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Event Sourcing: Contextual AI Requires a Contextual Data Model

Most business applications store only current state, losing the historical context needed for effective AI. Event sourcing addresses this by capturing every change as an immutable event in an append-only log, creating a 'black box' for business operations. This rich event history enables AI use cases like customer journey prediction, dynamic pricing, churn modeling, and demand forecasting. A real-world case study shows a German manufacturer used event-sourced data to train a cost estimation model achieving 91% accuracy with 15,000 historical data points. The post promotes Kurrent as a purpose-built event sourcing platform.

    #ai#machine-learning#architecture
Aug 04•7m read time•From kurrent.io
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The Black Box Your Business Doesn't HaveTwo Ways to Model Data: State vs. Event-based data modelPowering AI with Your Business Black BoxCase Study: Predicting Manufacturing Costs with 91% AccuracyBuild Your Business Black Box
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Event Store

EventStore specializes in event sourcing and stream processing, offering a robust platform for build...

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