by Ryan Harrington

Last week in our home state of Delaware there was an announcement - the New Castle County government launched an Open Checkbook. With the launch, New Castle County became the first local government in Delaware to embrace open data, joining the State of Delaware and their open data portal. The move was hailed as being important by local transparency advocates including Open Data Delaware, Common Cause Delaware, Delaware Coalition for Open Government, the Civic League of NCC, and League of Women Voters of NCC

A screenshot of the NCC Open Checkbook

A screenshot of the NCC Open Checkbook

Government transparency is extremely important, but the move highlights something that is, perhaps, more important - the intrinsic value of data for government. There is immediate value in being able to see data, but what is more interesting is being able to analyze data and make predictions from it. New Castle County's open checkbook currently provides one year of data (though more will be added later). This amount of data allows for certain basic analysis and visualizations to be made, which can be quite useful. However, as more data is added, more complex analyses could occur - allowing for patterns in the data to be found.

Being able to find these patterns could allow for New Castle County (and other local municipalities like it) to improve their operations. Here are two great examples of what could be accomplished given the appropriate data:

  • Optimize revenue streams
    Local municipalities are responsible for collecting paid taxes from its constituents. Sometimes, those taxes aren't paid, at which point the municipality must figure out how to collect the taxes. This can be difficult, though. Using predictive analytics could help municipalities prioritize resources to optimize their revenues.
     
  • Managing risk
    Municipalities have to manage all kinds of risk for its constituents. Often, municipalities are reactive to those risks, helping their constituents to deal with the consequences of negative events, such as from fires or crime. Predictive analytics techniques would allow for governments to deal with risk proactively rather than reactively, providing a better experience for their constituents. Resources would be better used building interventions for issues as opposed to having to deal with the consequences of an issue later.

In each of these cases, the necessary data for the analyses should not be opened for public consumption (it relies heavily on personally identifiable information or "PII"), but governments should certainly embrace the technology to make this work possible. While embracing open data is a great first step, using data to make the lives of constituents better should be the long term goal.

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AuthorRyan Harrington