New research suggests public sector efficiency might potentially be dramatically improved through the use of “process mining,” a data analysis technique that visualizes existing workflows. A new study from NEGZ, conducted in collaboration with the City of hamburg, details how government agencies can use data generated by everyday operations – from citizen requests to procurement – to identify bottlenecks and streamline processes. The findings, released December 16, 2025, offer practical recommendations for agencies looking to enhance clarity and accountability through data-driven insights.
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16.12.2025 16:51
Process Mining Reveals Hidden Efficiencies in Public Administration, New Study Finds
From citizen requests to procurement and internal approvals, public sector organizations manage countless processes daily – often digitally, but rarely with complete transparency. Process mining offers a solution by visualizing these workflows, identifying bottlenecks, and laying the groundwork for increased efficiency and accountability. A new study from NEGZ details how public administrations can leverage the power of this data-driven analysis.
Nearly all departmental procedures within public administration generate digital footprints, known as log data, as cases are processed. This data records when an application is received, reviewed, and completed. Process mining utilizes this information to reconstruct and visualize actual administrative workflows. This allows organizations to pinpoint inefficiencies in standardized processes, identify data silos, and eliminate redundant steps, leading to targeted improvements.
The study, “Three Case Studies on Process Mining | Designing Suitable Prerequisites for Unlocking Hidden Process Knowledge in Public Administration,” conducted by researchers at the German Research Center for Artificial Intelligence and Humboldt University of Berlin, examines the conditions necessary for successful process mining implementation in public sector organizations. The goal of the research was to analyze the efficiency of administrative processes and provide actionable recommendations for stakeholders.
“The IT systems of administrations already contain a wealth of process information that has so far gone untapped. Process mining can help make this knowledge visible and usable,” the researchers stated.
The research team analyzed three real-world scenarios in collaboration with the IT and Digitalization Office of the City of Hamburg (ITD): KoPers, an HR platform for digital personnel processes; the IT procurement process for Hamburg authorities BUKEA and BWS; and the JUS-IT application in the field of youth welfare. The analysis revealed that while the data exists, it is often incomplete, unstructured, or inconsistent. However, even with limited data quality, process mining provides valuable insights, provided administrations prioritize data quality, documentation, and standards.
The authors formulated four key recommendations for effectively implementing process mining in public administrations:
1. Assess the current state: Systematically document existing workflows, data sources, and roles.
2. Document and align processes: Capture process knowledge in a clear and modeled format, such as using HERAKLIT notation, a method that integrates workflows, systems, and data.
3. Ensure the quality of process data: Process logs must be complete, structured, and comparable.
4. Establish standards and governance: Uniform requirements for data quality, interfaces, and documentation facilitate cross-agency use of process mining.
The study demonstrates that process mining enables an objective, data-driven view of real process workflows, allowing organizations to identify bottlenecks and inefficiencies – such as unnecessary wait times or redundant tasks – and allocate resources more effectively. This approach can also support the digital transformation of public administrations by increasing transparency and improving the satisfaction of citizens and employees.
The study, “Three Case Studies on Process Mining | Designing Suitable Prerequisites for Unlocking Hidden Process Knowledge in Public Administration” was authored by Oliver Gutermuth, Alessandro Benke, Peter Fettke, and Wolfgang Reisig from the Institute for Computer Science (IWi) at the German Research Center for Artificial Intelligence (DFKI) and Humboldt University of Berlin.
Scientific Contact:
Peter Fettke peter.fettke@dfki.de
Further Information:
https://negz.org/publikation/drei-fallstudien-zum-process-mining/
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