Humanitarian Data Collection Methods and Tools

Rapid information collection and dissemination are crucial in emergency responses, as responders often operate under immense time pressures and without a comprehensive understanding of the situation (Van de Walle & Comes, 2014). However, the desire for comprehensive data can result in collection practices that may overlook issues of informed consent or the potential risks associated with aggregating and collecting personally identifying information (Crawford & Finn, 2014).

Data availability is a critical issue in humanitarian response, as the lack of appropriate and timely data can significantly impede coordination and, ultimately, the success of humanitarian operations (Lund, 2011). The response to Typhoon Haiyan demonstrated a direct correlation between the efficacy of coordination and the robustness of data infrastructure, highlighting the importance of data quality and accessibility in assessing needs and capacities (Chan & Comes, 2014; Madianou, 2015; van den Homberg et al., 2014).

Another of the primary challenges in humanitarian data collection is the lack of standardization. Data is often collected in different formats, leading to significant data-wrangling efforts. This lack of standards can impede the interoperability of data systems and complicate more comprehensive data analysis and sharing (Qadir et al., 2016). A good example is the collection of demographic data, which frequently presents its own challenges. Age categorization, for example, can be complex and context-specific. Benelli et al. (2012, p. 221) note that “disaggregating data by age requires the creation of categories for analytical purposes. Such categories are in part context-specific, and it is advisable to determine them in consultation with the local populations”. The Sphere Project handbook provides widely accepted age disaggregation standards for humanitarian settings, suggesting groupings such as children (0-5, 6-12, 13-17) and adults in ten-year age brackets (Sphere Association, 2018). The launch of the HDX, the formation of the Humanitarian Exchange Language (HXL), and the work of the Information Management Working Group (IMWG) led by UN OCHA has in recent years, helped tackle many of these issues around data standardization (Telford, 2020).

Joint rapid assessments have become crucial tools in humanitarian response. The Kenya Inter-Agency Rapid Assessment (KIRA) Mechanism was one of the first rapid assessment mechanisms to demonstrate the potential for collaborative tools to support efficient humanitarian action and strategic decision-making. These joint needs assessments provided quick yet comprehensive overviews of emergencies in Kenya, enabling faster and more informed responses (Limbu et al., 2015).

The adoption of mobile data collection technologies in humanitarian response led to a significant shift from the traditional pen-and-paper data collection methods (Lajoie, 2013). Summerville (2014) highlights how mobile applications have streamlined field data collection, analysis, and dissemination, drastically reducing response time and improving operational effectiveness. Chan (2012) further emphasizes the operational efficacy of smartphones and apps, noting that the immediacy of data transfer has significantly improved the coordination of relief efforts. Guha-Sapir and Checchi (2018) highlight how, in Myanmar, mobile data collection tools have streamlined considerably the assessment process, reducing the time required for data analysis and reporting.

Anderson (2015) outlines various tools currently used for mobile data collection in humanitarian settings, ranging from custom-designed applications to off-the-shelf solutions adaptable to multiple scenarios, and how these tools have enabled organizations to gather and analyze data more systematically in real-time, leading to more agile and informed responses. However, this digital shift in data collection efforts raises crucial discussions around data protection and privacy concerns. Kuner et al. (2017) delve into the complexities of safeguarding personal information within international data protection laws. This concern has grown with the rise of mobile data collection. It underscores the imperative for humanitarian agencies to adhere to robust data protection standards to avoid breaches that could jeopardize the safety and privacy of vulnerable populations. Over the past few years, humanitarian organizations have done significant work training humanitarians on data protection, responsible data, and more. This effort has been led by UN OCHA and IFRC, which have partnered on this effort since 2018, and their work has been codified into the Data Playbook Toolkit (IFRC, 2022).

One salient point in the literature is the phenomenon of data abundance resulting from mobile data collection. The ability to capture extensive data often leads to collecting more information than necessary (Morrow et al., 2016). While having a wealth of data can be advantageous, it can pose challenges in data storage and management and the potential for information overload, which can hamper rather than help decision-making processes.

Mobile data collection has changed how data is collected and where it is analyzed. The ability to transmit data instantaneously to headquarters or regional offices means that analysis is no longer confined to the field. Remote analysis allows for a broader range of expertise to be applied to the data, potentially leading to better outcomes (Morrow et al., 2016). However, this remoteness can sometimes detach the analysis from the on-the-ground reality, a challenge that organizations must navigate to ensure that the data is interpreted in the context of where it was collected.

In conclusion, while data collection in humanitarian response has significantly evolved by adopting new technologies, it presents opportunities and challenges. The sector must navigate standardization, privacy, and data management issues while leveraging the benefits of rapid, mobile-enabled data collection to enhance the effectiveness and efficiency of humanitarian operations.

Bibliography:

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