Background and scope
The study Regional and local data-driven innovation through collective intelligence and sandboxing was launched by the Digital Economy Unit of the Directorate Growth and Innovation of the JRC Ispra in the framework of the ELISE Action of the ISA2 Programme, with the aim to explore the interplay between digital technology and other factors transforming government operations in terms of service delivery, governance processes and policy-making mechanisms.
The objective is to assess the impact of the adoption of regional and local data ecosystems and digital twins, by exploring several governance and business models by taking advantage of a sandboxing approach. The study focuses on the following activities:
- Mapping the data ecosystems of various European cities, understanding their strengths and weaknesses
- Identifying technologies, approaches, models of different uses of data through sandboxes, namely agile and inclusive testing environments
- Summarising and disseminating lessons learned to ensure the sustainability of the identified beneficial uses of data
- Validating results, developing policy recommendations and suggesting research topics to spread and scale the creation of value from data
Outcomes of the research
- Description and analysis of regional and/or city data ecosystems. Seven local data ecosystems have been analysed – Barcelona (ES), Bordeaux Metropole (FR), Helsinki (FI), Milan (IT), Santander (ES), Poznan (PL) and Rome (IT) – assessing their data governance models, including for instance, the high-level architectures, the data flows, the services provided, the stakeholders involved, the level of maturity, etc.
- Overview of the established sandboxes together with a description of their use. Based on the main challenges identified by city authorities for their data ecosystems, four cities, from the the seven involved in the data ecosystem analysis, are going to co-design and implement sandboxing experiments to address the areas for improvements and test innovative solutions.
- Summary of the lessons learned from the application of sandboxing. Key lessons learned are going to be extracted from sandboxing activities, allowing understanding of how data-driven tools, approached and models can support the creation and strengthening of data ecosystems at local and regional level, and also shedding light on the opportunities offered by such a sandboxing approach.
The outcomes of the study are going to be collected in a Final Report, which will also outline policy recommendations, suggestions for emerging research topics and provide a common analytical framework for the use of sandboxing.
Data is going to be made available based on the Technical Specifications of the Living-in.EU (LI.EU) joint initiative, which are called “MIMs Plus” and are based on the OASC Minimal Interoperability Mechanisms (MIMs).
SAVE THE DATE – 2021 European Week of Regions and Cities (EWRC)
The preliminary results of the study will be presented and discussed in a Participatory Lab on 13 October 2021 (14:00 – 16:30 CET) in the framework of the EWRC. Registrations are open here.
Participants will be invited to attend a virtual journey around seven European cities involved in the study with the aim to explore the main obstacles and the key enablers for the development of data ecosystems in different city contexts, and also to discuss the potential of a “sandboxing” methodology to help local administrations test innovative data-driven solutions.
Participants will be presented the results of an analysis of the data ecosystems of seven different European cities: Barcelona (ES), Bordeaux Metropole (FR), Helsinki (FI), Milan (IT), Santander (ES), Poznan (PL) and Rome (IT). Participants will have the opportunity to learn what obstacles these cities are facing, and what enablers they have set in place, also discussing how these findings could be applied in other local contexts.
The journey is going to focus on how various cities taking part in the study have made use of a sandboxing methodology to strengthen their data ecosystems. Here, participants will discuss the potential of applying such a methodology in their own local contexts for the purpose of experimenting with innovative data-driven tools, approaches and models.