August 8, 2008

Prediction Markets and Enterprise Architecture

A very interesting article in ComputerWorld, 7 August 2008, called "Bet on it; Employee wagers help companies predict the future," shows how difficult planning can be and highlights the necessity of bringing stakeholders to the table to get better information (enterprise architecture) and better decisions (IT governance).

Google, for example, has for three years been using employee bets (over 80,000 so far) to predict market technology. “And Google has found that its employee bets are usually right.”

Forrester Research Inc. suggests that other companies can benefit from putting in place prediction markets to more effectively tap employee opinions on topics ranging from if a store will open on time to picking specific features for a new product.”

Oliver Young, a Forrester analyst, says that “One of the biggest struggles that most companies have—not surprisingly—is predicting the future. Simple things like project updates are full of politics, full of meetings. One of the biggest values of prediction markets is you get a lot more people looking at these major questions.”

Other companies like Best Buy, Corning, HP, and Qualcomm are using predictive markets for sales projects, product evaluations, estimating project delivery, and assessing market conditions.

In predictive markets (a.k.a. crowdsourcing), “employees operate as traders to earn points--and potential prizes and other recognition--for correctly predicting future events…for example, a person who has been good at predicting how new products will be received can be invited into meetings where new product designs are discussed.”

While betting is typically associated with greed and vice and people ending up losing their shirts, in predictive markets, employee betting is used in a positive way to capture information from a broad spectrum of people and thus, enable better governance/decision making.

Predictive markets are an affirmation of the need to bring people in the planning and decision making process. Information needs to be captured from across the enterprise , for example, using predictive markets or other collection techniques.

The more information served up in a user-centric way, the better the decisions.


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