Abstract

Data is crucial for decision-making as it guides policy and improves accountability. In 2014, the United Nations advocated for mobilizing the data revolution to promote sustainable development. Innovation within the industry is deemed crucial for addressing challenges in improving data for achieving and overseeing sustainable development (UN IAEG, 2014). However, innovation is often an imprecise, nebulous, and broadly applicable concept. Numerous organizations, such as the UN, governmental institutions, and NGOs, perceive a need to exhibit their involvement in ‘innovation’ due to the expected benefits for adopters, the creative firm, and society at large (Porter, 2011). This article delineates a study, encompassing an extensive literature review and a series of semi-structured interviews, aimed at understanding “data-driven innovation” and its use in NGOs. The research improves the understanding of the term “data-driven innovation” in contemporary scientific literature. Moreover, it provides an empirical analysis of the challenges NGOs face when utilizing data-driven innovation techniques for sustainable development, while also pinpointing examples that could serve as models for future applications.

Emerging technologies are causing a swift increase in data volumes and varieties, creating unprecedented opportunities for societal advancement and transformation (UN IAEG, 2014). Innovative, data-driven approaches are refining strategies and enhancing efficiency across various sectors. Data offers new opportunities to refine decision-making by guiding policies and enhancing accountability. The discussion of data for policy should distinguish between two fundamental types of data (Poel et al., 2015). The first is the use of public datasets (administrative open data and statistics) that are now employed more extensively, exploited with greater intensity, and integrated. The second pertains to innovative data sources, such as citizen reporting (e.g., crowdsourced surveys), open web data (e.g., social media), digital breadcrumbs (e.g., mobile phone data), and remote sensing data (e.g., satellite imagery) (UN Global Pulse, 2012; Letouzé, Meier, & Vinck, 2013; Bellagio Big Data Workshop Participants, 2014). The advancement of novel methodologies and analytical tools increases the capacity to derive valuable insights from diverse data sources to inform policy decisions. The innovative processing of data produces new economic and social benefits, creating a virtuous cycle that encourages more use of data-driven decision-making and analysis (Hilbert, 2013). The UN Independent Expert Advisory Group, in its report ‘A World that Counts,’ recommends promoting and diffusing innovation to harness the data revolution for sustainable development. Enhanced data provides superior insights, which requires strengthening sector capacities (UN IAEG, 2014).

Innovation refers to the introduction or adoption of a concept, activity, or object deemed innovative within a particular context. It differentiates itself from invention, as invention relates to creation, while innovation concerns the improvement or alteration of a subject (Edison, Bin Ali, & Torkar, 2013). The scope of innovation encompasses products, processes, organizations, industries, and settings. Innovation may be defined by two dimensions: the degree of originality related to the context of innovation, and the kind of innovation relating to its focus (Edison et al., 2013). Innovation research has classified many types based on their unique characteristics and the varying effects of environmental and organizational factors on their adoption (Jansen, Van Den Bosch, & Volberda, 2006; Kimberly & Evanisko, 1981; Light, 1998). Product innovations involve the creation and introduction of new or improved products or services (West, Ford, & Ibrahim, 2015), while process innovations focus internally, aiming to improve the efficiency and effectiveness of organizational processes (Boer & During, 2001). Deriving insights from data requires developing and executing innovative data-driven approaches. However, the concept of “data-driven innovation” remains inadequately defined, as do the methods through which it may be realized.

Non-governmental organizations have increasingly emerged as important contributors to development cooperation alongside traditional partners (IFAD et al., 2013; Rice, 1983; Eade et al., 2000). NGOs are non-profit groups that operate by providing services and facilitating change through resource mobilization and information distribution (Doh & Teegen, 2003; Spar & La Mure, 2003). Their numbers have risen substantially over time, and they now control a larger share of humanitarian resources than before (Macrae et al., 2002). Data is a significant asset for NGOs to improve decision-making and guide their strategies. The UN IAEG (2014) contends that enhanced innovation is necessary to overcome current challenges. Since NGOs are crucial agents of sustainable development, it is vital to understand their role in promoting the data revolution and the methods they may use to enable data-driven innovation.