When Every Input Is an Edge Case: An Adaptive Data Platform
For insurance bordereaux, the structure is often a moving target. Data may be spread across files or worksheets, with headers, summaries, and business information represented inconsistently, making it difficult to determine what should be treated as data and how it should be interpreted.
For our client, handling each variation through format-specific logic resulted in more than 200 R scripts. Frequent changes in source formats increased the maintenance burden of this model, ultimately causing onboarding to stall.
This talk presents the ongoing redesign of that approach into a scalable, configuration-driven data platform. Treating source variability as a design assumption, the platform combines reusable processing capabilities, configurable rules, controlled schema evolution, and a layered architecture to transform heterogeneous inputs into consistent, analysis-ready structures. It also explores a pragmatic balance between deterministic automation and AI: rules handle predictable decisions, while AI is used selectively where semantic ambiguity makes fixed logic less effective. [Human review, traceability, logging, and observability support responsible AI use.]
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