Technical report presents a client-side data normalization engine for enhanced data hygiene and real-time analytics in enterprise settings, implying reduced operational expenses.
Fragmented proprietary data schemas introduce operational friction, contribute to high failure rates and excessive operational expenses (OpEx) in enterprise AI/ML initiatives, and accumulate technical data debt across distributed systems. Traditional data normalization frameworks rely on centralized, server-side data warehouses that introduce significant storage liabilities, require prolonged procurement cycles, and perform poorly in resource-constrained thin-client environments. This technical report presents a domain-agnostic, client-side data normalization engine. By executing data transformation strictly within volatile browser Random Access Memory (RAM), utilizing off-main-thread Web Workers, and crowdsourcing canonical alias mappings at the point of operational entry, the architecture achieves a zero-server-storage footprint while producing standardized canonical crosswalks and low-latency visual analytics for real-time decision support. Document ID: MMHT-TR-2026-001 Publisher: MetisMap Health Technologies LLC Author: Stephanie Regalado
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Stephanie Regalado (2026) studied this question.
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