We live in a changing world, one marked by a cascade of seemingly intractable challenges – and ever-increasing new challenges coming in different forms.

From climate change to economic inequality to the threat of global pandemics, the problems we face today are more systemic and interconnected. They are less amenable to direct intervention or responses from a single group or agency. Instead, they require solutions that take into account a multi-dimensional and intersectional set of actors and variables. In addition, there is growing recognition that solutions to contemporary challenges should be rooted in values such as justice and equity.

In short, new types of challenges require new types of solutions.

This is a necessity that the philanthropic sector in particular has been increasingly reckoning with, as it seeks to modernize and update its approach to grantmaking and its broader efforts to bring about social transformation.

Recent years have witnessed growing interest, in particular, in the potential for modernizing philanthropy through data-led innovations. For example, the Rockefeller Foundation has announced that it will invest $3 million into an open-source data science platform, Global.health, which aims to establish ‘a global early warning system for pandemics.’ Similarly, the Joseph Rowntree Foundation’s ‘destitution study’ seeks to use insights captured from data about those on the lowest incomes. These and a myriad of other examples are evidence of a new desire to explore the possibilities offered by already-existing data as well as new sources and types of data.

The GovLab, an action-oriented think tank, has spent almost a decade examining the potential for data to bring about positive social transformation. A recent collaboration with the Paul Ramsay Foundation seeks to examine the use of data and technology in philanthropy in particular, with the aim of identifying best practices and opportunities for philanthropic innovation. Our research indicates clear – if scattered – efforts to innovate the use of data within the field of philanthropy.

At the same time, we find that these efforts are not widespread or systematic and that in general, philanthropies are failing to fully harness the potential of emerging technologies like machine learning, artificial intelligence, and natural language processing. We also identified shortcomings in applying approaches like crowdsourcing to empower community voices and integrating data collection into the full life cycle of grant design. As a general principle, we believe that philanthropies’ enthusiasm for data-led solutions needs to be more adequately balanced by a recognition of the associated risks, such as those that threaten privacy, new forms of inequality, marginalization, and more.

How can philanthropies move in a more deliberate yet responsible manner toward using data to advance their goals? The purpose of this article is to propose an overview of existing and potential qualitative and quantitative data innovations within the philanthropic sector. In what follows, we examine four areas where there is a need for innovation in how philanthropy works, and eight pathways for the responsible use of data innovations to address existing shortcomings.

Four areas for innovation

In order to identify potential data-led solutions, we need to begin by understanding current shortcomings. Through our research, we identified four areas within philanthropy that are ripe for data-led innovation:

• First, there is a need for innovation in the identification of shared questions and overlapping priorities among communities, public service, and philanthropy. The philanthropic sector is well placed to enable a new combination of approaches, products, and processes while still enabling communities to prioritize the issues that matter most.

• Second, there is a need to improve coordination and transparency across the sector. Even when shared priorities are identified, there often remains a large gap between the imperatives of building common agendas and the ability to act on those agendas in a coordinated and strategic way. New ways to collect and generate cross-sector shared intelligence are needed to better design funding strategies and make difficult trade-off choices.

• Third, reliance on fixed-project-based funding often means that philanthropists must wait for impact reports to assess results. There is a need to enable iteration and adaptive experimentation to help foster a culture of greater flexibility, agility, learning, and continuous improvement.

• Lastly, innovations for impact assessments and accountability could help philanthropies better understand how their funding and support have impacted the populations they intend to serve.

Needless to say, data alone cannot address all of these shortcomings. For true innovation, qualitative and quantitative data must be combined with a much wider range of human, institutional, and cultural change. Nonetheless, our research indicates that when used responsibly, data-driven methods and tools do offer pathways for success. We examine some of those pathways in the next section.

Eight pathways for data-driven innovations in philanthropy

The sources of data today available to philanthropic organizations are multifarious, enabled by advancements in digital technologies such as low-cost sensors, mobile devices, apps, wearables, and the increasing number of objects connected to the Internet of Things. The ways in which this data can be deployed are similarly varied. In the below, we examine eight pathways in particular for data-led innovation. Figure 1, below, maps these innovations to the challenge areas identified above.

Systems mapping can help philanthropies assess different aspects of social problems and prioritize interventions.

