Chapter 19: Big data, giving and volunteering

Big data discourses linked to giving and volunteering

While practice remains limited at this stage there is a growing popular discourse around the relationship between big data and social progress associated with philanthropy and volunteering. This discussion has four main inflections. First, there is growing interest in the co-creation and use of big data to create progressive social and environmental change. Stimulated by the ‘data for good’ civil society movement, this discussion highlights the potential of crowdsourced knowledge and digitally-savvy volunteers to both create and maximise the use of big data to make change happen by diagnosing social or environmental problems and co-producing solutions. Reminiscent of earlier invocations of the democratic power of the internet (see, for example, Rheingold 1993), and now manifesting in discussions of digitally-enabled social innovation (see Bria 2015), the data for good movement highlights the aggregate effects of many people producing, observing and inferring data in common ways for a shared purpose.

Second, the diagnostic potential of big data for identifying social needs and effective points of intervention for change has been mooted (Mead and Dreicer 2013; Smith 2014). As discussed in relation to types of big data above, for example, Smith (2014) has highlighted the importance of contextual data—such as the scope and location of a particular social problem—to making strategic philanthropic decisions.

Third, the predictive capabilities of big data is receiving growing attention from fundraisers concerned with observing donor contribution patterns over time and/or specified geographies in order to predict and derive maximum benefit from donor behaviour (Blackbaud 2014; Stevens, 2014). Deriving much from commercial marketing strategies, this discussion is concerned with the benefits of inferred data for maximising giving to particular causes.

Finally, the role of big data in assessing the social impacts of funded interventions has gained attention from philanthropy, particularly in North America (see Mead and Dreicer 2013; Smith 2014; States News Service 2014). Reflecting on practice within one large foundation, Mead and Dreicer (2013) suggest that effective data analytics can assist foundations to learn what is working and why across their grants portfolios. Smith (2014) also notes that impact data is potentially valuable to support institutional giving but observes that it is the most elusive data type of all. The benefits of using data to increase understanding of outcomes and impacts is not limited to philanthropy, and has been similarly mooted as valuable but currently difficult to achieve for NPOs (see de Las Casas, Gyateng and Pritchard 2013). The normative and practical challenges of measuring social impacts are beyond the scope of this review but are well documented in the nonprofit and social enterprise management literature (see, for example, Carman 2009; Luke, Barraket and Eversole 2013b). It is notable that United States (US) media reports have observed a revival of interest in storytelling to communicate social impacts as an explicit pushback against big data-driven impact measurement discourses (see Jensen 2014).

International context

With regard to philanthropy, the Foundation Center in the US provided early leadership in both promoting and enabling collaborative use of data by foundations to better enable strategic approaches to giving. In 2012, 15 of the largest US foundations partnered with the Foundation Center on the ‘Reporting Commitment’ project, which aimed to open up and integrate grantmaking data, with a focus on transparency to support strategic decision-making about philanthropic giving (States News Service 2012). Shared data has been utilised by the Foundation Center to develop interactive maps that visualise philanthropic giving. Since the initiative was established a further four foundations have joined (see http://glasspockets.org/philanthropy-in-focus/reporting-commitment-map).

Parallel to developments in policy and philanthropy and introduced above, civil society has given rise to a ‘data for good’ movement, which is explicitly concerned with the potential of data science to help respond to big social problems (PR Newswire 2013a). Initiatives to stimulate crowdsourced big data production and observation are growing, particularly in the areas of health and environmental protection. Examples include:

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  Cancer Research UK’s use of the Cellsider program through which volunteers rate anonymous images of blood in order to reduce sample classification to a manageable number for medical researchers (Fildes 2013)
  
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  Conservation International’s use of big data platforms to store and integrate massive sets of observational data for use by environmental scientists across locations (Worth 2013)
  
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  Flowminder’s use of mobile phone records to generate reports about the locations of displaced people for relief agencies after the 2010 Haitian earthquake (Taylor et al. 2014), and
  
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  Online Patient Network, Patients Like Me, through which many thousands of patients share information about symptoms and treatments, which generates outcomes-based health data utilised by medical researchers, pharmaceutical companies and public health organisations (Frost and Massagli 2008).
  

Inferred big data are also being utilised to support fundraising efforts by some NPOs in the US. Reporting on a case study of the use of big data to support the cultural sector in North Carolina, Stevens (2014) finds that big data can be effectively used to predict sector support for a recapitalisation strategy following loss of capital as a result of the 2008 Global Financial Crisis.