Sense-Making: Finding the Stories That Computers Overlook in Workplace Data
By James M. Fraleigh
With smart machines and computers assuming an ever-greater share of routine workday tasks, some may wonder whether humans will be squeezed completely out of the workplace. For example, fewer workers now are needed to assemble cars, harvest crops, or judge the potential of an oil deposit. But machines can’t extract the deeper meaning from the data that underlie these processes, or understand their ethical, moral, and quality of life implications.
Rising use of workplace automation will make uniquely human skills more valuable, particularly the process of sense-making: the ability to craft high-level insights from data and experience that aid in making decisions. As computers and automation continue to assume rote industrial and white-collar tasks, sense-making will become a vital skill, one of 10 described in Future Work Skills 2020, a report by the Institute for the Future for Apollo Research Institute. Sense-making will help workers better parse the vast wealth of intelligence yielded by data-mining of markets, customers, and their interactions.
Machines can gather and crunch these data, but only humans can bring it to life by finding its hidden meaning. For example, securities-trading programs can sort through masses of information for lucrative investments or the right times to buy and sell, but they can’t understand the context of these transactions. Buying stocks simply because they have reached the right price isn’t useful to the manager of a socially responsible mutual fund, or the endowment of a religiously affiliated university that avoids investing in defense, tobacco, or gaming companies. Computers present numbers; sense-making workers can craft them into useful strategies.
Humans also have an edge over machines in using their intuition to manage new and unexpected situations. Computers can analyze historical data to predict the movement of markets and securities, but they cannot account for how viable these choices might be amid subsequent legal or political changes. Because such changes rely on understanding how humans think and act, we still need perceptive, flexible people who can meld their observations with data to make effective decisions. To facilitate this, data-mining algorithms such as MINE, a joint effort of Harvard University and the Broad Institute, let users discover connections within datasets by presenting them as recognizable patterns. Platforms such as these can help those in security, fraud detection, or financial analysis functions resolve or even forestall crises.
Computers will continue to play a vital role in processing the data that fuel growth and innovation. However, until machines can factor social and behavioral motives into their predictions, workers who can make sense of these data will find their skills welcome in tomorrow’s workplace.
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James M. Fraleigh writes on a wide range of topics for Apollo Research Institute.