Urban Data Platforms
The introduction of urban data platforms is often hampered by various factors. On a technological basis, data quality, standardization, migration, and integration need to be handled appropriately to assure the successful implementation of urban data platforms. Unfortunately, it is oftentimes quite difficult to collect qualitative rather than quantitative data. To develop individual solutions can also be costly and time consuming. Therefore, people in charge should look for standardized solutions. However, most barriers are no longer a question of technical possibilities: 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 the two opposites of an open/free paradigm and a closed/proprietary side. Finding a suitable business model can therefore be a tough nut to crack. The lack of capacity and competences might also lead to failure. In regulatory and juridical regards, data security and privacy, GDPR compliance, and transparent data policy need to be considered. It also needs to be ensured that data is used solely in a bona fide way. Since there usually tends to be a lack of focus on the end users, the governance should convince stakeholders of the benefits of data sharing. They must define a good size for a project to start with and should focus on data availability (especially from larger companies) and data ownership which can be particularly problematic with utility companies. Since existing platforms on the market are ridged structures, more open and agile systems directed at delivering services need to be developed.
To pull end users onto the project side, an agile system that focuses on their benefits and delivers the emotional buy-in needs to be created. It is inalienable to engage with citizens, the public sector, and businesses equally. Cities should also avoid buying meta-platforms but should build it from ground up. 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. Besides, they can expand the definition of use cases to realize wider benefits and should identify the possibilities for collecting qualitative data.