Now You See It: An Introduction to Visual Data Sensemaking

The new edition of Now You See It is an extraordinary book: easy and enjoyable to read, broadly applicable, both comprehensive and concise, and beautifully produced. It is the best book I’ve seen on how to make sense of data visually—one of the most useful skills any knowledge worker can have.It is accessible as an introduction, and its depth and clarity make it a useful reference even for experienced data analysts. I’ve bought additional copies for a couple of colleagues and have personally recommended it to business analysts, product managers, software engineers, and experienced data scientists I know. (I worked as a management consultant for seven years, and founded & sold a company that built software that visualized enterprise networking data.)Something to note: this is not a how-to guide for any particular tool—Excel, Tableau, R, etc—it is a more foundational and conceptual text that practitioners can apply to the tools of their choice.If you own the first edition, the new structure, streamlined content, and the addition of part 3—how to distinguish signal from noise—breathe new life and make it even more useful as a reference.P.S. I noticed another reviewer complaining about smell and cheap-feeling paper... the copies I bought had neither of these issues.

This is yet another amazing book by Stephen Few. I recommend all of his works if you create graphs and want to communicate effectively.

We live in an era, the so-called Information Age, where data collection has become incredibly easy. The term “Big Data” gets thrown around casually as computers collect more information on us than we know how to process. Yet wise interpretation of those data is often elusive. We’re overwhelmed with it. Effective visualizations and charts can help us interpret it better, whether to present and persuade or to monitor and manage. Stephen Few, an eloquent minister-turned-data guru based in Silicon Valley, teaches us how to approach and use data from the beginning levels.Professionally, I write software for biomedical applications that use a lot of scientific visualizations for large datasets. Although I took several nuggets of knowledge from this book (e.g., q-q plots), it was not really written for an audience like me. It’s more geared towards the wider business community, for whom data collection is a way to manage engineered systems. Rigorous biomedical scrutiny of data through careful statistics is simply not covered in this book. While for most, this tendency is surely welcomed, I honestly missed the exacting statistical theory. Still, I suspect most readers will find this book very approachable with achievable aims… even when using a common spreadsheet program.Situated in Silicon Valley, Few clearly addresses an audience of those developing software with visualization technologies. Many times, he explicitly suggests features for new products. For software geeks like me, this trait is welcomed, but I understand that many business users, more interested in interpretation, might find it a bit off-putting. Nonetheless, I suggest it unwise to discard this whole book solely for that trait. This is the second book I’ve read by Few, and he consistently teaches me how to visualize and think about data in new ways – even as a scientist who is not deeply involved with business’s “bottom line.”Like many books on data visualization, this work is elegantly put together with color plates communicating graphs as models. It’s simply a well-produced, pretty book. Business readers, especially decision-makers, can and should take advantage of Few’s expert wisdom. Learning a handful of pearls can easily lead to increased performance. Those involved in visualization software and the still young field of data science can likewise gain insights from Few. Again, the statistics are light, so wider audiences can access this work without intimidation. I enjoy wrestling with an active, expressive mind like Few’s and am grateful for my experiences with his writing.

Its a good text

Such important info for people who need to translate data into consumable information, especially for the public.