Over the past decade, an increasing number of organizations have started to use decision support systems to predict behavior and results. Predictive analytics enables managers to make decisions about how to allocate resources and take other steps that are most likely to lead to positive results. This approach uses historical data to identify predictive variables. For example, predictive analytics is used in higher education to flag students who are at risk of flunking out based on the pattern of courses and grades that match those of students that have previously had trouble. Those students can then be targeted for support from advisers or tutors. Another example comes from Netflix, the movie rental company. One way Netflix uses analytics is to make recommendations to customers based on their viewing history; this provides additional value to customers and helps with customer retention. Another way the company uses analytics is to predict the number of customers that will want to view each film so it can determine what to pay movie studios for the distribution rights to DVDs.
Being good at analytics has become a sought-after competitive advantage; the better an organization becomes at making sense of enormous amounts of data, the better decisions it can make. Many companies have made investments in their analytics capability as a competency that can reduce costs, enhance operations, and increase revenue. Chain restaurants use it to determine the best places to locate new stores. Frequent shopper cards offered by many retailers help those companies identify and track the purchases of their most loyal and profitable customers and to target promotions to them. Analytics have even become common in professional sports; for example, baseball teams use a large set of metrics to determine the value of each player on their roster (and thus what they should pay them) and past performance data to determine how to pitch to a batter and to position players in the field to best anticipate where he will hit the ball. One consequence of the wide-spread use of decision analytics has been the creation of well-paying jobs for those with specialized skills in statistics and mathematical modeling; the Harvard Business Review calls these types of positions the “sexiest job of the 21st Century”.
References
Davenport, T.H. and J.G. Harris. Competing on Analytics. Harvard Business Press, 2013.
Davenport, T.H. and D.J. Patil. “Data Scientist: The Sexiest Job of the 21st Century,” Harvard Business Review (October 2012), https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century accessed 26 August, 2016.
