Now You See It: Simple Visualization Techniques for Quantitative Analysis

by Stephen Few

This companion to Show Me the Numbers teaches the fundamental principles and practices of quantitative data analysis. Employing a methodology that is primarily learning by example and “thinking with our eyes,” this manual features graphs and practical analytical techniques that can be applied to a broad range of data analysis tools—including the most commonly used Microsoft Excel. This approach is particularly valuable to those who need to make sense of quantitative business data by discerning meaningful patterns, trends, relationships, and exceptions that reveal business performance, potential problems and opportunities, and hints about the future. It provides practical skills that are useful to managers at all levels and to those interested in keeping a keen eye on their business.

Genres: Design, Business, Nonfiction, Science, Reference, Mathematics, Communication

329 pages, Hardcover

First published April 1, 2009

Reviews

[Lars · Rating 5 out of 5]

The reviews that complain over the lack of new or ground-breaking content are missing the point, I think; this is meant as an entry-level look at best practices in data visualization, not a technical (ish) manual like Colin Ware's book or a political statement like Tufte's ouevre. Indeed, it assumes no technical or statistical background and is on the whole extremely courteous to the reader's time and interests.

Few's books are perfect for office drones who need to produce charts and data visualization on a regular basis but do not have the formal training necessary to make these as meaningful as they should be. This category would include me, I suppose.

Yes, the reader could glean some of the insights in here through reading Tufte's books, but he/she would also have to suffer through a bog of screeds and gassy digressions to get to them (see my review of Beautiful Evidence for my somewhat unfavourable opinion on Tufte's later work).

I have now read all of Stephen Few's books. Like Show Me the Numbers, I found Now You See It jam-packed with useful insights. However, not all of them were new. There is some overlap in the content between these two books and for this reason I would not consider it essential to get both.

But I do consider it essential to get one. Recommended.

[Nathanael Coyne · Rating 5 out of 5]

A beautiful, well-designed and illustrated guide to all things data visualisation and using data viz in quantitative analysis to help communicate information and identify trends that would be difficult by just looking at the numbers. It is my go-to reference when figuring out how to represent data visually.

If you feel you already have a good grasp of the basics of data visualisation then you might be more interested in case study books like Steele & Iliinsky's Beautiful Visualization, The Wall Street Journal Guide to Information Graphics or perhaps even David McCandless.

Nathan Yau's Visualize This might be a good choice if you're looking for code samples and implementation information.

[Ben Sweezy]

I'm still not sure what this guy is gonna tell me that Tufte hasn't already. I think he may reveal Tufte's prejudices--Tufte wants every graph to be a piece of art, whereas Few recognizes the importance of dynamic connections, automated methods of visualization, and interaction. I think.

[Matt Osgood · Rating 3 out of 5]

Useful/interesting if you are a data nerd. Nothing groundbreaking.

[Michael Scott · Rating 3 out of 5]

Many pluses and minuses match the other books of Stephen Few:
+/- The target audience is the layperson starting with visualizing information, not the sophisticated crowd targeted by Edward Tufte. So, a good primer, but nothing to get excited about. Also, not enough for information visualization for scientific evidence, but a good start in that direction.

From a previous review:
+++ Good overview of what we know about information visualization. More systematic treatment than Show Me the Numbers.
+++ Excellent references: Edward R. Tufte (design of graphs and visual information elements), William S. Cleveland (design and interpretation of visual information artifacts), Colin Ware (human perception and memory model associated with visualization), John W. Tukey (statistics). Also some good references, less known: Gene Zelazny (practical guidelines on charts and slideware), Jonathan G. Koomey (high-level process from data to knowledge, Robert L. Harris (reference), Manfredo Massironi (psychology), Nancy Duarte (slideware presentations).
--/+ Reads like a good, but obvious rehash of what the others have said. Useful to traverse once.
-- Poor book design, with large imagery competing for attention with the many sections elements, and the buleted lists often obfuscating what is actually being said.

