Quantifying ‘Good’ Data Quality: Key Takeaways for Research Practitioners and Organizations

Guest Author: Melanie Courtright, Chief Executive Officer, Insights Association

In the world of insights and analytics, we often hear the phrase, “Data is the lifeblood of our profession.” While this notion is widely accepted, the reality is that the data flowing through our industry is becoming increasingly compromised and many are frustratedly uncertain about how to enhance its integrity. One of the key challenges is that data quality issues often appear nebulous, making it difficult to identify the levers that can trigger meaningful improvements.

Shedding light on the source of these problems and how they may be mitigated was the goal of the Insights Association’s Data Quality Benchmarking initiative.

The recently released report, Quantifying “Good” Data Quality provides highlights from the Base Wave of this initiative. By introducing eight key benchmarks for online research data quality, this study provides a foundational framework for assessing and improving data quality across industries.

This article highlights the report’s key findings and offers actionable recommendations for research practitioners and organizations to enhance their data quality practices.

The Need for Data Quality Standards

As businesses increasingly leverage artificial intelligence, automation, and advanced analytics, the accuracy, reliability, and consistency of data become more critical than ever. Poor data quality can lead to misguided business decisions, inefficiencies, and reputational risks. This benchmarking initiative seeks to establish clear, quantifiable standards that companies can use to assess and improve their data quality practices.

Key Data Quality Benchmarks

The study introduces eight core benchmarks that provide a comprehensive view of data quality in online research:

1. Abandon Rate – The percentage of respondents who start but do not complete a survey. High abandonment rates may indicate issues with survey length, engagement, or complexity.
2. Device Types Used – Identifies the devices respondents use to take surveys, helping ensure compatibility and accessibility.
3. In-Survey Cleanout Rate – The percentage of responses removed during a survey due to inconsistencies or poor-quality responses.
4. Incidence Rate – The proportion of eligible respondents who qualify for a survey based on screening criteria.
5. Length of Interview (LOI) – Measures the time it takes to complete a survey, influencing engagement and data quality.
6. Post-Survey Cleanout Rate – The percentage of responses removed after survey completion due to quality concerns.
7. Pre-Survey Removal Rates – The percentage of potential respondents removed before starting the survey due to disqualifications or suspicious activity.
8. Use of Link Encryption – Ensuring secure transmission of survey links to protect data integrity.

These benchmarks serve as a reference for organizations to gauge their data quality performance and identify areas for improvement.

Implications for Research Practitioners and Organizations

1. Reducing Data Fraud and Enhancing Security

One of the study’s key recommendations is reducing the need for data removal by addressing fraudulent responses. Organizations should leverage technology such as AI-driven fraud detection tools, digital fingerprinting, and behavioral analytics to identify and eliminate fraudulent respondents early in the data collection process. Additionally, increasing the use of link encryption can safeguard survey integrity and protect respondent data from unauthorized access.

2. Optimizing Survey Design for Better Engagement

A high abandon rate often signals issues with survey design. Long, complex, or redundant surveys can lead to respondent fatigue, resulting in incomplete or low-quality responses. Research practitioners should aim to:

• Keep surveys concise and engaging.
• Use dynamic routing to tailor questions based on previous responses.
• Ensure mobile friendliness, given the variety of devices used by respondents.
• Pre-test surveys to identify potential drop-off points before launch.

3. Enhancing Data Cleaning and Validation Processes

The study highlights the importance of robust data cleaning procedures, including both in-survey and post-survey cleanout rates. Companies should implement:

• Automated quality checks to detect inconsistent or illogical responses in real-time.
• Post-survey data validation techniques, such as duplicate response detection and AI-based anomaly detection.
• Clear documentation of data cleaning protocols to maintain consistency.

4. Monitoring Industry Benchmarks and Continuous Improvement

Organizations should not view these benchmarks as static but rather as evolving indicators of industry-wide data quality trends. Regularly benchmarking survey data against these norms allows firms to:

• Identify shifts in data quality over time.
• Compare performance against industry peers.
• Make informed adjustments to improve research methodologies.

5. Encouraging Greater Industry Collaboration

The report emphasizes the need for more firms to contribute data, especially in specialized areas like B2B and healthcare research. Increased participation in benchmarking initiatives will enhance the representativeness and utility of industry-wide standards. Organizations are encouraged to:

• Share anonymized data with industry groups to contribute to broader data quality benchmarks.
• Engage in professional forums and councils, such as the Council for Data Integrity, to stay informed on best practices and emerging trends.

Looking Ahead: Future of Data Quality Benchmarking

This report is the first wave of an ongoing tracking study, with future waves scheduled biannually. Over time, these benchmarks will become more refined and establish clearer normative data for what constitutes “good” data quality. The Global Data Quality Initiative aims to expand data collection tools and extend this research to international markets, further solidifying these benchmarks as industry standards.

For research practitioners and organizations committed to improving data quality, participating in upcoming benchmarking waves and engaging in industry discussions will be instrumental in driving meaningful progress.

Conclusion High-quality data is the bedrock of effective research, strategic decision-making, and business success. The Data Quality Benchmarking Initiative provides essential benchmarks that empower organizations to assess, compare, and enhance their data quality practices. By focusing on fraud prevention, optimizing survey design, refining data cleaning processes, and continuously monitoring benchmarks, organizations can build a stronger foundation for trustworthy insights.

To learn more about the Data Quality Benchmarking Initiative, participate in an upcoming wave, or join the conversation, visit the Insights Association’s Data Quality Standards webpage.