Data & Smart City Governance on the example of air quality management

Processes of rapid urbanisation pose immense challenges for for Berlin’s urban society. Here, the joint implementation of digital solutions to master challenges such as carbon neutrality, the mobility turnaround or the modernisation of administration raises pressing questions: Who is allowed to access which data for which purposes under which conditions? Which technologies are used to collect, file and process the data where and how? In which organisational structures or with which procedures are parameters for the processing of the data determined?

The aim of the project is to develop a data governance model that finds answers to these questions using a tool for monitoring air quality (air quality management) in urban areas as an example. The central question is: How can public administrations, economic actors and civil society cooperate on the topic of data use and processing in a way that is oriented towards the common good and on an equal footing?

“Data & Smart City Governance on the example of air quality management” is a pilot measure of the strategy Gemeinsam Digital: Berlin.

How do we understand data governance?

Data governance aims to balance conflicting interests in the collection and reuse of data. This concerns data sharing to generate new insights, to drive innovation or to connect people, be it personal data, business secrets or even open data. Possible conflicts can range from ensuring the provision of public services and safeguarding trade secrets to preventing surveillance and censorship up to protecting against exploitation, discrimination or political repression. Successful data governance thus aims to implement operational procedures in technical systems that protect the interests of all stakeholders and society as a whole. 

To achieve this goal, a set of decision rules, criteria and indicators, processes, roles, responsibilities, and guidelines and standards need to be defined. All actors involved need to be coordinated on the technological, organizational as well as legal level. This coordination is summarized in a data governance concept.

In our project, the data governance concept is developed inductively (bottom up) on the previously mentioned use case of air quality management. Additionally, existing governance principles in the area of smart city as well as corporate and data governance are evaluated deductively (top down). As a result, a model for data governance in data-driven public services is generated that can be transferred beyond the specific use case to other municipalities and applied to further challenges of urbanization. 

Participation process

In order to adequately address the complexity behind the issue of polluted air, we continuously involve stakeholders from politics, public administration, business and civil society throughout  the project. In an iterative participation process, existing interests in the collection and processing of data are discussed to identify and defuse potential conflicts early on. The goal of the participation processes is to navigate between existing democratic processes, the responsibilities and design options of the administration, and the freedom rights of all actors involved.

Project process

In the first phase, the project focuses on various questions about the three identified data governance problems. The answers found will then result in the data governance framework. A more detailed version of our process can be downloaded below.