[Mike Barretta · Rating 2 out of 5]

think this is geared mostly to people with little to no experience in making data visualizations. Since I have some background, the majority of the book was not useful. However, some of the guidelines on what types of charts work best with what types of analysis was handy, as were some of the aesthetics points like ideal aspect ratios

[Donn Lee · Rating 5 out of 5]

I really like Stephen Few's books - I always learn something new every time I flip through, or am reminded of a key visualisation principle that I can make use of straightaway.

What I do sometimes find a little bit of a pain is that because this book is written in a software agnostic point of view, there are some very cool visualisations that can't be done with the software I have, and sometimes even with ANY software out there (they're the "improvements" that Few asks us, the readers, to ask the software makers about).

Took me a couple of years to finish this book, but mostly because I wasn't reading it end-to-end, and was using it more as a reference, but good to finally put it down in Goodreads. I also have his other book "Show Me the Numbers" which is similar, and also similarly good.

(By the way, not sure if you're into the aesthetics and "feel" of books, but the physical book itself is actually very nice. Wonderful thick pages with beautifully rendered images in full colour.)

[Carlos Úbeda · Rating 4 out of 5]

Parte de un curioso enfoque a medio camino entre el tratado usuario de representación de datos y el manual de uso para un potencial comprador de software de análisis de datos. Desde luego, esto le hace ser menos atemporal e inspirador que algunos clásicos del género pero también lo convierte en una referencia especialmente útil. Su clara, ordenada y exhaustiva estructura recorre conceptos básicos aportando una solidez formal muy difícil de encontrar y componiendo lo más parecido a un “libro de texto” de la visualización de información.

[Flynn Heiss · Rating 2 out of 5]

Starts out with standard principles of visualization. Most of the book reads like a wishlist for how to choose/design viz software.

There are a handful of best practices - might be informative for someone new to data visualization but I didn't find anything new.

[Anna · Rating 3 out of 5]

The writing was fast paced. Almost too much so. I had to go back countless times on certain sections to truly grasp the meaning. While I appreciate the backstory for many of the concepts, it made the reading harder.

[Ed Kless · Rating 3 out of 5]

Decent, but not near the level of Edward Tufte's work, especially Beautiful Evidence.

[Ferhat Culfaz · Rating 5 out of 5]

Excellent overview on visual display of information. Complementary to Tufte and Turkey books.

[Chloe · Rating 4 out of 5]

Helpful resource but not one of the most essential in this genre.

[amy francis · Rating 5 out of 5]

FANTASTIC books by Stephen Few on visualization. He's the master of clear and concise visualization techniques. I make a point to re-read all his books each year. The basic direction remains relevant years later. It certainly helps to be able to deliver quantitative solutions to end users that read like a book for them. Each department, each company, each division has distinct and unique reporting needs. They need to be able to slice and understand their data on their terms. This gives you the knowledge to be able to deliver that and more visually.

These books are more conceptional than instructional. You need to understand basic business intelligence programming to be able to employ the concepts.

[Robert · Rating 3 out of 5]

A good overview of different ways of visually analyzing data. Essentially an instruction manual what good data products should include to help end users, and what end users should be looking for in their software. It also gives some good pointers, not just on what types of graphs you should make available, but what users should be able to do with those graphs.

Though it's 315 pages, it's actually very short due to the number of graphs. Plus there is a lot of information laid out in earlier chapters that is repeated an quickly be skipped. I'm guess you'll read through this once, and then just look at the section headings for reference.

