Maas-Peer – bloggpost 2

The Norwegian Maas-Peer learning network is part of the international Peer Learning Networks for data collaborations hosted by the Open … Continued

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The Norwegian Maas-Peer learning network is part of the international Peer Learning Networks for data collaborations hosted by the Open Data Institute (ODI) and Microsoft. The Maas-Peer collaboration consists of ITS Norway, Ruter, Nivel, Entur, FourC, SINTEF, Bouvet and Strandli Consulting.

The partners of Maas-Peer have taken part in an exciting and stimulating five-month journey to learn more about trust and data sharing. We have gained new knowledge from each other, from the five other international peer learning networks (PLNs), and knowledgeable persons and resources in ODI and Microsoft.  ODI has supported the network through stimulating workshops, open learning events and 1:1-meetings with each learning network.

The project has established «links» to many new and valuable external resources. These links serve as input for new projects and knowledge building.

The main objective of the Maas-Peer collaboration was to identify and understand the real barriers for a viable MaaS related to data sharing and trust. During the PLN process, two significant barriers were identified and clarified:

  • Sharing commercially sensitive data like vehicle position and capacity shared by the private or public transport/mobility provider leaves them exposed to competitors but doesn’t guarantee an increased number of trips or revenues. This barrier might also be linked to the potential unfair distribution of payments across the value chain, including public and commercial actors.
  • The traveller’s exchange of personal data might be a potential barrier if the traveller does not trust that GDPR or other regulations governing data sharing.

In addition to the significant barriers, the complexity of MaaS in itself is a barrier. The actors must handle many systems, data connections, cash flows and liability issues.

To mitigate the barriers, contractual protections that satisfy both the needs of data providers and data users seem to be satisfactory solutions. For the exchange of personal data, new methods for anonymization utilizing technologies like confidential machine learning (ML) etc., might be helpful in addition to the GDPR.

To get deep insight into the issues of trust related to shareable data in MaaS, the Maas-Peer team developed several different flow-chart ecosystems for MaaS-operations. We ended up with simplified, but principle MaaS-ecosystem where different types of data to be shared are identified:





Figure 1: simplified MaaS-ecosystem

The project achieved three significant effects during the learning process:

  • Increased awareness amongst the Maas-Peer partners of trustworthiness in data sharing
  • Clarification of values of data and data exchange for each of the actors in the MaaS ecosystem
  • Identification of risks related to data exchange for each of the actors in the MaaS ecosystem

The main conclusions and experience from the five months PLN collaboration were:

  • The MaaS data ecosystem is more complex than earlier anticipated
  • The data trust issues in MaaS are more complex than earlier anticipated.
  • Most of the trust issues are related to commercially sensitive data and potential unfair distribution of revenues across the value
  • The trust issues may be mitigated by contractual protections that satisfies both the needs of data providers and the data users.
  • The contractual protections should also regulate for transparency to demonstrate trustworthiness and to build trust between participants
  • The top-down approach of MaaS may be challenged by a more distributed, peer-to-peer model with an API ecosystem instead of a central service.
  • ODI, Microsoft and the five other PLNs have influenced the MaaS knowledge in Norway related to data-sharing and trust in a fundamental way

A short final report summarising the Maas-Peer findings and experience during the five months peer learning process i

ITS Norway will follow up the Maas-Peer project in MoneyMaaS (PengeMaaS) and the NOMAD-project. The findings in this project give valuable insight into the rationale for a research-oriented project on the business models for MaaS in general. A short final report summarising the project findings and experience during the five months peer learning process. Contact Trond.hovland@its-norway.no


Writen by Øystein Strandli Strandli Consutling