Harvard business school defines predictive analytics as the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions. The predictions could be for the near future—for instance, predicting the malfunction of a piece of machinery later that day—or the more distant future, such as predicting your company’s cash flows for the upcoming year. Predictive analysis can be conducted manually or using machine-learning algorithms. Either way, historical data is used to make assumptions about the future.
Predictive analytics is a relatively new but rapidly evolving tool in the humanitarian sector, and already changing the way organizations think about crisis response. Historically, humanitarian efforts were mostly reactive—waiting until disaster struck to mobilize aid. Now, with the ability to analyze huge amounts of data in real-time, organizations are starting to predict disasters and act before they hit, making responses faster and more efficient. However, with this innovation comes significant risks and challenges. As powerful as predictive analytics can be, the technology has to be handled with care.
At the heart of it, predictive analytics is all about making sense of patterns. It uses data from multiple sources— satellite images, social media chatter, weather reports, and historical data—to identify potential crises before they unfold.
These systems rely on indicators—key data points that serve as early warning signs. For example, take rising river levels; a pretty straightforward indicator that flooding might follow. There are also proxy indicators, which fill in the gaps when direct data is missing. In some cases, satellite images of an area’s infrastructure—like the type of roofs on buildings or how many cars are parked outside—can give a rough estimate of economic conditions. Humanitarian organizations use these clues to build a more complete picture, even when on-the-ground data is sparse. Both indicators can include data that points towards shorter-term risks (such as predicting an especially bad hurricane season a few months in advance) or broader longer-term risks (such as historical conflict data indicating a certain area is likely to experience future issues).
These inputs are used to build models, where output data indicates the likelihood of certain types of crises or events happening in a specific location or situation. AI and machine learning techniques are highly applicable within these models and allow for crunching information related to patterns in big datasets related to the above-discussed indicators. With machine learning driving the insights within a predictive analytical model, future guesses about what may occur can coalesce.
One of the biggest advantages of predictive analytics is how it shifts humanitarian work from reactive to proactive. Rather than rushing in after the fact, organizations can now send help before a crisis peaks. For example, in 2020, when floods threatened Bangladesh, humanitarian teams used predictive models to forecast the consequences of the unfolding events. They reactedahead of the disaster, sending in cash and supplies before the water even reached the villages. That’s what anticipatory action is all about: predicting, then acting. Due to predictive analytics, the United Nations was able to release $5.2 million in aid before the floodwaters had even hit their highest levels. Communities had time to move their livestock and secure their homes. Instead of chaos, there were already preparations in place.
From a financial standpoint, it’s also a game-changer. Being able to target resources before a disaster hits means more efficient resource allocation and less waste of money. In Bangladesh, for every dollar spent on anticipatory action, $0.80 in direct benefits was returned to affected families. The indirect benefits, like improved food security and faster recovery, were equally impactful.
A particularly good example of applied predictive analytics is the Forecast-based Financing project by the International Federation of Red Cross and Red Crescent Societies (IFRC). This system taps into weather predictions to preposition resources in areas where floods or droughts are expected. In Peru, this approach has helped people prepare for a particularly cold winter in advance, saving lives and reducing the need for post-disaster aid operations.
The World Food Programme’s HungerMap uses predictive models to anticipate food insecurity in conflict zones. Traditional data collection in these areas is often risky or near-impossible, but by analyzing trends like market prices and satellite images, HungerMap helps prioritize aid to the regions most at risk. It is an invaluable tool in preventing hunger crises before they spiral out of control.
Another strong tool is the UNHCR’s Project Jetson, which used predictive analytics to forecast forced displacement in Somalia. By analyzing climate data, market prices, and remittance flows, the tool helped agencies prepare for waves of migration long before people started moving. This tool is a perfect example of how predictive analytics transforms how we manage complex human crises.
Even in public health, predictive analytics is finding useful applications. The OCHA-Bucky model, developed by the UN’s Office for the Coordination of Humanitarian Affairs, has been used to predict the spread of diseases like COVID-19 in vulnerable populations. With this tool, aid organizations can figure out where to send medical resources before an outbreak gets out of control, potentially saving thousands of lives.
The upcoming Inform Warning platform, a project developed as a partnership between JRC, DFS, and UNDP, will also serve as a hub for predictive analytics. It will create a globally comparable predictive model of future risk, such that different countries' future risk levels can be anticipated depending on current crisis factors. In specific situations, Inform Warning also hopes to provide additional analysis and information to model specific future scenarios which may lead to disaster scenarios.
For all the promise of predictive analytics, there are risks too. The first is bias in the data. Predictive models are only as good as the information they are fed. If that data is biased or incomplete— if it doesn’t account for marginalized groups like LGBTQ+ individuals or remote populations—the model could miss critical details, leaving entire communities out of aid efforts. That is why it is so important for human oversight to be part of the process.
Another risk is data security. Humanitarian organizations collect huge amounts of sensitive data, from personal information to location details. In politically unstable regions, if this data falls into the wrong hands, it could put vulnerable populations at even greater risk. Ethically, this data can also be quite complex, as it involves many of the quandaries associated with data science projects in aid.
Lastly, there is a risk that these models might be potentially dangerous to make publicly available. Openly flagging a future crisis in a certain location or country may result in negative political responses from the governments and people in that area. Predicting future events can affect current behavior and current situations, thus changing the future being predicted.
A 2019 workshop on predictive analytics in the Humanitarian sector chaired by UN-OCHA found that “There is agreement that models are tools, not solutions and for a model to perform well, decision-makers need to be involved from the beginning (and throughout) in order to frame the question(s) for the model to answer.” This statement captures the developing consensus on best practices and routes forward for PA in the sector.
Predictive analytics has the power to reshape humanitarian work in incredible ways. Predicting crises before they strike offers a way to act early, save lives, reduce suffering, and use resources more efficiently. But it’s a tool that needs to be handled responsibly. The risks of bias, data breaches, and ethical overreach are real. As this technology becomes more deeply integrated into humanitarian work, we must keep those risks in check. With the right balance, predictive analytics can be a game-changer—ensuring that aid reaches those who need it most, before disaster strikes.
Sources:
https://research.library.fordham.edu/dissertations/AAI30246999/, https://centre.humdata.org/glossary-2/predictive-analytics/
https://www.mironline.ca/can-ai-in-the-humanitarian-sector-save-lives/