[Karen Mardahl · Rating 4 out of 5]

I read this book for my UX Bookclub. I never would have read it otherwise, and now I am so glad I did. It's a reference book that will be on my shelf for years. I have learned a lot from it and am now viewing statistics in newspaper articles with a completely different perspective. Another person in the bookclub agreed with me: he has started recommending this book to others. It's one that many of my peers could really use.
Despite the 300+ pages, it was an easy and enjoyable read. Nice layout, typography, and paper quality too! :)

[Ryan · Rating 2 out of 5]

A generally mediocre and long-winded book, made longer by the waste of white space. I can't believe this book was over 300 pages. I especially disliked how he keeps quoting Colin Ware in large blocks and using all of Ware's ideas. If I wanted to hear Ware's ideas I would go and read his book. Most of the ideas are half-baked and don't go far enough to satisfy the reader. There are also 2 random appendices: "How to express time as a percentage in Excel" and "How to Adjust for Inflation in Excel". Since this is not an Excel book, these appendices are strange (and random)!

[Michael · Rating 5 out of 5]

Comprehensive analysis of visualization techniques.

From the simple: use the right viz to reveal patterns rather than show a grid and let the user work it out, to the more complex: multivariate analysis - how to understand the data when there are many different data points to consider.

I found the writing style and examples very clear.

I will refer back to it as I develop in future.

Highly recommended to anyone trying to find insights or making that task easier for someone else.

[Joe Miller · Rating 4 out of 5]

Not exactly light reading, but so far it seems pretty useful. Aimed at analytic types who find themselves needing to express ideas in pictures rather than words/numbers. It doesn't get too far beyond bar/line/scatterplot graphs, and Few definitely isn't much for aesthetics. But it is (so far) a pretty good primer on what types of visuals work with what types of data.

[Blakely · Rating 4 out of 5]

Really the only thing I can fault this book with is being way to heavy to carry around in a bag - making it impossible to read on my daily train commute - and way too nice to read at the gym. Thus I was left reading it at work when I had a few minutes of spare time here and there over a period of about six months.

Definitely not the best way to absorb information!

[Vincentjones · Rating 3 out of 5]

It is for business intelligence rather than for academic or technical writing.

It is an introduction for all kinds of skills and tools rather than an detailed textbook to show you how to draw plots step by step.

I've learned some interesting software I've never heard about: Spotfire, tableau, panopticon.

[Manuel Frias · Rating 4 out of 5]

Stephen Few works as a teacher, and he must be a good one. If he teaches as well as he writes I would like to attend one of his courses.

I liked very much the best practices chapters where he explains how and when different visualization techniques can be used, from time-series to multivariate analysis.

[Jason · Rating 4 out of 5]

A very impressive introduction to data visualization techniques. Many helpful tips are provided by the author as well as some basic introductions to various types of software packages available to help data visualization and analytics. I'd rate this one somewhere between a 4.25 and a 4.5 personally, and look forward to reading more by this author.

[Max Lybbert · Rating 4 out of 5]

Stephen Few continues his effort to improve how people make charts and report numbers. Now You See It covers some ground -- such as time series and how to handle outliers -- that I hadn't seen Few cover in his other books. And Few names products that offer the features he considers necessary for clear and useful charts.

[Dale · Rating 4 out of 5]

This is a good overview of simple data visualization techniques. There's nothing really earth shattering, and most of the techniques are readily achieved in common data analysis software such as Excel, Tableau, and R.

[Genie · Rating 4 out of 5]

Follow up to Show Me the Numbers. Good intro to visual design, I learned a few new
Things and reviewed a lot. Excellent reference, though, and I enjoyed the examples and the author's passion for the topic. Glad I read it.

[Anetq · Rating 5 out of 5]

Excellent. Show and tell about data visualization og data analysis. An easy read and full of goodness. And even if you're not going to read it, take this with you: Stop making pie charts, they're not helping us understand anything!

[Lydia · Rating 4 out of 5]

This book is a great place to start learning about data visualization. It is clear that Stephen Few is constantly researching new methods and software and thinking about how to improve the way we analyze data with visuals. I would love to see an updated 2016 edition.

[Zach Olsen · Rating 4 out of 5]

For anyone who has ever had to make sense of lots of data. Easy to read, lots of great tips.

[Hom Sack · Rating 5 out of 5]

Clear and well presented. Unfortunately, it is more than I can absorb in one reading.