The rise of social media and the improvements in data aggregation led to the collection of large quantities of user-generated data, and it was only a matter of time until businesses started using this data to improve operational efficiency. For the last several years, big data has been a major point of interest for companies around the globe, which see it as a key to confident decision making. In his article, The Dark Side of Big Data, Tom Goodwin argues that big data is not as reliable as some think it to be.
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The author compares big data to a weather forecast. Despite the increase in computational power and the existence of satellites constantly monitoring weather patterns from the orbit, the complexity of atmospheric interactions means there is only so much that can be predicted using this data. Goodwin argues that marketers and business executives seem to overestimate the ability of the data to predict human behavior, which is often weird and complex to the point it seems irrational.
Every internet user has seen businesses’ confused attempts to predict our behavior by what we buy or search for. In a simplified model of a world, a user looking for item X is likely interested in similar items or intends to buy it. In the real world, a simple search query can be interpreted in hundreds of different ways, and each one is just as likely as the other. The author explains that the size of data does not make it any more reliable, the value of this data is in its meaning, and it is still up to interpretation: “Big data can’t explain how I can be a Guardian-reading, Whole Foods loving, Golf playing guy that owns an old lowered plastic dipped BMW with spinning chrome wheels.” (Goodwin, 2015, par. 2).
Data-driven decision making is often seen as a more sensible approach, since any decision supported by data is bound to be risk-free and therefore, successful. Goodwin argues that history proves the opposite – many companies achieved success by taking risks and creating trends, rather than following them. One such company is Apple, which would have never created iPhone if risk-averse, data-driven thinking was in place. Apple would have presented an iterative improvement, another phone with a stylus and qwerty keyboard.
Improvements in data aggregation which allow for larger quantities of data to be stored and processed do not make larger data sets more valuable or accurate. The value of big data comes not from the size of the data, but from its analysis, from the way it is interpreted and incorporated into decision making, along with other ways to connect a brand with customers. Big data analysis has to be context-specific; otherwise, it is just statistics that have no real value. The recent improvements in data aggregation and analysis do not mean that humans’ decision-making can be mapped out and predicted. Moreover, Goodwin argues that big data is not yet able to replace human experience or intuition.