Systems mapping, where relationships and feedback loops between actors and trends are visually depicted, can be used to illuminate the various issues associated with complex social systems as well as the multi-factor and intersecting drivers of a particular problem. This in turn can provide awareness of which issues are well-funded, support prioritization efforts, and help identify ‘orphan’ issues – problems that are not being addressed.

For instance, data-driven systems mapping has been used to identify the drivers of childhood obesity in Australia. Using this approach, the research team was able to synthesize several complex drivers and conclude that investment in socioeconomic factors could help minimize childhood obesity rates.

Network analysis and topic mapping can be used to identify new and emerging problem-solvers, key topics, and issues.

Methods to map actors and topics within ecosystems–more commonly known as networks analysis and topic maps–can help identify potential partners and influential stakeholders, visualize which issues actors are focused on, and who is receiving funding, and help to build consensus-based agendas.

For example, to help inform a data and research agenda for adolescent mental health, UNICEF and the Data for Children Collaborative partnered with The GovLab to apply its participatory topic mapping methodology. The exercise involved 70 experts from around the world, including youth advocates, who examined and prioritized various aspects of adolescent mental health. The final topic map helped prioritize key topics and issues associated with adolescent mental health that could be addressed through enhanced data collaboration.

Thick data methods can help philanthropies understand the experiences of the communities they aim to serve and support.

Thick data – data that captures the lived experience of people and communities, represented for example by user-generated images and narratives–can help philanthropies shift individual and community voices to the centre of their decisions.

For instance, Fire to Flourish, a disaster resilience, and community development program, is using thick data methods to combine lived expertise of communities with data-driven modeling and decision-support tools to test a new model of community-led resilience. A number of researchers and organizations are similarly leveraging artificial intelligence and natural language processing to automate the analysis of many voices and unlock even greater insights about individual experiences.

Positive deviance can be used to assess what is working and improve internal decision-making processes.

Individuals or communities that have better outcomes despite similar challenges are known as ‘positive deviants.’ Studying these outliers can allow us to identify how problems are being solved, and highlight the contributing factors to more effective interventions. By applying analytical methods that search for such positive deviants, philanthropies can encourage decision-making processes that include system outliers and improve how programs are delivered.

For instance, The Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ) Data Lab identifies and analyses the statistical outliers in data sets in order to unlock the potential innovation they hold. They are able to create pilots to understand what behaviours made the positive outcome possible and whether behaviours can be transferred. GIZ’s approach is being applied within the United Nations Development Programme’s (UNDP) Accelerator Labs to identify how local actors are solving social problems and developing targeted interventions at the local level.

Non-traditional data can help make visible what used to be invisible.

Telecommunications data, citizen science, social media data, and wastewater data are just some examples of non-traditional data sources that are expanding the information basis for philanthropy. Non-traditional data, often collected through new technologies and platforms by private actors, are able to capture more granular insights at scale about social problems. When used responsibly, these data sources have the potential to radically transform the grantmaking process. They can be used to identify underlying causes, enabling philanthropies to get closer to the root of problems.

The power of non-traditional data was evident throughout the Covid-19 pandemic when public health experts and officials used mobility data to understand patterns in the spread of the virus and the impact of national and local control measures. Taiwan, for instance, used mobile phone data to track individuals’ locations during their quarantine period after entering the country; this contributed to Taiwan’s low case counts in 2020.

Simulation can help philanthropies better anticipate and plan for the future.

Data generated through simulations can inform funding needs before they occur, allowing philanthropies to better prepare for, and measure the impact of, their efforts. Simulations can be used for scenario planning, giving stakeholders room to re-imagine and plan for what is possible. Recent advances in automated simulation platforms allow users to combine big and thick data sets. These could provide philanthropies with new ways to assess the outcomes of programs and partnerships, including new partnership structures such as data collaborations.

Climate Interactive, MIT Sloan Sustainability Initiative, and Ventana Systems developed ‘En-ROADS‘ – a data-driven simulation platform that forecasts how different policy interventions could impact the climate crisis. The dashboard has been used by 130 US Congress Members. Additionally, users have noted that it increased their motivation to combat the climate crisis.

