Last week at nGenera’s Enterprise 2.0 conference, I was in the audience for Hagai and Jeff’s presentation of their research on prediction markets (PM) and their role in the enterprise. While PM present a significant opportunity for companies seeking to harness the collective intelligence of their workforce, they have yet to be deployed on a widepread enterprise basis. A few companies that have delved into the PM space with varying degrees of success include Best Buy, Electronic Arts and Arcelor Mittal.
PM harness the wisdom of crowds approach to knowledge creation and management, and I want to share a chart that Hagai and Jeff prepared to help compare and contrast variations on this approach to “none of us is as smart as all of us.”
In my opinion, the most interesting evolution in harnessing the wisdom of crowds comes at the top right corner of this illustration, through reality-mined PM (the collection of machine-sensed environmental data pertaining to human social behavior). These PM collect information that on an individual basis would be of relatively little value and aggregate this data to display and predict useful information. There exist a number of rich applications based on this type of passive intelligence collection, such as Trapster (previously highlighted by Hagai) and Citysense, developed by a leader in this field, Sense Networks.
One of the early applications of this rich data aggregation has been the display of traffic patterns in real time by tracking the movement of every enabled mobile device through a city’s roadways.
While I am excited by the power of these PM to enhance their users’ daily experience, there are certainly tradeoffs on the personal level that must be considered before wholeheartedly buying into the exciting possibilities created by these tools. One of these considerations was hit home in a quotation from Sense Networks’ co-founder, Tony Jebara.
“Just as Google indexed pages on the Internet to optimize web discovery, Sense Networks has indexed the real places in a city and characterized them by activity, versus proximity or demographics, to better understand the context of consumers’ offline behavior.”
While the information collected by Sense is done on an anonymous basis, it is important to consider the tradeoff of a database knowing your daily, weekly and monthly personal traffic patterns so that your behavior can be “better understood” (read: so that we can market to you more effectively). Just as your Internet activity has been turned into targeted banners and spam, how long will it be before your rich traffic data leads to yet another layer of sometimes unwanted and evermore targeted marketing based on a “better understanding of your offline behavior?”
I am excited to see both the progress and conflict that will emerge from powerful reality-mining tools. What’s your take? Golden Nugget or That’s a Little Too Weird for Me, Thanks?
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Thanks for this great post
Comment by Yuce Zerey - July 1, 2008 7:50 am
[...] as Ian Da Silva pointed out about reality mining in a previous post, how far is too far? There is inherent value in opening up and sharing information, but we’re [...]
Pingback by Wikinomics » Blog Archive » Is there a limit to what we should share? - September 23, 2008 10:26 am
[...] as Ian Da Silva pointed out about reality mining in a previous post, how far is too far? There is inherent value in opening up and sharing information, but we’re [...]
Pingback by Is there a limit to what we should share? - September 23, 2008 11:56 pm