The Challenge

In the digital age, security professionals and analysts have an abundance of publicly available information (PAI) at their fingertips. While traditional PAI can be a valuable resource, it is important to recognize its limitations and adopt solutions to close those gaps. Relying too heavily on PAI, such as open-source databases and monitoring of news sources, social media, and other online platforms, comes with notable limitations, including:

• Lack of depth and breadth resulting in an incomplete view of population characteristics that influence global and localized security environments.
• Lack of the reliability required for sound analysis in the form of inaccuracies, biases, and misinformation.
• Lack of localized context and nuance in behaviors, sentiments, livelihoods, and other population characteristics that vary sub-nationally.
• Inability to provide representative, measured changes in behaviors and sentiments over time.

Fraym Solution

Fraym data addresses these limitations by delivering comprehensive, reliable, and spatially precise insight into global population dynamics. Fraym’s novel technology allows users to zoom in on any community across the globe and know what populations look like, what they think, and how they behave. This insight enables improved localized risk assessment and enhances analytical products in support of security operations.

In contrast with traditional PAI, Fraym data is:

1. Comprehensive: Fraym produces hundreds of unique indicators on perceptions, attitudes, livelihoods, behaviors and more, for any country of interest.
2. Highly reliable: Fraym data is based on scientifically sampled household survey data points, not open-source or web-scraped data and delivers representative, measured changes in behaviors and sentiments over time.
3. Standardized and Spatially precise: Fraym uses machine learning (ML) to produce data at the neighborhood level to deliver nuanced insights with unprecedented granularity.

For example, Fraym modeled the probability of experiencing armed violence in Burkina Faso down to the neighborhood-level, across the entire country. In validating this layer, 92% of future attacks occurred within predicted high-risk areas. Anticipating risk with this high level of reliability and spatial precision is impossible without Fraym’s foundational population data and cannot be achieved by monitoring social media trends or relying on traditional PAI.

Conclusion

Fraym data addresses key limitations of traditional PAI by delivering comprehensive, reliable, and geographically precise insight into global population dynamics. With Fraym’s foundational population data in hand, users can improve risk assessment and decision making for security operations anywhere in the world, including fragile or hard to reach places.