We currently live in an era where information exchange is becoming everything. The collection of our data is a continuous process that gives businesses and marketers superior information on our behaviour, social class, purchasing preferences and even personal things like our political affiliations and sexual orientation. In fact, most human activity elicits some sort of information or another. Purchase history’s, online profiles, website views and “likes” on Facebook all reveal information about ourselves. More often than not, we are completely unaware of what information we make available to others.

Data scientists and business analytics specialists working for retail firms have become experts in analysing customers’ consumption patterns. Some of their forecasts in human behaviour are seemingly invasive. (Hill 2012) reported that retail giant, Target, had been able use consumption data to determine whether a woman is with child before she needs to start buying the essential items for her baby. While it isn’t illegal for businesses to use information available in the public domain, it certainly invades ones privacy. In one such instance, Target was guilty of exposing a girl’s parents of her pregnancy through mail and coupon advertising sent to the family’s home (Lubin 2012).

Kosinski, Stillwell et al. (2013) conducted an experiment to find out which “liking” behaviour was most indicative of intelligence in subjects. The item that was most indicative of intelligence was that of curly fries. Despite the food having no logical connection with intelligence, the findings were backed up through the process of homophily (individuals bond with other individuals similar to themselves). What this meant was that the subconscious effort of liking an arbitrary page on Facebook such as curly fries, meant that that information miners would be able to identify you as intelligent. This has glaring implications, especially in the practice of recruitment.

Golbeck (2013) warns that recruitment firms are already using information such as this to determine a person’s likelihood of being an alcoholic or using drugs. Mention has even been made of the fact that people who use Facebook are actually the product and not the customer because they provide the data and information that is then sold off to businesses.

So how can we combat this? Is there a way forward to protect the type of information we broadcast about ourselves? It has been proposed that notification alerts should be displayed before submitting information and “liking” content online. It would explain the risk of revealing certain type’s information online. Another option is the use of data encryption which would render data useless for companies looking to exploit it. Despite the public’s’ reservations about this method of data gathering, this new and profitable method is unlikely to change anytime soon.

References

GOLBECK, J., 2013. The curly fry conundrum: why social media “likes” say more than you might think. 3 edn. Mid Atlantic: TED.

HILL, K., 2012. How Target figured out a teen girl was pregnant before her father did. 3 edn. New York: Forbes.

KOSINSKI, M., STILLWELL, D. and GRAEPEL, T., 2013. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), pp. 5802-5805.

LUBIN, G., 2012. The incredible story of how Target exposed a teen girls pregnancy. 13 edn. New York: Business Insider.

“Industry expert, market expert, sales expert, technology expert”. These phrases make up just a few of the growing number of “experts” operating in today’s economy. Experts are people who possess a unique or certain set of skills, knowledge and know-how that make them an informational asset to people or organisations seeking understanding and insight. Experts appear regularly on political talk shows, interactive panels discussing climate change and even lend their heralded expertise to predicting sporting events and recommending investment options. Successfully predicted outcomes usually boil down to the superior “expertise” whether serendipitous in nature or not. However, the reign of the expert may be coming to an end with the emergence of prediction markets.

A prediction market is essentially an information market that facilitates the trade of event outcomes. Market prices are usually indicative of how probable the crowd believes a certain event to be (Tziralis, Tatsiopoulos 2007). They aggregate data and information to generate insights. These prediction markets give businesses the opportunity to predict the success of new product launches, prices of certain products and to generate new insight into how information flows around the workplace. Outside of the business context, prediction markets are being used to predict presidential elections and other significant events (Broughton 2013).

It is widely acknowledged that surveys may be superseded by the use of prediction markets as a decision making and information elicitation tool. In fact, many firms have put prediction markets to use internally via incentivised games. Google even created their own currency, called Goobles, which employees could use to make bets on the success of prospective google products. Incentivised by cash and gift rewards, employees set about providing informed information that would allow Google to gather predictive data.

Following the theme of a diminished role for experts and according to Thompson (2012), predictive markets are creating a changing dynamic within organisations. Chief executives are traditionally seen as the main source of information, expertise and direction. Prediction markets challenge this idea because all employees have access to the superior, collective information of the organisation as a whole. The idea unsettles the traditional business hierarchy. As such it slightly reduces the value of the expert. Although it is interesting to note that the role of the expert won’t be made completely redundant. Much of the information companies rely on is informed data and this means that people trading within the information market need a certain level of skill, knowledge and information to be able to give the business any valuable predictive information.

So while field matter experts may not necessarily be made redundant, it is likely that they will need to continue improving what they offer. Being up to date with the latest industry practices, methods and understanding how to coexist in an era where the “crowd” and useful information is more easily collected is more important than ever before.

References

BROUGHTON, P.D., 2013. Prediction markets: Value among the crowd. Financial Times, .

THOMPSON, D.N., 2012. Oracles: How Prediction Markets Turn Employees into Visionaries. Harvard Business Press.

TZIRALIS, G. and TATSIOPOULOS, I., 2007. Prediction markets: an information aggregation perspective to the forecasting problem. World Review of Entrepreneurship, Management and Sustainable Development, 3(3-4), pp. 251-259.