Data science is transforming decision-making: How will society strike a balance?
Data science is a fancy term for statistics. It’s the extraction of knowledge from data, which can be derived from multiple digital sources and turned into a resource to make business decisions.
Sounds simple enough, right? But the complex formulas behind data science can involve artificial intelligence, information theory, machine learning, computer programming, data warehousing, and high-performance computing, along with other disciplines most people hadn’t heard of a decade ago.
Another way of thinking of data science is algorithmic math. It’s about using a precise sequence of operations to produce a finite outcome — a concept that sets many geek hearts aflutter and increasingly drives consumer and marketing decisions affecting everyday life.
It’s also a trend that raises questions about where data science ends … and where human choice and analysis begin.
Automated decision-making is meant to take the human out of the equation. It’s less emotional, more statistically efficient, and increasingly more predictive of human behavior, especially when the right data — and enough of it — is analyzed.
The potential applications are seemingly endless, from loan approvals to weighing insurance risks, and from medical diagnosis and public health to crime prevention and sentencing. Data science can be used to determine how to market products to targeted audiences, to set insurance premiums based on the health habits of the insured, and to predict how financial markets will perform.
Skip the gym today? Your insurance company might someday know that.
Data science is behind Wisconsin-based startups that are changing how many people shop for groceries, to cite one recently reported example. Fetch Rewards, GrocerKey Inc., and Pinpoint Software Inc. are bringing digital technologies to the grocery industry. Technologies developed by these Madison-based companies can be used to deliver groceries, compare prices, order food, track inventory and expiration dates, and connect customers with point-of-sale coupons.
In the process, these platforms create data banks that are potentially valuable to companies that want to know more about your behavior when you’re shopping. At the end of the day, that’s the core value behind data science companies: the data.
If it’s groceries, no one really cares if you buy organic oat bran or jelly doughnuts — unless the data is sold to your health insurance company, which could chart how well you’re following that low-sugar, low-cholesterol diet.
But what if data science is used to determine whether you qualify for a loan? By using social media connections, or even examining how people fill out online applications, big-data-driven lenders can know borrowers at a deeper level.
A recent New York Times story examined how data science might be used to fine-tune assessments of whether a loan applicant is a solid risk or a potential deadbeat. That can help people who deserve the benefit of the doubt but hurt those who come up short on a data-driven analysis lacking human review.
“A decision is made about you, and you have no idea why it was done,” Rajeev Date, an investor in data-science lenders and a former deputy director of the Consumer Financial Protection Bureau, told the Times. “That is disquieting.”
Data science is still a young science, subject to human error — or, more precisely, human judgment about what data is crunched in the process. The promise of data science, however, is that software algorithms can learn as they go by sifting through ever-increasing amounts of information.
Because that information originates with humans, is analyzed by humans, and is applied by humans, data science may never lose its human touch. It’s less a question about the data itself than how it’s used.
As the world of Big Data grows, so will opportunities to address truly global issues such as climate change — as well as routine consumer choices in the grocery aisle. As with any science, however, data science will carry an obligation to get it right and present all results, successful or not. The data itself may be complicated, but transparency in how it is presented will allow people to make their own decisions.
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