modern conflicts are increasingly shaped by information. data—when collected, refined, and interpreted with a structured intelligence methodology—becomes a strategic asset. this article explores how the intelligence cycle can be applied to open-source conflict datasets to support decision making in humanitarian, security, and geopolitical contexts.
every intelligence process begins with a question. in our case: “what can conflict data tell us about emerging risks in the middle east and afghanistan?”
this question sets the scope and determines the type of data required—conflict records, actors involved, temporal patterns, and socio-economic indicators. defining direction ensures that analysis remains focused, relevant, and actionable.
using openly available datasets, such as the asia conflicts database used in the accompanying notebook, we collect information about incidents, dates, locations, fatalities, and actors. as in any intelligence-led process, reliability and accuracy are crucial. even small errors in dates or categories can distort the entire analytical picture.
image 1: fatalities by country
before any model or prediction can be trusted, the underlying data must be valid. raw data is never ready for decision making; it requires careful cleaning and standardization.
processing transforms unstructured or inconsistent information into a usable analytical asset. in the notebook, this includes cleaning date formats, normalizing categories, encoding actors, and scaling numerical variables.
image 2: cleaned dataset
during this stage we also merge multiple datasets—conflict incidents, socio-economic indicators, and temporal patterns— into a unified structure. this processed dataset becomes the foundation for deeper intelligence work.
once processed, the data is explored to identify patterns, anomalies, and long-term trends. visualizations highlight conflict hotspots, annual escalation cycles, and actor dynamics across the region. afghanistan repeatedly shows the highest number of fatal incidents, indicating persistent instability and a chronic lack of structural security.
image 3: conflicts on the map
combining conflict frequencies with economic indicators reveals an inverse relationship between gdp per capita and incident density—poorer regions tend to experience more violence. however, the relationship is not linear: political fragmentation, tribal structures, and external influence all modulate the risk environment.
with the cleaned dataset, predictive models such as xgboost can be trained to estimate future flashpoints. these predictions are not certainties, but they provide valuable foresight for humanitarian agencies and strategic planners seeking to prioritize resources.
the purpose of intelligence is not analysis for its own sake, but enabling informed decision making. once conclusions are extracted, they must be communicated clearly to those who rely on them.
in this case, the outcomes can guide organizations in:
effective dissemination transforms data into intelligence and intelligence into impact.
the intelligence cycle is iterative. after disseminating insights, analysts must evaluate the accuracy, relevance, and limitations of their conclusions. this feedback loops back into the direction phase.
image 4: intelligence cycle
in our analysis, future improvements could include:
intelligence is not about secrets—it is about understanding. by applying a structured framework like this intelligence cycle to open data, analysts can uncover deep insights into global challenges.
the combination of data science, ethical methodology, and intelligence practice empowers us to identify emerging risks, anticipate crises, and support better decisions in an increasingly complex world.
the analysis presented in this article is based on publicly available data and synthetic models created for research and educational purposes. it does not represent classified information, operational assessments, or official intelligence from any institution. while every effort has been made to ensure accuracy and methodological rigor, the interpretations and conclusions are exploratory and should not be considered definitive. this work aims to illustrate analytical techniques, promote responsible data use, and encourage critical thinking in the context of open-source intelligence.
- a. r. brea, written 11/2025
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