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SOME MORE REMARKS about applications that combine prediction markets and organizational data (org charts, social networks, seating locations). The obstacle to these applications is not a lack of data. Jed mentions privacy concerns -- and if he thinks this is a big obstacle then I'd be interested in discussing his thoughts.
A bigger problem is that that current PM vendors and consultants cannot support these applications. At heart, these vendors are software engineers and salespeople at heart, not statisticians or data miners. They want to write one system that can support lots of clients. At conferences, one hears PM vendors complain about having to do "customization" work for clients.
This approach would not work for the applications I describe for two reasons: - The inputs for different clients won't be the same. Each client's organizational data will likely take a different structure. This makes it difficult for PM vendors to architect a single system that can served many clients (yet another challenge with integrating markets with other corporate IT services).
- The outputs for different clients won't be the same. The business relevance and statistical power of each analysis will differ with each client's data.
PM vendors may also need to familiarize themselves with the statistical learning methods necessary to fully utilize these rich datasets. So what's the solution? First, move to a software-and-consulting model. By 'consulting,' I don't mean 'consulting on how to implement the market.' I'm talking about helping the client solve its problem using a variety of data, including PM data.
Second, the vendors also need to pitch prediction markets as more than a forecasting tool. People in the business world commonly identify as data junkies -- probably moreso than they identify with the 'wisdom of crowds' ethos. It is unclear how much companies really care about accurate forecasting anyway.
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Disclaimer: Opinions expressed on this site are the author's and not necessarily his employer's.
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