group this e-book could serve as a gentle introduction to the field in the most general way, while the latter could appreciate the big picture take by one of their very experienced colleagues. I am definitely in the first group, and I really enjoyed this short e-book.
The central idea of this e-book is that in design of data-driven products it helps to use the actual usage of the product as a guide and a driving force. This is where the Jujitsu metaphor comes in play: just like a practitioner of that martial arts relies on the opponent's own attacks and forces and tries to martial them to his own advantage, so also a designer of a data-driven product will ideally try to use the "gravitational pull" of data and its use to his advantage.
Patil illustrates his ideas and concepts with several useful examples, mostly from his work at LinkedIn. These are useful examples in their own right, as they also give the reader a few insights into how LinkedIn actually connects people. Some of the ideas in the book are already well known to most software designers and entrepreneurs (good data structures are crucial, try to design a minimally functioning prototype and then iterate, etc.), but others are a bit counterintuitive and novel.
This short e-book is very readable and well written, something that one can never take for granted for a geeky book on data science. It's an interesting read and I was able to finish it in a single sitting.
The book describes how to create data products in the area of web 2.0 and gives some really useful examples (Facebook, LinkedIn, etc.) and principles for data or more general software product processes.