MOBIDATALAB is preparing a report on the Data sharing market technological developments.
This deliverable synthesises the actions and learnings of task 3.2 which objective is to identify which type of data and which related products and services have the highest positive impact on the creation of innovative digital services.
Starting the study, it was clear that it was difficult to give an exhaustive and complete view on the market, both in terms of the number of companies analysed, and in terms of the level of information for each. First, the list of companies active in the field is significant and changes frequently: new companies are created, others terminated, and others change their positioning. Second, the information available on each company is not homogenous – indeed, some, having strict confidentiality concerns, are reluctant to provide detailed information on their activities. That’s why only a sample of the companies was analysed to describe the market.
Through the analysis of recent reports on mobility data sharing and the description of 37 products, services, and platforms of mobility data-sharing, the study reached the following results:
- Identification of 9 data types with high potential of impact on the creation of innovative digital services (vehicle location, environment, maps, payment, vehicle usage, static infrastructure, dynamic infrastructure, ticketing, user-generated).
- Identification of 4 main components of the mobility data sharing value chain: generation, collection, analysis, and exchange.
- Positioning of 37 products, services and platforms according to the data they provide or aggregate and the components of the value chain they offer.
- Detailed description of 37 products, services and platforms: mobility domain focus, revenue source, geographical footprint, data providers and users, onboarding process, data re- use terms, data storage location, GDPR compliance.
In addition to these results, the study highlights 4 main insights on the state of the market:
- Intense competition level. Within each of the value chain component companies are competing. Data generators compete with each other. For example, OEMs and Telecommunication companies compete on mobility data. Similarly, cartographic data providers compete on the mapping offer. Analytics software providers also compete on a similar value proposition for the same type of clients. In addition, competition is fierce between companies having an integrated approach on several value chain components. These companies own or have access to relevant data for mobility and offer standard or customised data and analytics services.
- Heterogeneous availability of data. Vehicle location data, cartographic data and static infrastructure data are the most commonly available data. On the contrary, payment, ticketing, environment, and dynamic infrastructure data are less easily available.
- Lack of transport modes integration.A strong business ecosystem has developed around connected car data with some players specialising on this data source to develop digital services or analytics solutions. For the clients of these organisations the possibility to aggregate other vehicles data is limited. A similar trend can be observed in the context of public transport data. Organisations aggregating multiple types of vehicles (cars and public transport, for example) are of two types: mapping services which provide journey planning with data they own, access, or buy and software which do not provide data but allows users to aggregate their data and build customised data services.
- Multiplicity of data-sharing governance systems.Many of the companies which position as aggregators or analytics service providers use open data sources as OpenStreetMap. Collaboration occurs also between companies when some sell data to others. We only had a partial view on these practices as this information is not public and some companies declared using other data without explicitly naming their partners. Interestingly, some data exchanges between organisations are not monetized. Some collaborations are settled by barters, for example, the Waze for Cities program by Waze.
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