Leah Kieff
In developing countries, the impact of significant events such as earthquakes, extreme weather, terrorist attacks, cyber incidents, or health emergencies can be more pronounced given the lack of planning, budget constraints, and weak infrastructure that these countries are confronted with. Leveraging data effectively is fundamental to managing the impact of these disasters. New and emerging technologies, including machine learning (ML) and AI, can be leveraged to process and organize the data into usable information, as well as to support effective collection. But any mass collection of data carries privacy concerns, which must be mitigated from the start. Given the recent devastation of Hurricane Melissa in the Caribbean and with the AI Impact Summit approaching in February 2026, these are timely topics to address.
The type of data that may be useful in a disaster can range from personal health information to satellite imagery of an affected geographic area. Satellite imagery can be invaluable in determining the level of destruction, including baseline and post-disaster comparisons. Census data can help determine the demographic characteristics of a region for logistics for evacuations, and the movement of supplies. Successful intelligence collection and analysis can allow law enforcement to stop a terrorist attack from occurring.
The usefulness of data in disaster management is only increasing in the era of big data. The term “big data” describes massive data sets that are collected from the palms of our hands, via cell phones, wearable technologies, and digital transactions. This data may be actively volunteered by the individual (such as social media postings) or passively provided (such as automated means like credit card usage). While large data sets such as censuses have been collected for thousands of years, the types of data sets, as well as collection methods and speed, have been revolutionized by technological advances.
Despite the promise, the collection and use of data for disaster management is not without risks. Data quality may not be accurate, leading decisionmakers to believe that they have a truthful picture of the disaster, including those impacted, when they may not. And even if the data sourcing and validation are done correctly, the aggregation, synthesis, and analysis must be done well to allow for effective inference-driven decisionmaking. The existence of these huge data repositories without a process to transform them into informed decisions is equivalent to attempting to put crude oil into your car instead of gasoline. Data pipelines can refine raw data into insights. These pipelines can and should be supported by AI and ML to increase efficiency and accuracy.
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