Identity resolution without the nightly batch

Identity graphs are fundamental to advertising platforms, supporting functions like cross-screen frequency capping and attribution from exposure to purchase, but their effectiveness is hampered by outdated batch processing methods. These graphs, which integrate device signals and identifiers, traditionally rely on a nightly rebuild schedule that fails to keep pace with real-time data demands, leading to inaccuracies in targeting and attribution. The delay between data changes and graph updates can result in compliance issues, especially under regulations like GDPR, which require prompt processing of consent withdrawals. The inherent circularity in match rate metrics, measured against the existing graph, masks these staleness issues, while increasing processing demands further strain the system as data volume grows. The shift to continuous resolution, where signals are incrementally integrated into a live graph, offers a solution by ensuring timely updates, enhancing accuracy, and compliance. This approach allows for real-time decision-making, reducing the limitations imposed by nightly batches and improving the platform's ability to handle transactional updates and analytical tasks.

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