This is the second of two blogs on Data Collaboratives by Stefaan G. Verhulst of The Governance Lab (GovLab) at New York University. Stefaan explains the 5 specific value propositions of Data Collaboratives identified by the Gov Lab. In addition, he tackles the issue of data security. Specifically, how organisations need to professionalise the responsible use of data. To do this, organisations need to embrace the creation of Data Stewardship job roles. (Read Part II here)
At a broad level, data collaboratives offer the possibility of unlocking insights and solutions from vast, untapped stores of private-sector data. But what does this mean in practice? GovLab’s research indicates five specific public value propositions arising from cross-sector data-collaboration. These include:
- Situational Awareness and Response: Private data can help NGOs, humanitarian organisations and others better understand demographic trends, public sentiment, and the geographic distribution of various phenomena:
- One notable instance of this value proposition has been Facebook’s Disaster Maps initiative. Following natural disasters, Facebook shares aggregated location, movement, and self-reported safety data collected through its platform with responding humanitarian organisations, including the International Federation of Red Cross and Red Crescent Societies (IFRC), United Nations International Children’s Emergency Fund (UNICEF), and the World Food Programme (WFP).
Disaster Maps provide another tool in the humanitarian response toolkit to fill any gaps in traditional data sources and to inform more targeted relief efforts from responders on the ground.
- Knowledge Creation and Transfer: Data collaboratives can join widely dispersed datasets, in the process creating a better understanding of possible correlations and causalities as well as what variables make a difference for what type of problem:
- For example: researchers at Data2X, a collaborative platform dedicated to improving “the quality, availability, and use of gender data” has sought to leverage the insights generated by analysing geospatial data, credit card, mobile phone data, and social media posts to pinpoint problems that women and girls in developing countries are facing, such as malnutrition, education, healthcare access, and mental health issues.
- Service Design and Delivery: By definition, data collaboratives increase access to previously inaccessible (i.e. privately held) datasets. These datasets often contain a wealth of information that can enable more accurate modelling of ICSO service delivery:
- For example, the use of Geographic Information Systems (GIS) enabled WorldPop and UNFPA to map human populations that were traditionally unreachable through conventional approaches toward the goal of improving service delivery, resource allocation, urban planning and disaster management by development organisations.
- Prediction and Forecasting: Richer, more complete information from a data collaborative enables new predictive capabilities for ICSOs and others. Thus, allowing them to be more proactive and put in place mechanisms that prevent or at least mitigate crises before they occur:
- For example, the Malaria Elimination Initiative developed DISARM (Disease Surveillance and Risk Monitoring), a platform that uses satellite data and Google Earth data to predict Malaria outbreaks. Mobile phone data has also been used in predicting population displacement during the 2015 earthquake in Nepal, which helped international and domestic humanitarian organisations deliver aid more effectively.
- Impact Assessment and Evaluation: Finally, data collaboratives can aid CSOs in one of the most important (yet often neglected) steps of their value chains: monitoring, evaluation, and improvement. By leveraging data, CSOs can rapidly assess the results of their actions, as to iterate on products and programs when necessary:
- This is what Sport England did, for instance, when it used Twitter data to understand women’s views on exercise to inform its successful #ThisGirlCan campaign aimed at improving women and girl’s health and physical activity.
Professionalising the Responsible Use of Data
These value propositions offer a compelling case for greater use of private data through data collaboratives to solve complex public problems. However, a variety of concerns still exist. Some of these concerns (e.g. fears over privacy) involve public fears, while others (e.g. worries over a potential erosion of competitive advantage) are more internal oriented. Nonetheless, all of these concerns need to be addressed in order to foster greater trust and appreciation of the potential of data collaborative.
That is why there is a need to develop a framework that would guide the responsible use of data. GovLab has looked at these issues in a recent report, The Potential of Social Media Intelligence to Improve People’s Lives: Social Media Data for Good. Responsible data use has many aspects, and there are various degrees of responsibility. At the very least, it means having core (written) principles, and well-defined policies and practices for how data is collected, stored, analysed, shared and used (across the data lifecycle).
In addition, it is essential to conduct regular risk assessments that consider the balance between the potential value and dangers inherent at every stage of the data lifecycle. Such risk assessments can help data stakeholders decide when data sharing can be truly beneficial (or what the opportunity cost may be of not sharing the data). Several ICSOs have already started developing such responsible data frameworks such as Oxfam (Responsible Data Policy) and World Vision (Data Protection, Privacy & Security (DPP&S) framework). Increased awareness, further coordination (toward perhaps an ICSO Responsible Data Framework) and translation of these policies into decision trees may be required.
Data Stewardship roles
Yet not only do ICSOs and other private actors lack the frameworks to determine how to responsibly share and use data for the public good, they often lack a well-defined, professionalised concept of “Data Stewardship.” Today, each attempt to establish a cross-sector partnership built on the analysis of data requires significant and time-consuming efforts. ICSOs rarely have personnel tasked with undertaking such efforts and making such decisions.
The process of establishing “Data Collaboratives” and leveraging privately-held data for evidence-based policy making is onerous. Also, it is generally a one-off process and not informed by best practices or any shared knowledge base. Thus it is prone to dissolution when the champions involved move on to other functions.
By establishing “Data Stewardship” as a job function in organisations alongside methods and tools for responsible data-sharing, we can free data sharing for development from its stuck dynamic, and turn it into a regularised, predictable, and de-risked activity. Only then can ICSOs use and share their own data and that of others – including private companies – through data collaboratives to help transform how they achieve their missions while improving people’s lives.