Here's some good news for Economics majors. According to today's Wall Street Journal:
One of the reasons they are hot again is thanks to big data. Firms are faced with a flood of data, which does them no good unless they have people who know how to analyze it. I've written here previously about the big data labor shortfall and the role of the data scientist.
Companies need to have people with expertise in economics, math and statistics to analyze their data, not just BI professionals to gather it for them.
When you delve into the world of data scientists and developing predictive analytical models, the primary expertise you should look for on a resume are in the fields of economics, business, math and statistics. Although these folks need to have some programming chops, IT professionals should perform the programming heavy lifting via data integration.
I am simultaneously amused and frustrated that so many industry pundits and folks in higher education proclaim that the data scientist shortfall can be filled by training computer science majors in big data programming tools. (No offense to those folks, as they have a big role to fill in getting the data, but their role does not generally include performing analytics and formulating the predictive models.)
As the article states:
"A lot of companies have programmers who are able to process big data," said Tom Beers, executive director of the National Association of Business Economics in Washington, a professional organization with about 2,400 members. "But to find a causality between two things and draw a conclusion really takes somebody with an economics background."
As we in the business intelligence field know all too well, bad data equals poorly-informed decision-making. So, the economics experts also have to understand something about data quality and how to deal with the shortcomings of the data they use to perform analytics.
With this in mind, the WSJ article points out that:
"The glut of available data is forcing economists to serve as gatekeepers to ensure that disparate units within companies are using the same data sets and information inputs in their forecasting."
The Wells Fargo example it provides illustrates this problem that can arise if data is not consistent:
"Previously, one unit might base unemployment figures on payroll data, while another would use household surveys. Doing so undermined the accuracy of tests to measure risks for losses and contributed to mistakes in business planning."
Economics majors take note: you might want to add more math courses than your major requires, and also some computer science. Then there will be no question that you're hot!