How We Approach Data Quality at Behaviorally
At Ignite, one shift was consistent across every conversation.
Data quality is no longer something managed at fieldwork. It is becoming a system-level requirement, driven by AI risk, organized fraud, and rising expectations for transparency.
One main idea is clear, quality is no longer about cleaning data, it is about controlling how it is created.
1. AI introduces hidden quality risks
AI is powerful, but it introduces new, less visible failure points.
Outputs can appear valid but be incorrect. Automation can mask issues instead of exposing them. Traceability is often lost.
This revealed that quality risk is no longer visible in the output. It sits inside the system. What matters now is not just what AI produces, but how it produces it. Without control, scale amplifies error.
2. Fraud Is Organized and Scaling
Fraud is no longer an isolated behavior.
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It is incentive-driven,
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Coordinated across networks, and
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Increasing with AI and automation.
This changes where the problem lives. What this revealed is that fraud is no longer something you remove at the survey level. It is something you prevent at the system level. If bad actors enter, quality is already compromised.
3. Transparency is no longer optional
There is a clear shift toward traceability across the data supply chain. Clients expect proof, not explanation. Quality must be transparent and auditable. Sourcing and validation must be documented. Data quality is becoming a credibility standard.
At the end of the day, it is no longer enough to say the data is good. You have to prove it.
4. Fraud Prevention is moving Upstream
Fraud prevention is shifting from detection when and during it happens to prevention where it detects before the event happens.
Verification is happening earlier through device checks, identity validation, behavioral signals, and real-time risk scoring. This is a structural change.
Post-field cleaning is reactive. Prevention is now the baseline.
5. How You Collect Data Determines Who Shows Up
Requiring respondents to speak or show their faces significantly reduces fraudulent participation. Fraud scales in environments where identity is easy to mask and participation is frictionless. When respondents have to show up, the dynamic changes.
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Visibility increases the cost of participation.
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It makes automation harder.
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It limits the ability to scale coordinated fraud.
Methodology becomes more than a way to collect data, it becomes a control mechanism.
6. Design Determines Data Quality
Poor survey design leads to poor data. Engagement and experience directly impact response quality.
The takeaway from Insights Association Ignite are clear:
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Increase engagement and sustained attention
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Reduce respondent fatigue and satisficing
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Capture more in-the-moment, unaided responses
The results are higher quality data. Better experiences produce better data. When people engage more naturally, they respond more truthfully.
The implication for the industry is moving towards proven, controlled, and auditable data quality systems.
Contact us today to design data quality into your system from the start.
THE AUTHOR
Anne Martin is the Vice President of Field Operations at Behaviorally, where she focuses on supplier partnerships and the quality of research delivery across markets. Her specialty is building strong relationships, setting clear expectations, and supporting quality controls that lead to reliable shopper insights. Anne brings a practical operations lens to the sourcing, monitoring, and delivery, with a focus on sample quality and consistency.