Similarly, researchers at the University of Melbourne have created positive outcomes with their use of machine learning and data mining techniques to investigate the relationships between sustainable development goals (SDGs). Using these techniques, the researchers were able to identify patterns to help policy-makers decide what needs to prioritize in order to maximize the success in achieving the SDGs.

Rapid experimentation has the potential to transform the grantmaking lifecycle and build greater philanthropic legitimacy. 

Data generated through different types of experiments can help philanthropies understand what works in real-time and adopt a more iterative approach to program development and adjustment. These experiments may involve new participatory models for grant giving that include grantees in decision-making.

For instance, in 2015, Finland’s Design for Government: Humancentric Governance Through Experiments program proposed a new model to integrate experiments into Finnish policy design. One of the policies that benefited from this model was related to basic income, whereby a number of experiments were undertaken. Design Helsinki, the design-thinking lab of the City of Helsinki, adopts similar experimental and iterative approaches to their city projects, from rebuilding Helsinki’s City Hall to increasing levels of physical activity amongst older adults.

Collective intelligence methods can create new opportunities for achieving impact.

Collective intelligence methods include new approaches to gathering insights from different groups, including the use as knowledge graphs and crowdsourcing. They open up a host of new ways to collaborate with people, partners and communities. Widespread access to new technologies has expanded collective intelligence initiatives, allowing for broader participation, in real-time. With more diverse populations able to generate and collect data, philanthropies are presented with unique opportunities to co-design data initiatives that reflect community values.

Leveraging open-source algorithms and data science competitions to capture crowdsourced intelligence, The World Bank is using machine learning techniques and other new data collection instruments to transform how the organization predicts and measures world poverty.

So what will it take to bring philanthropy into the 21st century?

A 20th-century toolkit is no longer sufficient for the 21st-century challenges that philanthropies seek to solve. The sector needs data innovations to improve how it identifies needs and priorities, reimagines decision-making, builds cross-sector ties, develops a culture of iteration and agility, and rebuilds legitimacy through improved value and impact assessments. More importantly, while we can develop more data intelligence, we will also need to focus on how to translate this into decision intelligence – i.e., the capacity to translate insights into action.

Data doesn’t simply represent an opportunity for new or better solutions. It also represents a broader opportunity to reimagine how philanthropic work is done and shared, and how its impact is distributed across populations. The responsible use of data and its associated methods and tools have the potential to unlock precision philanthropy, design more bespoke interventions, and be part of a wider process of social transformation.

Achieving these wider goals will require rethinking how data is used, and the very meaning of data innovation. In particular, we need to move beyond simply accessing and using data at the level of individual organizations, and toward a less atomized and more collaborative process of decision intelligence. Philanthropic organizations need to leverage data-enabled collaboration throughout the funding lifecycle. Data analytics itself can play a role in catalyzing and building coalitions between government, non-government, and civil society organizations to enhance collective intelligence and build shared agendas.

In short, what’s required is a transformation of the broader ecosystem of philanthropy and the emerging field of data for social good. The power of data to generate new insights and innovations will be directly proportional to the extent we are able to break down silos – organizational, cultural, and technical–and create broader alliances and networks within the field of philanthropy. And so we end where we began: in a world where our challenges are increasingly interconnected and systematic, we need solutions that are likewise more collaborative, and collective, and that derive from cross-sectoral interaction and sharing.

Stefaan G. Verhulst is Co-Founder and Chief Research and Development Officer of the Governance Laboratory @NYU (GovLab) where he is building an action-research foundation on how to transform governance using advances in science, data and technology.
Email: stefaan@thegovlab.org

Lisa T. Moretti is a Digital Sociologist and tech ethics activist based in the UK.
Email: lisatalia.moretti@gmail.com

Hannah Chafetz is a Research Fellow at The Governance Laboratory.
Email: hchafetz@thegovlab.org

Alex Fischer is the Head of Research at the Paul Ramsay Foundation in Australia.
Email: alexfischer@paulramsayfoundation.org

The authors would like to acknowledge Ciro Cattuto, Ben Gales, Tessa Grey, Nicola Hives, Michael Hogan, Kimber Kunimoto, Sarah Pearson, Barry Sandison, Keren Swanson, and Evan Tachovsky for their contributions to this article. Thank you for your time and effort.