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042025OperationsAnalyticsWorkflow Design

Participant Data Collection Redesign

Redesigned a participant data workflow by reducing inputs to core KPIs and implementing phased collection for better usability and cleaner analysis.

October 2025

Observation

Input Fatigue & Data Integrity

The onboarding workflow required too much information in one session and produced a 40% drop-off before completion. Input inconsistency also increased reconciliation effort to about 15 staff-hours per week.

Hypothesis

Progressive Profiling

Data quality can be improved through collection architecture. By shifting from a front-loaded intake to a progressive model, we can reduce early friction while improving record validity at each stage.

Experiment

The Low-Code Stack

A modular low-code workflow was implemented using Airtable, Typeform, and Zapier to test maintainability and data quality under real operating conditions:

  1. Phased Logic: Split intake into Eligibility (entry), Logistics (post-acceptance), and Feedback (post-program).
  2. Strict Typing: Implemented regex validation for all inputs to reject malformed entries immediately.
  3. State Automation: Auto-tagging record status (Applied → Active) to remove manual data entry.

Findings

The Delta

  • Completion rate: 60% → 92%.
  • Manual reconciliation: 15h → 2h / week.
  • Reporting readiness: 100% of records are structured for automated reporting.