This book is about visual organizations, that is, how organizations can leverage data visualization (infovis) to make better decisions. The author has a very lucid, concise, organized and informal style which making all topics very accessible. I have mixed feelings. For one the author uses several superlatives to describe data visualization as well as big data. These superlatives contrast with the probabilistic thinking claimed (p.11). The case studies are interesting, especially Netflix, but they suffer from availability bias in that only companies who agreed to disclose their information to the author are described in those positive terms. In other words are they succesfull because of dataviz or do they tend to be described in those terms? This point is very well made in Phil Rosenzweig book "The Halo Effect".
Most of the referenced sources (PWC, articles from magazines) are themselves of questionable validity thus spreading inaccuracies like a genetic mutation. I feel the author could have referenced more scientific sources to make his points; after all, they are not lacking. Big data seems to be a recurring feature of this book which is elevated to the detriment of anything that's not big. I think this shows a missunderstanding of the role of data, big or small, in decision making (p. 121). Instead the author seems to leverage the appealing logical argument that the more the data, the better. A paradigm which is not necessarily supported by theory, simulations, and empirical evidence.
I was a bit disappointed not to see a reference to Edward Tufte or Stephen Few when talking about minimalism in graphics (p. 150). Another questionable statement is about interactive graphs which the author support but for which there little consensus (p.151). Appealing, yes. Wow factor, yes. Effective? We don't know.
Also, there is little mention of R. For isntance on p.188 MATLAB is mentioned, but isn't R mentioned? THis is mind boggling, given R's strong graphic capabiities since the late '70s. Which brings me to the next point: dataviz with all these superlative terms seems to be described in novel ways, like something we did not have before. Truth is we have had sophisticated graphs for a fairly long time, what has changed is that data is much cheaper to own and tools have become a little easier.
I like and share the author's point on how companies could leverage from dataviz. For instance, the issue of trasnparency (p.40), data accessibility (p.52), tools, learning the tools (p.53), the notion that datavix is not an IT project (p. 107, 154).
In conclusion, I tend to be overly critical but I did enjoy the book. I think that if an organization has chosen to leverage dataviz, this is a valuable book to read. If, on the other hand, a company is trying to evaluate if dataviz is effective for them, then I would suggest that there exist much more unbiased sources of information.
無料のKindleアプリをダウンロードして、スマートフォン、タブレット、またはコンピューターで今すぐKindle本を読むことができます。Kindleデバイスは必要ありません。
ウェブ版Kindleなら、お使いのブラウザですぐにお読みいただけます。
携帯電話のカメラを使用する - 以下のコードをスキャンし、Kindleアプリをダウンロードしてください。
何か問題が発生しました。後で再度リクエストしてください。
著者について
著者をフォローして、新作のアップデートや改善されたおすすめを入手してください。
他の国からのトップレビュー
Thomas Speidel
how organizations can leverage data visualization (infovis) to make better decisions. The author has a very lucid
2015年1月10日にカナダでレビュー済みAmazonで購入
David F.
A good roadmap for implementing data visualization
2014年5月13日にアメリカ合衆国でレビュー済みAmazonで購入
Santiago Ortiz
The real life of information visualization
2014年4月1日にアメリカ合衆国でレビュー済みAmazonで購入
Christopher Schultz
Pass on this "shiny object" of a data viz book
2014年11月23日にアメリカ合衆国でレビュー済みAmazonで購入
Amy's Husband
Opens new doors for traditional data warehouse architects
2014年11月4日にアメリカ合衆国でレビュー済みAmazonで購入
