Archive for the ‘Data Visualization’ Category
What I’m Reading Around the Web – 02-12-10
Keep Your Log Files, from Ian Lurie. Good advice, although I haven’t done post-hoc logfile analysis since clickstream analytics tools only did logfile analysis. My advice: set up a cron job and compress files by month. Once a year, archive them, just make sure that your backup solution handles the current set. Someone once told me that logfiles were needed for Sarbanes-Oxley compliance, I did not believe them.
Dave Naylor explains systemic discrepancy between bit.ly vs. Google Analytics referral in Why Google Wants You to Use it’s Url Shortener.
Google Uses Hours of Search History to Serve Ads from WebProNews – this is pretty cool. Having just taken the AdWords certification exam for the second time, I see this change making a few of the questions potentially confusing.
Via Kottke, a visualization of Twitter’s codebase changes and developers over time:
Twitter Code Swarm from Ben Sandofsky on Vimeo.
From SmartData Collective, Huffington Post: Crawling with data addicts.
From Silicon Alley Insider, Google Is Wrecking DoubleClick, Says Unhappy Client. I’m not sure if “wrecking” is appropriate, especially if Google starts to allow third-party ad serving through AdWords. Note to Google: please start doing this, it would be highly disruptive to the entire online advertising market!
From Dave Chaffey, Customising Google Analytics for your business – 6 key types of customisation. Great summary here of GA functionality.
From Chandoo, an open thread on Excel Keyboard Shortcuts. I have to admit, the only one I use with any regularity is F4 and CTRL+Arrow Keys. CTRL+Space and Shift+Space are awesome ones that I always forget about.
From Search Engine Land, 20 Metrics to Effectively Track Social Media Campaigns.
From Logic+Emotion, Six Ways to Find Social Media Talent. This advice is really applicable to finding any talent.
Phase Out Gauge Charts for Clarity
This makes absolutely no sense. These charts are an extreme waste of space when it comes to data density and they rarely contain anything that is actionable (for example, we are on pace for 105% of our site’s average visitors this month?… great). They fail and fail hard.
I have a personal vendetta against these charts because I spent an entire semester with them about ten years ago working for a grad student in the Laboratory for Automation Psychology and Decision Processes at the University of Maryland as an undergrad. I spent hours and hours sitting in usability labs testing other students’ ability to interpret gauges just like the ones available for use in our dashboards. I don’t remember the experimenter’s specific findings, but I do remember that the gauges didn’t work so well.
Over on Instant Cognition, Clint clearly agrees with me. In his article, A Gauge Chart that Works, he attempts to improve upon the gauge and succeeds. However, I still can’t think of an data application for it that wouldn’t have a more elegant solution.
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