Bringing government data to life with Power BI
In today’s digital era, governments across the globe are amassing larger amounts of data than ever before, publishing an increasing quantity as open data for anyone to use. Indeed, the analyst firm Gartner predicts that more than 30 percent of digital government projects will treat any data as open data by 2018.
As the amount of open data grows, the key is turning it into actionable insights that lead to higher-quality decisions. Yet analyzing the skyrocketing quantity of information can be an overwhelming task. Microsoft Power BI is designed to help customers overcome this challenge by making it simple to transform vast amounts of data into rich visuals that can be examined from multiple perspectives. By reviewing data in visual form, governments can more easily identify critical patterns that move their communities forward.
To demonstrate how Power BI can improve government effectiveness, we recently leveraged open data to show how governments can turn publicly available data into critical insights. We leverage open data in four areas of interest—optimizing transportation, promoting job growth, addressing citizen complaints, and analyzing government budgets—to spot patterns that can help government leaders better focus their efforts.
In each of these instances, we used Power BI to visually display large amounts of data to easily examine the information by different variables—and ultimately obtain key insights that can improve how communities are governed:
Optimizing transportation
Accessing Australian car accidents data, we obtained detailed breakouts of vehicle collisions across the continent, including the time, location, and severity of the accident; the participants involved; whether alcohol was a factor; and much more.
Analyzing this data, we were able to identify the seasonality of accidents, with the lowest occurring in the winter months, and the highest in the summer and early fall. We concluded that the majority of accidents are vehicle-to-vehicle crashes, with collisions with fixed objects as a predominant second. We also observed a gradual climb in the number of accidents as the day progresses, with the exception of 8 a.m., where there is an abnormal surge in accidents.
With information such as this at hand, governments can create more measures to prevent vehicle-to-vehicle collisions, especially in the summer months. They can also put measures into place to prevent the high volume of accidents during morning rush hour and evening hours.
Promoting job growth
Using Canada’s employment and social development data, we were able to identify where the majority of Canada’s workforce resides, knowing that a large percentage works in the retail, healthcare and manufacturing sectors. By displaying this data in a map layout, we were able to see employment data by province, with the size of each bubble indicating the number of those employed in each area. These insights could be used for job education and training programs aimed at growing industries where more workers are needed.
Addressing citizen complaints
Analyzing New York City’s detailed 311 data, we obtained detailed information about the calls being made, including the time of day, location, incident type, and agency involved—leading to insights about where the city can ramp up its services.
Analyzing citizen complaints by month, for example, we were able to see that the highest number of complaints in February were related to street conditions, blocked driveways, and snow. Similarly, analyzing certain types of complaints by time of day, we learned that most garbage-related complaints occur between 8 a.m. and 11 a.m., while most noise-related complaints occur between 11 p.m. and 3 a.m. With these insights, the city could secure snow removal equipment before it’s needed. It could also work with trash removal services to deter complaints, while enforcing noise ordinances during the hours when complaints are highest.
Analyzing government budgets
Lastly, we examined San Francisco’s budget data from 2010 to 2016, gathering detailed information at the departmental level. Visualizing this information using Power BI, we could easily observe where budgets are growing. We could also see where most of the revenue is being generated.
By comparing revenue to spending by department, for example, we observed that Public Works, Transportation, and Commerce make up the vast majority of both spending and revenue for the city. Examining this data in greater detail, we were also able to see which departments are consistently over budget from year to year. With these insights, the city could either allocate larger budgets to those departments that consistently come in over budget, or focus on ways to increase revenue or cut back spending within those groups.
These are a few of the ways governments can turn data into critical insights. By putting increasing volumes of data to better use with Power BI, governments have the opportunity to improve efficiency while better serving their constituents. To learn more, please see the Microsoft Power BI blog.
