In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With Beautiful Data, you will:

• Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web

• Learn how to visualize trends in urban crime, using maps and data mashups

• Discover the challenges of designing a data processing system that works within the constraints of space travel

• Learn how crowdsourcing and transparency have combined to advance the state of drug research

• Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data

• Learn about the massive infrastructure required to create, capture, and process DNA data

That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:

• Nathan Yau

• Jonathan Follett and Matt Holm

• J.M. Hughes

• Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava

• Jeff Hammerbacher

• Jason Dykes and Jo Wood

• Jeff Jonas and Lisa Sokol

• Jud Valeski

• Alon Halevy and Jayant Madhavan

• Aaron Koblin with Valdean Klump

• Michal Migurski

• Jeff Heer

• Coco Krumme

• Peter Norvig

• Matt Wood and Ben Blackburne

• Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen

• Lukas Biewald and Brendan O'Connor

• Hadley Wickham, Deborah Swayne, and David Poole

• Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza

• Toby Segaran

Amazon.com® Reader Reviews (Ranked by Helpfulness)

Average Amazon.com® Rating:  Based on 8 Ratings

Good content, lousy print quality - 2009-09-01
Reviewer Rating:
While the content of this book is interesting and informative, I am struck with what lousy print quality it is. For a $40+ book you would expect a hardback, or at least a paperback with thick stock pages and color plates that actually look good. It was hard for me to appreciate the content when it felt like each page (or the cover) was going to rip because they were such thin and poor quality stock. The color plates are washed out and pixelated. I was expecting the same high quality we got with "Beautiful Code". O'Reilly usually does a much better job. That said, if these types of aesthetics don't bother you (although with a title like "Beautiful Data" I would question that it wouldn't) the book itself is an interesting read.

Occasionally brilliant discussions on data and what data can and cannot do - 2009-10-12
Reviewer Rating:
"Beautiful Data" is a collection of essays on data; how people have transformed it, worked within its confines, and offers a glimpse of where we might go. Many of the essays are wonderful snippets into how some people perceive data while others fall flat. Overall its a mostly enjoyable read that helps open up your mind to new potentials.

First a disclaimer; I am not a data person. However I've been involved, fairly heavily, in the data field. In the parlance of the world, I'm a back end person. However I'm always trying to think about the front end; how will things be used and what information can we gleen from the system (or systems). With that in mind, this is a book that speaks to me - its all about the front end.

Some of the best essays in the book would be:

The first essay by Nathan Yau he talks very much about user created data and personal databases (knowledge bases). What's exciting here is how he takes data already out there, data you have provided, and creates something useful and yes, beautiful, out of it.

The Second essay by Follett and Holm really gets down to how if you want the data, you need to present it in a way that brings people into the process. As someone who has a slight crush on the statistics and practices in polling (and designing poll questions) this essay really was a fascinating read.

The third essay by Hughes detailed how he handled images on the Mars mission. There wasn't anything here that wasn't done in embedded systems 15 years ago; still it was a great walk down memory lane since I used to program embedded imaging systems.

Chapter 4 really hit home PNUTShell is cloud storage and data processing in real time. This really is the stuff of the future.

Chapter 5 by Jeff Hammerbacher really didn't offer too many insights but his writing style is fluid and fun plus he offered a glimpse into how Facebook grew.

We then have the slow section of the book - Chapter 8 on distributed social data had promise but it read more like a company white page than an interesting article. Same with Chapter 12 [...].

Thankfully chapter 10 on Radiohead's "House of Cards" video was there - and here we are presented with true beauty in data - beautiful enough to create a music video out of!

I'm still on the fence with Chapter 13 - What Data Doesn't Do. It was an interesting chapter but it felt both too long and too short at the same time. I almost felt that in the author, Coco Krumme, were to write a book on this topic, I'd want to read it. However her essay was not the right vehicle.

Finally, the last chapter - "Connecting Data" was a truly inspiring piece; one that offers up paths for the future. I am sure a few start ups will form over the questions posed in by Segaran (or maybe the questions to the questions).

Overall there were enough strengths to overcome the weak chapters. My main complaints are trivial; poor binding of the book, too many PhD candidate papers and not enough from out in the trenches. I'd love to see something from Stonebreaker here; its hard to talk about beautiful data and not have him in it. Or forget [...]and talk about many eyes. Or map reduce. Still, "Beautiful Data" succeeds. It opened up my mind to different possibilities for data representation and usage.

Beautiful cover, that's for sure - 2009-11-08
Reviewer Rating:
... The contents are less impressive: O'Reilly bring together a heterogeneous group of authors and let them fend for themselves, with no editorial effort to unite their stories. Some authors hold their own, presenting interesting analyses and visualizations, or just interesting tales, others are less successful. (The spectrum of statistical expertise, for example, is bounded by Andrew Gelman and a graduate student believing that normality is a requirement of the central-limit theorem). 'Interesting' is a good thing, but for $40 I would like 'useful', and here the book comes up short. An appealing leisure read, but not much more, I am afraid.

Beautiful delight! - 2009-10-11
Reviewer Rating:
Segeran & Hammerbacher (et. al.) offer an insight on data works where inspiration may find a way. Hopefully any reader may become an author for a further version.

Midnight DBA Loves "Beautiful Data" - 2009-09-29
Reviewer Rating:
This is a collection of 20 different stories about data - gathering, planning, interpreting, storing, visualizing, etc. I'd like to go through and comment on every story in the book, but then this would be a Cliffs Notes, not a review. Let's have some highlights:

In "Seeing Your Life in Data", Nathan Yau tells about developing two projects: "the Personal Environmental Impact Report (PEIR), a tool that allows people to see how they affect then environment... and your.flowingdata (YFD), and in-development project that enables users to collect data about themselves via Twitter". That in itself is cool; users simply sent formatted tweets ("ate salad") to track mood, eating, or what have you, and then interact with the data on the site. The difference in the data collection for the two systems is also an interesting discussion, and I liked the insight into the process for choosing the best PEIR visualization.

I think my favorite chapter is titled "What Data Doesn't Do", by Coco Krumme. To break away and talk about me for a moment (and isn't everything, in the end, about me?), I subscribed for some time to a LSAT Logic in Real Life podcast, which explored the fallacies behind our reactions to common or current events. I really enjoyed learning the names and methods of misplaced logic and biases. "What Data..." struck me in a very similar vein. I've been trying hard not to quote this chapter, for fear that I'll just type it out. Still, I can't resist my absolute favorite paragraph. It begins with the header "Data Alone Doesn't Explain"

"People explain. Correlation and causality, you may have heard, make strange bedfellows. Given two variables correlated in a statistically significant way, causality can work forward, backward, in both directions, or not at all. Statisticians have made a hobby ... of chronicling the abused of correlation, like old ladies clucking at the downfall of traditional values in the modern world."

Beautiful. Again, I'd love to give a review of each chapter, but then you'd fall in love with my writing instead of Beautiful Data. Yeah, of course you would.

Finally, and most shallowly, it's a really pretty book. Check out the cover art! And that's without considering the 70 color plates, including everything from user surveys and line charts, to laser data and DNA. One, two...that's 33 words to effectively say, "Pretty pictures!"

Let's be serious for a moment, though. This book was, to me, truly extraordinary and truly entertaining. I read it in pieces over the course of a few weeks, and it was lovely to take in one story - one angle on data problems or applications - and muse on it on and off until I had a few minutes to read the next. It's a book that lends itself to piecemeal reading, jumping around, and rereading at will. And it's one I recommend not just to IT pros, but to everyone.

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