If you want to uncover business news, never underestimate the power of looking at datasets that don’t look like traditional business datasets.
Speaking in the “Investigating business with data” session at NICAR15, the Wall Street Journal’s Andrea Fuller and CNBC’s John Schoen provided a wealth of examples of stories written using not only traditional business datasets like the SEC's Edgar and FINRA but also non-traditional business data sets such as Medicare.gov.
Datasets like Medicare.gov greatly aided the Wall Street Journal’s investigation into Medicare fraud. While Medicare might not be the first place one would look to uncover a business story, doctor’s offices are businesses. So are hospitals.
In another example, the WSJ used oil and gas data to map out oil routes along various states. Companies are required to report how much oil it moves through each state. Using this data and ArcGIS, the WSJ mapped out oil routes across counties in various US states.
Another reason to look at non-traditional business data sets is to remember that non-profits are not always charities. The NFL and the NCAA have been the center of many big business stories within the last year and both are non-profits.
There will definitely be times when the data will be proprietary, expensive or unstructured. Fuller writes many data scripts using primarily Python to solve these problems.
Schoen, who has been covering economics and financial news for more than 30 years strongly emphasized that one should never go looking at a dataset with a conclusion in mind. Data analyses must be conducted and then the story should be looked into further as opposed to writing a story and then cherry-picking numbers to support the story.
With a reporter’s mindset, access to wide range of data sets, and some web scraping skills, you are on the track to uncovering interesting business news.
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