Data Quality & Validation
Independent checks on fraud, duplication, speeding, and consistency so your final dataset is fit for decision-making.
300+
Datasets Validated
Data quality checks that protect your decisions before results are shared.
We review survey data with a dedicated quality lens – identifying fraud, duplication, speeding, inconsistent responses, and weak open‑ends – so low‑quality records are removed or flagged before analysis begins.
Our checks are independent of supplier QC and are documented clearly, giving you confidence in how the dataset was validated.
What Our Data Quality & Validation Covers
We work across single‑market and multi‑market studies, applying consistent rules while allowing for study‑specific nuances.
Fraud, duplication, and device/IP hygiene checks- Speeding and straight‑lining detection
- Logic and consistency checks on key variables
- Open‑end relevance and richness review
- Summary of exclusions, rules, and impact on base sizes
- Flags or variables to support further diagnostics
Why Independent Validation Matters
By applying clear rules and documenting every step, we reduce debates about “what was removed” and help stakeholders trust the final numbers. The same framework can be reused across waves, improving comparability over time.
25+
Quality Rules Library
90%
Projects with Documented Exclusions
24hr
Typical Validation Turnaround
How We Approach Data Quality
We agree upfront on thresholds and rules – for example, acceptable completion times or open‑end length – so quality decisions are aligned with your risk tolerance.
During validation we create diagnostic views that highlight where issues cluster, such as specific sources, devices, or geographies, helping you refine future designs and supplier choices.
FAQ
Questions about data quality & validation
Can we customize the quality rules you apply?
Yes. We have a base library of rules, but we can adjust thresholds or add study‑specific checks based on your needs.
Do you share which respondents were removed?
We can provide flags or separate lists of removed respondents, along with the reasons, so you can review or replicate decisions.
At what stage do you prefer to run validation?
Ideally, we run interim checks during fieldwork and a final pass after closure, so issues can be addressed early where possible.
Can you validate data collected by another vendor?
Yes. We can act as an independent quality layer on top of data delivered by other suppliers, as long as we have access to the necessary fields.
Our Service
Data Quality & Validation
We apply dedicated quality checks to survey data to identify and remove low-quality responses before delivery. Our process covers fraud detection, duplication, speeding, inconsistent answers, and open-end review.
Every exclusion is documented, with clear criteria and counts, so you know exactly how the dataset was cleaned and why specific records were removed or flagged.