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Urban Data Platforms

Urban Data Platforms

Challenges/ Barriers facing


  • ‘We are unique’ syndrome: yes and no. Doing it yourself is a costly option. Look for standardized solutions,
  • Difficult to collect qualitative rather than quantitative data
  • Data quality, standardization, migration and integration

Social & behavioural

  • Barriers are no longer technical – need to alter the mind-set of stakeholders

Finance & business model

  • Conflict between the ‘open free’ paradigm and the ‘closed/proprietary/paying’ side of UDPs. How to find a good business model?

Competences & risks

  • Lack of capacity and competences

Regulatory & juridical

  • Data security, data privacy, GDPR compliance and transparent data policy
  • Ensure data is used solely in a bona fide way


  • Data availability – especially from lager companies
  • Data ownership is a problem – particularly with utility companies
  • Platforms on the market are ridged structures, need more open and agile systems directed at delivering services
  • Lack of focus on the end user
  • Challenge to convinvce stakeholders of the benefits of sharing data
  • Define a good size for the project to start with


Incentives/ Policy recommendations/ Suggestions/ Best Practices

User incentives

  • Create an agile system by focusing on benefits for end user: deliver the motivational buy-in

Best practices & suggestions

  • Engage with businesses, citizens and public sector equally
  • Avoid buying meta-platform, build it from ground up
  • Work with good examples and mock-ups to convince easily and quickly.
  • Think in terms of services rather than the heavy old-style platform. The available data should be easily accessible.
  • A healthy mix of open and closed data should be realized.
  • Shared ownership, platform as an enabler
  • Explore sharing capacity for data science and analytics
  • Start small and scale-up. Use easily available data early in the project.
  • First focus on data collection rather than standardization. Data collection is the main issues, standardization is always possible later in the process.
  • Pin-point a responsible party to ensure data security (e.g. local government)
  • Team-up with artists, designers or social scientists to generate new ideas for collecting qualitative data


Plan for Implementation (Next Steps)

  • Explore the financial benefits of data in your city
  • Develop a process to secure organizational buy-in
  • Learn from good practice – Lighthouse programme
  • Develop innovative tenders
  • Expand definition of use cases to realize wider benefits
  • Don’t forget the offline community
  • Identify the possibilities for collecting qualitative data
Lesson identified at: