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An emerging class of business software is built on the proposition that listening to what employees know and think can drive success

Nobody would dispute the claim that in business, as in all walks of life, knowledge boosts performance.

A more contentions issue is how any large enterprise can bring together all the knowledge that its employees possess into a meaningful whole.

The most common approach is to capture that knowledge in the form of data through IT systems and then, as far as is possible, to compile the data into large repositories. Having been robbed of the context in which it was created, that data is then subjected to translation and analysis so that the business might figure out what it all means.

There are some circumstances in which this works perfectly, but it is not the only way.

Employees, as a rule, understand the environment in which they work. If a product in development is going to fail, or if a marketing campaign is not going to deliver results, there’s a reasonable chance that someone within the organisation already knows. And while a good manager will elicit these judgements, more often than not they go unheard.

Presented below are two examples of technologies that aim to address this. They both work by mathematically aggregating the opinions of groups of employees, in one case to detect the sentiments of the workforce, in the other to facilitate transparent and collaborative decisions.

The two examples provide a glimpse of the theory and practice behind an emerging category of software, in which the collective wisdom of the workforce is regarded as an asset and which may influence mainstream business software in the coming years.

Prediction markets

Until 2007, Mat Fogarty worked as financial forecaster for corporations, starting with Unilever in the UK before emigrating to the US to join video game maker Electronic Arts. It was his job to predict how much money given projects were going to cost and earn – predictions that the chief financial officer would use to allocate budgets.

Typically, these predictions were pretty inaccurate, he recalls. “When you tried to predict how much of a given product you were going to sell in Q1, your estimate for the length of the launch phase would be unreliable, your sales predictions would be off and you would have positive or negative biases depending on the season.”

He soon discovered, though, that all the information required to make more accurate predictions was available within the organisation, just not in the finance department’s models. Instead, he says, it was “with the people”.

“When I worked for EA, I used to play football with my colleagues,” Fogarty explains. “Often, the information I was getting from the engineers, game testers and marketing guys on the football pitch was very different from the information that I was giving to the CFO from the systems.

“I thought, ‘This is ridiculous – there has to be another way.’”

This led Fogarty to explore the possibility of using ‘prediction markets’ in corporate planning. A prediction market uses the model of a financial trading market – whereby participants buy and sell ‘assets’ – but instead of shares in a company they trade bets on the likelihood of a given outcome. Famous examples include the Iowa Electronic Markets, in which participants bet real money on outcomes including the US presidential election.

Proponents argue that prediction markets are a highly accurate way of making forecasts, as rather than relying on the influence of a few so-called experts they aggregate what the ‘crowd’ knows. (Like the ‘rationality’ of financial markets, however, this is much debated).

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