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Urban data governance

On this page you will find lessons learnt that are distilled from various workshops that the SCIS team attended. The most important points are summarised, giving a quick overview of the challenges / barriers but also solutions with regard to each topic.
Urban Data Governance
Lesson identified at: 

Challenges

Urban data governance is of vital importance for smart cities. Unfortunately, its implementation might be hindered by diverse barriers. First of all, a transparent and explainable policy on data usage needs to be defined and the foreseen algorithms have to be explainable and open. Moreover, the usage model for existing data might have to be changed and appropriate business models for a data based service operator should be introduced. Technology wise, data from older and different datasets has to be migrated and integrated and the interoperability between the datasets needs to be ensured. Furthermore, the development of an open data system including data analytics often proves to be challenging. One of the reasons is the limited availability of required competencies within the community. Besides, also the availability of resources to store, maintain and aggregate all of this data is not to be taken for granted. And of course GDPR has to be adhered.

Many city administrations – but also companies responsible for the development of the data governance structure – need to cope with limited data availability (both from public and private entities, sometimes even from another department within the same organization) and they face challenges when it comes to data ownership. Other challenges include the alignment of the urban data governance model with the local and national strategies and policies and the definition of an appropriate governance model for a data based service provider.

Last but not least, services that are in line with user expectations and the city policy have to be identified.

Recommendations

The barriers mentioned above can be vanquished via manifold manners. To provide user incentives, societal implications and benefits should be kept in mind. Contracts and procedures need to be handled with care and data sharing and provision agreements should be embedded within them. In the end, a governance model similar to utilities should be established. Additionally, a multidisciplinary team can be assigned as a DPO committee (DPO = Data Protection Officer). Besides, a public-private partnership for data governance could be used.

Plan for Implementation

Persons in charge can setup an open data portal for the whole city and could strive to integrate diverse partners. They could also figure out the best way to tag and identify personal data automatically. Furthermore, they may identify common ways of working to deal with unexpected data use cases in order to maximize the data usability while complying with GDPR at the same time. They can also define procedures and relation models to work with partners and can organize a workshop on the governance of urban data platforms. It is also indispensable to define good governance models for cities managing urban data platforms and to collaborate with other cities to share best practices and to discuss about ongoing projects.