Moral => you probably use Pie Charts too much!
11 months ago
I’ve been playing with wordclouds a bit lately (see here and here), so thought I’d throw together a quick visualization of my delicious tags.
Seems pretty consistent with my (professional) interests although I’m using delicious a lot less than I used to thanks to Twitter’s iPad application.
May have to create another visualization of my tweets to see how it compares.
@neilkod has been tracking tweets from PubCon, a social media conference currently taking place in Las Vegas and sharing some of his insights and analysis on the PubConTweets site.
One of the cool visualizations Neil has put together (shown in part above) shows the interconnectedness of the top 150 users of the #pubcon hashtag. It seems that @unmarketing is the most connected amongst the top #pubcon tweeters given his high inward-connectedness (number of pubcon-tweeters that that follow him, represented by size of circle) and high outward-connectedness (number of pubcon-tweeters he follows, represented by darkness of circle).
The full post and SVG visualization are on Neil’s blog.
The news feed I’m reading should also be intelligent enough to know what I’ve already read that day and what I haven’t. It should factor in stories my friends recommend and what’s being discussed on my social networks. Most important, these systems should do this without my having to instruct them or tell them anything.
Sounds like the news feed from Nick Bilton’s “I Live in the Future & Here’s How It Works” (see NYTimes review here) is powered by Newstogram.
Don’t believe everything you read [in Forbes]
Data Ninja Neil Kodner and I had a discussion yesterday about the article on LinkedIn’s data science group in this week’s Forbes magazine.
Deep Nishar and the LinkedIn data science team are doing some fascinating research and there are some great nuggets in the article but Neil and I were both surprised by the following quote:
Today 100 data researchers among LinkedIn’s 700 employees look at everything from data center behavior, search and mobile communications, as well as analysis of personal data.
To summarize our reaction: NFW!
You could say we were skeptical (or, if true, extremely envious) that data researchers made up 15% of LinkedIn’s workforce. Thankfully given the wonders of modern communication we were able to check the information with members of the data science team at LinkedIn.
And the immediate response from DJ Patil:
So seems you can’t believe everything you read.
The BBC news website publishes about 150,000 words each day. To skim every individual article would take upwards of 17 hours.
From Martin Moore’s excellent “How Metadata Can Eliminate the Need for Pay Walls”.
How much digital information will be produced in 2010? 1.2 Zettabytes
How many 16GB iPads would it take to hold 1.2 Zettabytes? 75 billion (or an iStack the size of Wembley Stadium’s field and 339 miles high)
That is pretty hard to visualize, so thank goodness for the folks at Wikibon (full visualization)
Cool World Cup / Twitter visualization app from The Guardian.
The app is dynamic and you can ‘relive’ each game through the lens of the tweets sent during the game.
Note: The image above is from New Zealand’s game against Italy (paused just after New Zealand took their first ever lead in a World Cup game).
From a VC and entrepreneurial perspective, what excites me is that we are just scratching the surface of what to do with all of this data and how to turn it into actionable, meaningful insight.
Ed Sim (Dawntreader Ventures) on the Data Revolution
…via BeyondVC
In a not-atypical scenario, a publisher may only receive $1 of a $5 cost-per-thousand media buy once all the middlemen have taken their tithes. Where does the rest go? According to an estimate from Tolman Geffs, co-president of investment bank Jordan Edmiston, it gets divided like this: The agency ($.75), ad network ($2), data provider ($0.75), ad exchange ($0.25) and the ad server ($0.25).
