I’m continually amazed at the developmental progress in — and growing use of — visualization tools to summarize data.  In a different post I wrote about the progress companies like Intronetworks has made in visualizing relationships between member profiles and member interests in online community networks.  In a recent article, NY Times wrote about a data visualization tool called “Many Eyes.”

Here, an experimental site from IBM’s Alphaworks, Many Eyes (www.many-eyes.com), gives users the ability to upload data and collaborate with other users to visualize data sets in various interactive displays.  These range from traditional line and bar charts, bubble graphs and bar stacks for numerical summaries to tag clouds, word clouds and word trees for displaying relationships and/or frequency of text-based data.

For example, below is a visual comparison of data sets using the text of speeches given recently by Presidents Bill Clinton and Geoge Bush at their respective party conventions about their respective party candidates.

Bill Clinton’s Speech on Barack Obama (click it):

George Bush’s Speech on John McCain (click it):

After importing the text of each speech, the Many Eyes engine can present a visual of the data set via several models you choose.  In the cases above I chose a Wordie cloud to show the frequency of words used in each speech.  The larger the text, the higher the frequency.

The visualizations above suggest the greater frequency in which Mr. Bush mentions Mr. McCain by his first name than did Mr. Clinton of Mr. Obama.  Kinda gives you the impression that John is George’s BFF, huh?  Whereas Bill seemed a little more formal in his references of Barack as suggested by the relative frequency in the use of “Barack” and “Obama.” 

But interesting, too, is the frequency of other nouns used in each of the speeches.  ‘Seems to be a more “wordly” theme to Mr. Clinton’s speech about Obama than those used in Bush’s for McCain.

Another beauty of these types of visualization tools is the flexibility they afford in being able to get another view of the data set.  For example, the snapshots below show a tree diagram from the same data sets, but using each candidate’s last name as the root. 

Clinton speech using “Obama” as root (click it):

Bush’s speech using “McCain” as root (click it):

What jumps out at me is the “meatier context” in which Clinton’s words use “Obama” than the contexts in which Bush uses “McCain.”

Using the same visualization tool and taking a page from real estate, I uploaded text from NAR’s Marketing and Member Benefits page.  Just for grins and giggles, I wanted to see what words jumped out.  After removing the obvious references to “Realtor”, “Realtors,” “Benefits,” and variations of “members” and “Membership” (since I wanted to focus on the benefits themselves that NAR envisioned for its members), the following jumped out.

In addition to the minor prominence of the word “dues”, it’s refreshing to see words like “information,” “program,” “technology,” “awareness,” and “ethics” jump out as among those with most frequency in the text of NAR’s vision for benefits to members.  Don’t get me wrong, I’m not sucking up to NAR by any means.  The reality is that vision and execution are two different things.  But, I also believe a prerequisite to execution is a reasonably good vision.

But, back to focusing on the tools for a moment.  Perhaps most remarkable to me about these visualization tools isn’t so much that they are being developed, but that they’re evolving in ways that solicit input from multiple minds.  IBM’s platform for Many Eyes allows users to publish data and visualizations in a discussion forum.  This helps get the perspective and input of others on the same data set. There’s no way that any one person can “know it all.”  And, by collaborating in a way that knowledge capital from multiple members of a group is solicited, then it opens up more opportunities for new insights to be found by examining the same data set in different ways while also protecting against biases of any one individual.