Data donation for non-profit status

Although trust in the secure handling of personal data has risen slightly among the Swiss population, there are still concerns about the collection, dissemination and storage of personal data. For example, 81% of respondents trusted public authorities to handle data in 2023, compared to just 63% in 2020/2021. However, we can also observe a similar trend in the economy (from 33% to 36%), although this increase is very small. Other studies also show that there is a greater willingness to provide personal data for charitable purposes such as current social challenges. This white paper aims to discuss how and under what conditions this can happen and offer people the opportunity to donate personal data voluntarily and in a self-determined manner for charitable solutions.

Publisher

The project "Data donation for non-profits" was developed by Risiko-Dialog in cooperation with the University of Zurich, Swiss Data AllianceZurich Urban Development, Stiftung Mercator, Zurich University of the Arts (ZHdK ) and the POSMO Switzerland cooperative.

In this white paper, we would like to present a framework and practical applications that show how voluntarily donated data can contribute to overcoming social challenges such as climate change, traffic congestion or health crises. As part of the project, various organizations were involved in this contribution on the basis of two use cases. The aim was to give people the opportunity to use their personal data to help solve social problems.

Use cases

In the first use case, the donated data supported the University of Zurich's crisis management team in restoring normal operations during and after the Covid-19 pandemic by providing a data-driven basis for decision-making. The insights gained on data donation were successfully integrated into UZH's guidelines. The prototype developed is available to all universities and the insights gained are publicly accessible. This use case underlines the effectiveness of data donation in organizations, especially in crisis situations.

In the second use case, we identified effective communication strategies that positively influence the motivation to participate in data donation and at the same time clarified the value of data donation for transportation planning. In collaboration with the POSMO data cooperative, data was collected via an app and made available to the city of Zurich in aggregated form for transport planning purposes. The use case places particular emphasis on a holistic approach that includes the technical infrastructure and active communication in addition to the concept of data donation. In the second use case, we therefore specifically investigated the conditions under which people are willing to donate their data for charitable purposes. Because of this approach, we worked together in a very broad-based consortium.

Results

We can summarize the following findings:

1. Based on the two use cases, we were able to show that voluntary data donation can make an important contribution to overcoming social challenges such as climate change, traffic congestion or health crises.

2. In addition, the willingness to donate data in Switzerland is high, but the purpose of data use plays a central role for data donors.

3. Successful data donation requires targeted communication, as this can significantly promote the acceptance of data donation. Different communication strategies were tested in the white paper and it was confirmed that congruent communication can motivate people more strongly to donate data. Furthermore, the motivation to donate can also be strengthened by regular feedback on the handling of the data.

4. Trust in the organization collecting the data is also important for data donors - binding legal framework conditions must be clearly formulated.

5. The use of data donation can support data-based decision-making, especially where representative data is difficult to collect (e.g. in healthcare or mobility). However, it is important that the results of data donation are embedded in the overall context of the problem. An analysis of the representativeness of the data and a comparison of the question and the results must always be taken into account.