Blogging What You Didn't Know You Knew
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As a longtime blogger at IBM -- one who has a day job helping drive new business for our company via the Web -- one of my biggest challenges is keeping up with increasing volumes of information and eking out the bits most relevant to my areas of interest.
By way of relevance, IBM's Information Management group has a statistic that consistently sticks with me: Managers spend two hours a day searching for information.
And yet 50 percent of what they find is useless, and 42 percent of them accidentally use the wrong data on a weekly basis.
Think about that.
That's like saying managers spend two hours a day trying to find their way back to their office.
"Uh, Bob, I won't be in for a couple of hours because, uh, I'm lost and completely clueless, uh, and I don't know how to, uh, get to the office from here. Uh, see you then...Hopefully."
If managers spend two hours a day searching for information, you can only just imagine what bloggers spend.
Though many bloggers (including myself) use RSS readers and even intelligent filters to monitor mentions of specific words (think Google News and Blog Alerts), there's been a dearth of "smart" monitoring and recommendation capabilities available for the blogosphere.
But the possibilities are immense.
By way of example, if I'm interested in the topic of Internet marketing, it would be great to have a technology that could make automagical recommendations of relevant Internet marketing blogs based on blog viewing patterns, posting frequency, comments, and other relevant blogging indicators.
IBM's Tokyo Research Laboratory has been working on such an approach, and in March will be rolling it out in partnership with Japan Internet media firm, CyberAgent.
In its Ameba blog service, CyberAgent specifically will introduce two new functions:
Ameba has some 4.5 million users in the Japanese blogosphere, one of the world's largest by country.
What's difference about the approach taken by the Tokyo Research team is that though there has been substantial standalone analysis of text data or user activity, these folks focused on composite analysis.
This approach provides for a new data analysis platform that allows text data and user existence in a form of node, allowing for the accumulation and analyses of user activities as a linked network.
This allows for more relevant blog recommendations based on expressed interest, with both what one writes on one's blog as well as what one reads in other blogs feeding the recommendation engine.
Think of it as a sort of blogging collaboration filtering engine, except that one's input to drive the filter includes what one publishes in their own blog!
This initiative will roll out in March, and I, for one, am going to be very interested to see what comes of it.
Now if you'll excuse me, I've got to go find my way to the office and I only have a couple of hours!