When Metrics Meet Practice

Abstract:
Modern analytics systems measure speed, throughput, and productivity with great precision, yet they often overlook the human experience that surrounds those metrics. Cognitive load, clarity, ease of interpretation, and reflective decision-making remain largely unmeasured dimensions.

This paper explores a conceptual framework for privacy-preserving behavioral analytics that focuses on how people experience data, not just how systems process it.

The work models ease, clarity, and contextual pressure through directional behavioral indicators using statically defined synthetic data. No human-subject data was collected, processed, inferred, or classified. The goal is to demonstrate how architectural choices, local-first processing, automatic expiry, and lightweight interpretability manifests, can support more intentional and autonomy-preserving analytics.