Operations & Data

Measures consumer trust, noise tolerance, adoption likelihood, and barriers for retail drone delivery. Designed for market researchers evaluating residential readiness for aerial last-mile logistics.

Captures stakeholder expectations for data product availability, freshness, and quality to inform internal SLO/SLA definitions. Designed for data consumers across engineering, analytics, and business teams.

An internal assessment for measuring employees' data literacy, self-service analytics confidence, tool adoption, and support needs — designed to surface skill gaps, trust issues, and barriers that inform data-enablement strategy.

Measures data practitioners' confidence in lineage accuracy, impact analysis efficiency, and tooling gaps. Designed for data engineering, analytics, and platform teams to identify high-priority improvements to lineage infrastructure and workflows.

Diagnoses catalog discoverability, ownership clarity, and data trust across teams. Designed for internal data practitioners to identify governance gaps and prioritize catalog improvements using NPS and behavioral metrics.

Measures how easily analytics teams can find, evaluate, and trust data assets across the organization, revealing gaps in metadata, documentation, and lineage practices. Designed for data and analytics professionals; estimated 6–8 minutes.

Measures customer perceptions of clarity, timeliness, and trust in crisis and outage communications. Designed for B2B operations teams seeking to benchmark and improve incident response communication.

Measures adoption rates, usability barriers, perceived safety outcomes, and data trust related to IoT wearables and site sensors among construction workers. Designed for safety managers and operations teams evaluating wearable technology programs on active job sites.

Measures employee-perceived usability, reliability, and efficiency of internal tools and workflows. Use with cross-functional teams to identify friction points and prioritize improvements.