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Working in humanitarian analysis, especially in crisis zones, can be emotionally and psychologically challenging due to the constant exposure to distressing situations, such as conflict, displacement, and human suffering. The emotional toll is an inevitable aspect of this work, as analysts often deal with reports and data that reflect severe hardship, trauma, and loss. It also stems from the ethical responsibility that humanitarian analysts carry. The knowledge they uncover can directly influence the delivery of aid, policy decisions, and interventions that impact vulnerable populations.
The tension between the urgency of the situation and the limitations of available data often leaves analysts wrestling with the challenge of how to best represent the true magnitude of a crisis. This situation can create a constant psychological burden, as analysts balance the need for precision and clarity with the devastating human realities behind their findings. To handle this, it is essential to develop coping mechanisms and a strong sense of emotional resilience.
In the second contribution of our Human Algorithm series, we interviewed Hélène Pélisson, Senior Analyst at Data Friendly Space (DFS). With a master's degree from the NOHA network, specializing in humanitarian action and humanitarian law, Hélène has dedicated the past six years of her professional journey to having a positive impact in crisis-ridden regions around the world. Her career began as a humanitarian analyst, where she immersed herself in addressing multifaceted challenges, including the compounding effects of the COVID-19 pandemic on existing crises in Africa, the complex situation in Sudan, and the ongoing conflict in Ukraine.
With her, we discussed the emotional costs of analyzing crises, the role of misinformation and bias, and how DEEP and GANNET have enhanced her analysis work.
How do you handle the emotional or psychological toll of analyzing distressing situations in crisis zones?
Constantly working with information about people’s suffering is undeniably challenging. I don’t think there’s a single right or wrong way to shield yourself from the psychological toll it can take. However, I do believe it’s crucial to create boundaries—taking a step back at the end of the day, being present in your daily life, and leaving work where it belongs, rather than carrying the weight of distressing events with you constantly.
At the same time, when analyzing crises, it’s essential to remember that behind the data are real people—individuals facing unimaginable hardships. It’s easy, for analysis purposes, to reduce suffering to numbers (people affected, people in need, fatalities, displaced individuals, etc.), but these are human lives, just like yours and mine. Maintaining a balance is key: protecting yourself from emotional exhaustion while not becoming so detached that the human aspect gets lost. Listening to people’s stories serves as a daily reminder of why this work matters, and I believe that’s important.
What role does misinformation or disinformation play in the challenges you face in your work?
Misinformation and bias are significant challenges when analyzing secondary data. Political perspectives can shape media coverage and social media narratives, while misinformation can also appear in government reports and other sources. Today, information is often used as a strategic tool, much like culture was seen as a soft power in the late 20th century. Analysts must always account for this factor.
This is why human analysis remains crucial and cannot be fully replaced by technology. Analysts must triangulate data—verifying sources, considering potential biases, and cross-checking with other documents, especially primary data, to ensure accuracy. Once verified, transparency is key. Clearly stating where and how information was obtained is essential to maintaining credibility and minimizing misinformation, disinformation, and bias in analysis.
What tools or methodologies do you use to assess the needs and conditions of affected populations?
I started my role as an analyst with manual analysis, going through documents myself, taking notes, and compiling secondary data reviews. However, this process was incredibly time-consuming, and I often felt I was missing key information simply because there wasn’t enough time to go through hundreds of documents or reach out to all potential partners for internal data.
Since joining DFS five years ago, I’ve relied on two major tools: DEEP and GANNET. Though different in their approaches, both have been invaluable for distinct purposes. I appreciate DEEP for its ability to produce comprehensive, detailed analytical products. It allows me to take my time reviewing tagged information, triangulating data, incorporating quantitative analysis, and collaborating with other organizations through brainstorming sessions and risk analysis. On the other hand, GANNET is ideal for fast-paced analysis when urgent insights are needed from multiple sources.
Each tool serves a unique purpose, saving time and enhancing accuracy—not just for analysts but also for those with limited analytical backgrounds who require reliable information.
Stay tuned as we continue to showcase the humans behind our algorithms.
