Building and maintaining trust is an ongoing and iterative process that remains a focus throughout the life cycle of a data sharing partnership.
Of course, it’s easier to build trust when data partners have previous experience working together. Prior successful collaboration improves baseline levels of trust and comfort. With prior positive experiences of working together, established communication channels and relationships facilitate working together on new initiatives.
But prior experiences aren’t necessary precursors to building trust in new partnerships. Setting up relevant governance mechanisms can help build and maintain trust, even when partners have never worked together before. Participatory governance models such as data trusts or collaboratives, detailed and standardized data sharing frameworks or memorandums of understanding between partners, and conflict resolution mechanisms are among the most widespread and effective mechanisms for doing so.
Participatory governance models — Hong Kong Data Trust 1.0
Hong Kong’s public transportation system consists of taxis, Mass Transit Railway (MTR), buses, minibuses, and tramways. Each transport sector is privately owned, and data sharing among providers has historically been limited. The critical barrier to data sharing has been lack of trust among stakeholders and the absence of a framework to govern data sharing.
A Data Trust at the University of Hong Kong (HKU) aimed to overcome this barrier through data sharing in real time. This Data Trust was a proof of concept for a trusted third-party model that encouraged data controllers (public transport operators) to securely share their data with the Data Trust. The Trust itself was an entity hosted by HKU (a neutral third party) with fiduciary responsibility to the data controllers and the technical capacity to analyze and process the data.
Relations between the public transport operators and HKU were formally established through memorandums of understanding. Data sharing via the Data Trust was also facilitated by a clear legal framework, constant communication with stakeholders, and open channels for feedback.
Detailed and standardized data sharing frameworks
Development Data Partnership is an initiative that seeks to meet the needs of international development organizations. It created a platform to connect them with technology companies or data partners. Since building trust between partners, negotiating terms, and entering into a data sharing agreement requires time, Development Data Partnership created a highly detailed master licensing agreement that lays out procedures for crucial aspects of data management, such as ownership and access. All partners sign the same agreement, and this provides the framework under which the Development Data Partnership must gather, harmonize, process, and store data. The agreement makes the procedures predictable and trustworthy, lowering barriers and providing an easy system for a partner to share data.
Standardized agreements, however, are not always the best option. For example, Global Fishing Watch works directly with national governments to track illegal fishing and enforce action against rogue actors. National governments are both data providers (alongside other public and private providers of automatic identification systems and vessel monitoring systems) and users within the initiative. Given the direct engagement with governments and the specific regulatory requests of each partner, creating standard terms of engagement is not always possible.
Data collaboratives — an example from California
The California Data Collaborative (CaDC) is an independent nonprofit working as a coalition of water supply agencies in California to facilitate data-driven water policy and operations. According to the GovLab, “Data Collaboratives are a new form of collaboration, beyond the public-private partnership model, in which participants from different sectors — in particular companies — exchange their data to create public value.”
CaDC seeks to make water data analytics central to improved water management. It maintains a secure, cleaned, and standardized water use database for each member agency. Using the data, CaDC creates data analysis tools and develops directly actionable insights.
The CaDC is governed by a steering committee composed of members from all participating water supply agencies. This Committee sets the research and policy priorities of the CaDC and identifies pilot projects for the collaborative. In its initial workshops, the CaDC developed a “trust framework” that included standardized data sharing and data transfer agreements that were available to all. Organizers hoped the “open” agreement would promote transparency and ensure everyone had access to the same data sharing rules. This equitable, transparent mode of governance has contributed to the smooth functioning of the CaDC.
Even though conflict is an inevitable aspect of cross-organizational and cross-sectoral partnerships, this review of data sharing partnerships found limited mention of formal conflict resolution mechanisms. The experts consulted suggested that disagreement tends to cluster around the establishment of the initiative rather than its implementation or functioning.
Practices such as meeting regularly with partners and establishing clear decision-making processes and governance bodies help build the tools for conflict management and ensure that issues that arise within the initiative are sorted out regularly. However, more research and experimentation are needed to understand which conflict resolution mechanisms are optimal for data sharing initiatives in the development sector.
*More research, experimentation and/or knowledge exchange is needed.