As businesses work to fuel innovation and sharpen competitive edge, a growing number are recognizing—and tapping into—government’s vast stores of data. In fact, many now depend on government data to guide business investments, develop new products and service, and foster innovation. The Roundtable on Open Data for Economic Growth, co-hosted by the Center for Open Data Enterprise and the U.S. Executive Office of the President Office of Management and Budget (OMB), revealed work already underway and pointed to compelling opportunities on the horizon.DOWNLOAD THE PDF
With all the recent attention on big data and advanced analytics, it is easy to forget that businesses have been using data to create new insights and innovation for centuries.
The term “business intelligence” was coined in 1865 by Richard Millar Devens in his Encyclopedia of Commercial and Business Anecdotes. It was first used to describe how a 17th century banker achieved competitive advantage by collecting and analyzing internal and public information relevant to his business activities.
While that may be ancient history, business intelligence remains highly relevant—and government has a key role to play in collecting and providing data.
Many businesses follow a problem-centric approach to using government data. The focus: combining proprietary data with publicly available data to address existing organizational challenges.
Other businesses use a discovery-centric approach, which allows data analytics teams to identify new correlations and trends by fostering intellectual curiosity. One such organization is the Kellogg Company, which applies data-driven approaches to help increase revenue and reduce costs.
At the Roundtable on Open Data for Economic Growth, a representative from the Kellogg Company shared four key steps of the process it uses, and any company can use, when applying government data for business decisions:
FORMULATE THE QUESTION AND HYPOTHESIS
For example, data scientists at the Kellogg Company used government data to explore the impacts of cold weather conditions on consumer spending behavior. The question could be: “Is the harsh winter affecting our consumer sales?” The corresponding hypothesis could be: “Lasting cold temperatures are reducing the number of shopping trips and reducing cereal consumption.”
IDENTIFY RELEVANT DATASETS
Businesses can look to both government data and proprietary data. For the hypothesis on winter sales, for example, the Kellogg Company combined its own retailer point-of-sale data (by zip code, by day, and by three-year history), and government weather data (by zip code).
CONDUCT ANALYTICS TO DEVELOP INSIGHT
In this case, the Kellogg Company used data analysis to identify sales patterns and develop models to show how they correlated with weather patterns. The Kellogg Company discovered that sales drop when temperatures are below 20 degrees Fahrenheit for three consecutive days. However, they may not drop on Friday—when the effect of payday outweighs the effect of weather.
APPLY FINDINGS TO BUSINESS STRATEGY
Use this process to enhance business strategy, optimize customer experience, support product innovation and improve operational efficiency and effectiveness. In this example, the Kellogg Company changed its digital coupon distribution schedule to drive consumers to make purchases on Fridays since the data analysis demonstrated that it’s a relatively “weatherproof” day.
Chief Technology Officer, Accenture Federal Services
Director, Digital Government, Accenture Federal Services