How can data science contribute to government modernization?

The modernization of governmental services is a topic, which has received special attention on the agenda of Germany’s current coalition. To advance in the area of information technology, the national e-government center (NEGZ – Nationales E-Government Kompetenzzentrum) was founded. For its annual meeting in 2015, NEGZ invited idalab’s managing director Dr. Paul von Bünau to speak about the potential of data science for government modernization.

About the national e-government center (NEGZ)

As opposed to Nordic countries, such as Denmark and Sweden, where citizens can conduct most of their administrative duties online, Germany’s administration is still struggling to adequately capture the opportunities of digitalization for bureaucracy. To fully utilize the potential of new technologies for public administration, Germany’s National IT-Summit is a key event bringing together decision-makers from various industries with IT-experts. As a result of the 2013 summit, the national e-government center was founded as a dedicated body to drive and coordinate innovation with regards to governmental and administrative modernization. More than 70 renowned experts from the administration, science and business sphere form the interdisciplinary backbone of NEGZ, working to unleash the potential of new technologies for government and bureaucracy.

idalab to outline data science impact in governmental services

As part of NEGZ’s 2015 annual meeting, idalab’s managing director Dr. Paul von Bünau was invited to speak on the potential impact of data science to transform governmental services. Using best practice examples from the United States of America, von Bünau outlined selected key areas of application.

1. Improving the efficiency of budget allocation

The budget of schools is highly dependent on the number of enrolled students. In Chicago, budget allocation is primarily based on enrollment numbers of the previous year, additionally taking into account the forecast of the respective school principals. In practice, those predictions often don’t hold true, and budgets have to be re-adjusted, causing an organizational nightmare for all involved parties. Data analysis and smart prediction models helped to effectively avoid those misallocations.

2. Smart determination of housing inspections

In light of housing shortage in New York City, apartments are often inhabited with more people than allowed. In case of fire in those buildings, these people are in a very unfortunate situation. As preventive measure, fire figthers have been checking apartments based on complaints of neighbors. But in only 13% of all complaints, a serious violation of housing rules was detected. Using techniques of data science, a statistical model was composed, which allowed inspectors to significantly drive up “inspection success”.

3. Earlier detection of serious hygiene regulation violation

Chicago has more than 15.000 restaurants, but only a handful of inspectors from the health department to check for compliance with hygiene regulations. In roughly 15% of inspections, serious violations of hygiene regulations are detected. To allow for earlier and more targeted check-ups, the Chicago Department of Innovation and Technology, the Chicago Department of Public health and employees of Allstate insurance worked together to improve the scheduling of health inspections with predictive analysis. As a result, inspectors are now able to find serious violations on average seven days faster than before, significantly lowering the amount of food poisoning incidents in the city.


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