Data Quality

Quality at every stage of research.

Our four-layer defense system and continuous monitoring protect outcome reliability, reduce invalid responses and ensure clients get data they can trust.

Quality framework

What we aim for.

Every study we support is held to clear, measurable quality standards.

High-quality, valid responses
Consistent fraud detection across all studies
Rapid identification and removal of suspicious activity
Data quality scoring above industry benchmarks

Industry benchmarks we track

High detection rateFraud detection target set above 96%

Low incidenceFraud rate target below 0.8%

Rapid responseDetection within minutes during fieldwork

Strong quality scoresData quality target at or above 92%

Methodology

Four-layer defense architecture

Quality controls are applied at every point — from registration through final data delivery — so problems are caught early, not after results go out.

01 / 04

Entry

Registration verification

Preventing bad data before it enters the system. Every participant is screened at point of entry.

IP reputation screening against known fraudulent ranges
Device fingerprinting for duplicate device detection
Email domain validation and disposable email filtering
CAPTCHA and bot-prevention controls (reCAPTCHA v3 / hCaptcha)
Rate limiting per IP, device and email domain
Geolocation verification

02 / 04

Profile

Profile validation

Ensuring participant profiles are accurate, internally consistent and sufficiently complete for quality research.

Logic-check validation questions
Demographic consistency cross-checks (age/gender/location)
Profile completeness requirements (minimum 80% fields)
Completion-time validation against floor thresholds
Periodic re-validation — random subset checks every 90 days
Profile quality scoring: 0–100 with tiered access

03 / 04

Fieldwork

Survey-time quality controls

Real-time monitoring during fieldwork to detect and respond to low-quality behaviour as it happens.

Embedded attention checks (2–3 per survey) with automatic disqualification on failure
Straight-lining detection (same answer selected >80% of time)
Speeding detection (completion time <50% of survey average)
Open-end quality checks (minimum length and uniqueness)
Real-time quality scoring — composite of attention, time, consistency and open-end quality
Automatic flagging for manual review below quality thresholds

04 / 04

Post-survey

Post-survey analysis

Final quality review before data leaves our side — automated cleaning supplemented by human judgment.

Statistical outlier detection
Cross-validation against historical response patterns
Duplicate detection via device fingerprint matching
Machine-learning pattern recognition on response sequences
Manual review queue for flagged respondents — SLA within 4 hours for system-flagged cases
Continuous feedback loop: detected fraud patterns feed back into earlier layers

Our view

Why quality matters more than volume.

It's an industry cliché to say "quality data." But if your data quality is unreliable, nothing downstream holds: not brand tracking, not consumer insights, not concept test results.

Our approach is layered, operational and transparent. We don't pretend to have perfect data, but we do have clear controls that make it significantly better on average — and we are honest about that.

Improvement cycle

How we keep getting better

DailyAutomated monitoring with same-day response to quality anomalies

WeeklyQuality team review of trends, policy adjustments and training updates

MonthlyComprehensive system audit and supplier performance evaluation

QuarterlyExternal validation and industry benchmarking to track progress

Need quality support?

Need data-quality controls for your own surveys?

We can integrate quality monitoring into existing research workflows — let's talk.