Math Makes Data Revolution in Business, Social Sciences Possible

By Brio, Moysey | AZ Daily Star, November 4, 2014 | Go to article overview

Math Makes Data Revolution in Business, Social Sciences Possible


Brio, Moysey, AZ Daily Star


"Algorithms churning through huge data bases containing factoids about what, how, and where people buy, have been essential in increasing the productivity of the U.S. commercial real estate sector. The ability to analyze inventory and target customer need via massive mathematical data sets is a game changer."

-- Mike Hammond, CEO Cushman & Wakefield| PICOR

A data revolution is taking place in the social sciences, health care, business, finance and commerce. This far-reaching transformation is driven by novel computational algorithms that track and analyze human behavior on the Internet and on mobile devices using enormous computer processing power and storage capacity allowing analysts to gain empirical insight.

Underlying this revolution, more than ever is mathematics, and at the University of Arizona School of Mathematical Sciences, we are playing an important part in developing new minds and methods to play critical roles in this emerging field.

To illustrate the different approaches, we can compare how increased data fidelity can reject erroneous assumptions. Fifteen years ago my online purchase of a Batman film led Amazon.com to incorrectly deduce my interest in the vague category of "comic book heroes." Today, such predictive methods have vastly improved.

Rather, Amazon can now detect trends in all my previous purchases, deducing that my interest in another Jack Nicholson film may make me the actor's fan rather than a superhero follower. Through application of a higher volume of data and through trends in consumer networks, Amazon can vastly increase certainty in its customer predictions and recommendations.

On a large scale, to improve patient care the government has begun penalizing hospitals that regularly readmit patients within 30 days of discharge. These penalties have cost hospitals more than $280 million in Medicare funds.

In response, hospitals are hiring mathematicians and data scientists to assign patients a predictive score based on thousands of clinical and administrative data variables collected for each patient to ensure better discharge decisions and follow-up care.

Advanced mathematical predictive models are not only facilitating marketing and consumer analysis but are also aiding decision-making in numerous other areas of public administration and private businesses. …

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