"The quest for data quality continues, and we must be relentless in our efforts to achieve it." Our Group Services Director, Horst Feldhaeuser, writes for Research Live about the persistent issue of data quality, in his most recent article entitled "Five ways to pursue better data quality in your market research practice." In the piece, he covers the current quality landscape and gives some practical advice for those looking to elevate the accuracy, verifiability, and quality of their insights.
Here's a summary of the key points covered in the article:
Use the Right Terminology: Employing accurate and transparent terminology is crucial in discussing data quality issues. By aligning language across the industry, we can minimize disconnects and accelerate progress. Keeping abreast of the work being done by leadership bodies in this area is essential.
Battle Survey Fraud: Combatting survey fraud requires a multi-faceted approach that includes both basic and advanced techniques. Investing in technology and human expertise to detect and prevent fraudulent activities is paramount for long-term success.
Reduce Bias and Increase Representativity: Addressing bias in research, particularly with the increasing use of AI, requires intentional efforts in survey design and sample selection. The Market Research Society's Representation in Research Group recommends expanding profiling points for participants to improve representativeness.
Improve Respondent Experience and Engagement: Enhancing interactions between researchers and survey participants is crucial for obtaining accurate and meaningful feedback. Practices such as thoughtful survey design, proper rewards, and privacy protection contribute to positive respondent experiences.
Implement Data Quality Checks Throughout the Process: Employing real-time monitoring and validation checks during data collection helps identify and address errors early on. Ensuring quality data inputs is essential for delivering reliable insights during the analysis and reporting stages.
In addition to these strategies, Horst emphasizes the importance of maintaining a human element across processes is emphasized. He closes his article with: "The human feedback loop can help organisations pick up on certain elements that just feel wrong. Our intuition, while it’s not always foolproof, is really difficult for AI and technology to replicate, and is something that should not be overlooked."