Data validation that helps turn raw responses into reliable research evidence
Market Intelligence Unit helps clients review, clean and validate survey data so analysis is based on responses that are more reliable, consistent and usable.
Clean data is the foundation of credible research
Market Intelligence Unit helps clients review, clean and validate survey data so analysis is based on responses that are more reliable, consistent and usable.
Validation support when you need it
Our validation support can be used during fieldwork, after fieldwork or before final analysis and reporting.
We help identify data quality concerns early, reduce avoidable reporting risks and give your team a clearer view of the dataset before analysis begins.
Structured quality checks for better research data
Our checks are designed to identify and reduce low-quality responses, helping improve the reliability of the final dataset.
Duplicate Checks
Review duplicate responses, respondent IDs and repeated records that may affect dataset reliability.
Speeding Review
Check completion times and identify responses completed too quickly for the survey length and complexity.
Straightlining Detection
Review patterned responses, repeated scale selections and low-effort matrix behavior.
Attention Checks
Review trap questions, attention checks and logic consistency across key survey paths.
Open-end Quality
Check for gibberish, irrelevant text, duplicated answers, AI-like patterns or poor-quality comments.
Geo & Device Review
Review geo, device, IP or source information when available and appropriate for the project.
Quota Consistency
Review sample consistency, quota distribution and audience alignment against project requirements.
Cleaning Notes
Prepare validation notes and a transparent summary of what was checked, flagged and cleaned.
A practical process from raw responses to cleaner analysis files
We use a practical validation workflow that makes the cleaning process structured, transparent and easier to review.
Define Rules
We confirm quality criteria, validation checks and removal or flagging rules with the client.
Review Data
We review responses against agreed checks such as duplicates, speeders, straightliners and open-ends.
Flag Records
We identify questionable records and separate them for review based on the validation criteria.
Clean or Mark
We remove, mark or retain records based on agreed instructions and project requirements.
Summarize
We provide a transparent summary of what was checked, flagged and adjusted.
Quality checks without overpromising
We do not claim that any research process can guarantee completely fraud-free data. Instead, we apply structured checks to identify and reduce low-quality responses, helping improve the reliability of the final dataset.
- Validation criteria are defined before cleaning begins.
- Questionable records are flagged for clear review.
- Cleaning decisions can be aligned with client instructions.
- Final outputs are supported by validation notes and summary checks.
- Analysis and reporting are based on cleaner input data.
What you can receive after validation
Deliverables can be tailored based on the project stage, dataset condition and reporting requirements.
Flagged Dataset
A dataset with quality flags showing which records were reviewed, flagged, removed or retained.
Cleaned Analysis File
A cleaner dataset prepared for crosstabs, dashboards, reporting or further statistical analysis.
Validation Summary
A practical summary showing the checks performed, issues identified and cleaning decisions made.
Need to improve survey data quality?
Share your dataset, questionnaire, quality rules and project context. We will review the requirement and recommend a practical validation approach.
Tell us about your data quality requirement
Share your project details and we will review your validation needs, dataset status and reporting requirements.
What happens next?
Once we receive your enquiry, we review the dataset stage, questionnaire, known quality concerns and required outputs before recommending the right validation approach.
- ✓ Dataset and questionnaire review
- ✓ Quality rule recommendation
- ✓ Flagging and cleaning approach
- ✓ Validation summary and delivery plan