It’s been a couple weeks since the Insights Association’s Corporate Researchers Conference (CRC), and congratulations are due to the organizers for hosting another fantastic event. We heard from important voices in the industry and learned about trends impacting everyone—from tech partners and sampling companies to agencies and brands.

Not surprisingly, data quality remains a critical topic. But today's data quality threats are evolving too fast for old defensive strategies. For brand researchers, the complexity of tech-enabled fraud means blindly trusting partners is no longer enough; you must actively engage and demand transparency and rigorous quality control.

Poor quality data isn’t only about fraud

Fraud is a major factor, but it's not the only one. Critical decisions about sourcing, field management, methodology selection, and survey design also contribute to the issue. While these issues are potentially more manageable because they're more obvious, the difficulty now lies in enhanced tech-enabled fraud originating from hidden or less obvious sources. This includes issues connected to both AI and sampling technology.

The result? We are seeing more and different quality issues than ever before

I participated on a panel at the CRC alongside Kristen Deevers (American Greetings) and Ronda Slaven (Synchrony Financial) to review these issues and talk about their relevance to brand researchers. The central question for brand researchers today is: "With new data quality concerns at play, what do we need to be thinking about to ensure our projects are covered?"

A robust, layered approach to quality control and fraud detection is absolutely essential. As an agency, we’re intimately involved throughout the process, watching threats and reacting in real time, but brand researchers don’t have the time to scrutinize every piece of data for their projects. So how can we bridge the gaps to provide confidence to brand researchers that quality is being effectively managed?

Brand Researchers: The New Quality Control Playbook

During our panel discussion, Kristen and Ronda shared a couple of actionable ideas that brand researchers can implement immediately:

• Implement a Partner Checklist: Kristen suggested having a checklist of questions focused on process and fraud detection to ask partners. If a partner hesitates or provides less than transparent responses, this should be a major red flag.
• Demand Visual Verification in Qual: Ronda mentioned her requirements for B2B qualitative research, which include having participants on camera for the entirety of every interview. If a participant goes off camera or can’t be seen, they are immediately removed from the interview.

These are both great suggestions for brands, but the issues impacting data quality are complex and often hard to see on the surface. The new mandate for brand researchers is not just to trust, but to verify that critical steps impacting data quality are being managed.

Four Ways to Verify Your Partners' Quality Control

Here are four steps you can take today to verify the quality control of your partners:

1. Demand Sourcing Transparency: From the beginning, ask questions of your partners and keep asking until you’re satisfied with the responses. You have the right to know where your sample is coming from and to make decisions based on your study specifications. If a partner isn’t transparent with sourcing or sampling decisions, insist on it—and refuse to work with them if they don’t provide this information.
   
2. Request Full Documentation and Reporting: Request documentation of the agency’s quality procedures and ask for reports. These reports should show the amount of sample being removed at various stages of the research. This may include both internal QC summaries as well as third-party fraud detection information.
   
3. Utilize a Standardized Checklist: As Kristen shared, use a checklist to ensure all your requirements for quality are in place and to align with the partner on standards.
   
4. Debrief and Collaborate on Solutions: Data quality isn’t a single-issue concern—we all have a part to play. From survey design to sampling and field management, debrief with your partners. Talk about what worked and what didn't with respect to data quality and continue to evolve your own standards.

Data quality is now a shared, evolving responsibility. Start by leveraging the resources from groups like the Insights Association and the GDQ (Global Data Quality) Initiative to align on best practices.

Resources for Aligning on Best Practices:

Insights Association (IA): https://www.insightsassociation.org/

GDQ Initiative: https://www.globaldataquality.org/