This great site provides a companion infographic for every episode of NPR’s This American Life series.
Cleanly presented and arranged like a narrative, these visualizations outline some of the key facts of an episode and demonstrate a nice example of how infographics can be used to tell a story.
]]>Pivot garnered quite a bit of attention at the 2010 MIX conference, and attendees were apparently wowed by the elegant and engaging interaction model.
I can see numerous applications for this technology in my field of healthcare technology. Pivot could be used to track symptoms across thousands of patients, identifying trends, common side-effects, and adverse events. The application could also have use for processing and analyzing electronic medical records.
Check out the demo video here:
The Microsoft Pivot platform is a free download from Microsoft Labs. Worth a look!
You may also want to check out Mike Demopoulos’ write up on healthcare applications of technologies promoted at the 2010 MIX Conference at Cambridge BioMarketing’s Biorhythms blog. Mike also hosts two great blogs on Silverlight and WPF development.
]]>Great dataviz from GE and Ben Fry: http://www.ge.com/visualization/health_costs/index.html
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Dead on
]]>Pear Analytics released a new study on Twitter usage trends. They tracked 2,000 tweets over a 2-week period and then categorized them into six usage buckets: news tweets, spam, self-promotion (my favorite), pointless babble, conversational tweets, and pass-along value. Tweets containing “RT” were categorized as “pass along”, and those using “@” were considered conversational.
The study revealed that over two thirds of tracked tweets were either “pointless babble” or “conversational”. I suppose this isn’t too surprising. However, the study found that news-related tweets constituted the smallest category; less than 4% of tweets are news-related.
The study is provided in whitepaper format on Pear’s website and is worth a read. There are also some interesting findings on user demographics and the overall reach of Twitter.
I’ve been a Twitter user for a couple of years now. My usage is casual at best. Maybe I’ll check once or twice a day and perhaps post a handful of tweets a week. I’ve never really used it for promotion, nor have I made any effort to recruit followers. I find it useful for tracking news and updates on industries I’m interested in—hash tags are the most useful feature to me. But getting to the good stuff requires a lot of sifting. Several months ago I removed all the so-called “social media experts” from my follow list and the results were remarkable. It’s amazing how such a small group of power-users can create so much useless static.
Check out the visualization below (from Gizmodo). Despite the hype and promise, it’s easy to make the case that Twitter has become a soapbox for a select group of loud mouths. This statistic is likely one that keeps the Twitter founders up at night—it points to unsustainable business model.
The Pear Analytics study is one of several that have clearly illustrated that Twitter provides value in terms of reach, trend-tracking, and marketing. However, the majority of content is either pointless or conversational and likely not of interest to the largest Twitter population: relatively passive users like me. The study highlighted a new service called Philtro that uses preferences and complex algorithms to serve up only the Tweets that you real care about—supposedly leaving the junk behind.
]]>Some interesting highlights:
- Consumers who visit product sites are nearly three times more likely than the average US adult to request prescriptions by name from their doctors.
- When consumers use search engines for pharmaceutical or health information online, they are most likely to search by the name of a specific condition/disease or the name of a specific product.
- The most common situation for which they use a search engine for this purpose is when they have symptoms for a condition.
The graphic illustrates percent change in S&P Market Index, by year, going back to 1825. I like the simplicity and clear quantification across both percent change and time.
You’ll see 2008 you look to the far left (-50% change – worse since 1931). Yes, this has been a bad year!
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If that wasn’t enough, I also noticed that there was a 3X increase in the number of purchases in July and August. I’m usually careful about my spending and I didn’t notice any real additional spending; the iPhone’s purchasing experience is dastardly clean and effortless.
Is this common among new iPhone owners? Apple has been vague about the amount of profit made from iTunes—mostly due to scrutiny from the entertainment industry. If you look at their Q308 financials, they indicate a 35% over the year in “Other Music Related Products and Services.” I’ll be interested to see if the numbers increase in Q408 with more iPhone 3G users.
I may be an anomoly, but it’s clear that the iPhone makes it much easier (and transparent) to purchase from iTunes, and I’m curious to see if this becomes a real boon to iTunes.
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In the past, the data had to be pulled into Excel and then graphed. This took up a lot of unneeded effort—especially when drafting a quick campaign report. It would great to see Google support comparison charting and more visualization exports in Analytics.
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