This report explores various options for the development of a Conflict Early Warning System (CEWS) that can immediately flag countries or regions that face imminent emerging conflict, support strategic and programmatic planning and enhance conflict analysis as well as existing capabilities by providing a common platform that is fully integrated into the network model and decision-making cycle of the relevant ministries’ departments and posts. It analyses the practices, principles and promises of EW based on a review of academic literature; an analysis of a set of Conflict Early Warning Systems currently deployed by governments, international organisations, non-governmental organisations, research institutes and expert analysis. It reflects on opportunities and limitations and outlines risks. The explicit purpose of this report is to distil the most important insights to be considered in the design of a Conflict Early Warning System for public organisations.
The review presented in this report finds a thriving EWEA community both inside and outside governments that offers many practices and principles in existing EW that can be used in the development of an EW that is instrumental in achieving these goals. Key findings of this report are:
- Effective EW and EA are embedded in a wider system and encompass more than mere technological solutions.
- At the same time, new data, methods and tools can effectively contribute to this endeavour.
- Organisations setting up a CEWS will benefit from private sector best practices to ensure continuous innovation and organisational take up.
- An effective CEWS offers monitoring, prediction, and explanation.
- A CEWS should not be perceived as a panacea for all purposes nor as a crystal ball.
- A CEWS offers many opportunities, such as facilitate the alignment of policy efforts within government, across government and with additional stakeholders.
- CEWS also comes with risks that need to be considered and addressed, such as: warnings based on inaccurate information; violations of data privacy; exclusion of local views and failure to understand local circumstances.