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Urban Data Platforms

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 Platforms
Lesson identified at: 


The introduction of urban data platforms is often hampered by various factors. From a technological perspective, project teams regularly suffer from the ‘We are unique syndrome’. (Have a look at the SCIS policy paper to read related content.) Every project indeed is unique in some way, but doing everything yourself is a costly option. Looking for standardized solutions, or building on the experience of other projects could help to reduce costs.

When it comes to data collection, gathering qualitative data turns out to be a bigger challenge than collecting quantitative data. Furthermore, data quality often is not optimal, there is a lack of standardization, it is challenging to migrate data from platform to another and not straightforward to integrate different platforms.

Although most of the technical barriers mentioned can be solved, other challenges are harder to deal with. From a social and behavioural point of view, the stakeholder mind-set needs to be altered to guarantee a successful implementation process.  Moreover, urban data platforms are often caught between an open/free paradigm on the one hand and a closed/proprietary side of urban data platforms on the other hand. Finding a suitable business model can therefore be a tough nut to crack. The lack of capacity and competences within the community might also lead to failure.

When it comes to regulation, ensuring data security and privacy, complying with GDPR providing a transparent data policy need to be taken into account. It also needs to be ensured that data is used solely in a bona fide way.

Projects tend to have a lack of focus on the end users, and should definitely convince stakeholders of the benefits of data sharing. (Have a look at the Citizen Engagement page to learn more on how to involve people.) Project owners must define a good project size to start with and should pay attention to data availability (especially when dealing with larger companies) and data ownership (specifically when utility companies are involved). Since existing platforms on the market are ridged structures, more open and agile systems directed at delivering services need to be developed.    


To convince end users of the project, an agile system which focuses on their benefits and delivers the emotional buy-in needs to be created. It is key to engage with citizens, the public sector, and businesses equally. Starting small and scaling-up by using available data early in the project are further suggestions. Cities can work with good examples and mock-ups to convince easily and quickly and should think in terms of services rather than the heavy old-style platform. The available data should also be easily accessible. Moreover, a healthy mix of open and closed data should be realized. In this context, cities should focus on data collection rather than standardization first since this is always possible later in the process. Additionally, sharing capacity for data science and analytics can be explored while shared ownership might be used on the platform as an enabler. A responsible party to ensure data security (e.g. the local government) should be pin-pointed. Finally, persons in charge should team-up with artists, designers or social scientists to generate new ideas for collecting qualitative data.

Plan for Implementation

To take the next steps successfully, cities should explore the financial benefits of data in their cities and should develop a process to secure organizational buy-ins. They may learn from good practices like the Lighthouse Programme and can develop innovative tenders (read more on our dedicated page on Innovative Procurement of Smart City Solutions). Besides, they can expand the definition of use cases to realize wider benefits and should identify the possibilities for collecting qualitative data.