Automated Data Cleaning
Automated Data Cleaning
Data quality in market research is critical to ensuring reliable insights, yet our manual data cleaning process was time-consuming (100 hours/month) and inconsistent across teams. Some relied on an outdated internal tool, but it only flagged poor-quality responses rather than removing them, still requiring manual intervention. This inefficiency led to inconsistent data quality and high labor costs.
Problem
Market research data often contained low-quality responses that needed to be identified and removed to ensure reliable insights. Some respondents completed surveys too quickly to have read the questions properly, while others selected the same response repeatedly, indicating disengagement. Additionally, open-text responses sometimes contained copied content, irrelevant text, or nonsense entries.
Our existing process had several key issues:
Time inefficiency – Cleaning required senior staff involvement, making it a costly and low-value task.
Inconsistent standards – Different teams applied different rules, leading to data discrepancies.
Manual review bottleneck – Even with an internal tool, final decisions required manual validation, slowing down projects.
Data fragmentation – Local working files made it difficult to track which records were removed, leading to misreporting.
Lack of trust in automation – Researchers and operations teams were hesitant to rely fully on automated checks, often manually reviewing flagged respondents.
Challenge
We needed to automate data cleaning while maintaining control over key decision points. The solution had to:
Reduce time spent on cleaning while keeping high data quality.
Allow junior staff to manage the process without extensive training.
Create a single source of truth by centralizing cleaned data.
Avoid unnecessary removal of legitimate respondents while ensuring poor-quality data was eliminated.
Balance the trade-off between data quality and sample costs.
Build trust among teams by improving transparency around flagged respondents and allowing better reporting.
Approach
To design an effective solution, we:
1️⃣ Conducted user research – Held interviews and feedback sessions with research teams and operations to understand their workflow, key frustrations, and desired improvements.
2️⃣ Evaluated the existing tool – Reviewed the internally built tool with its creator to identify gaps, limitations, and opportunities for enhancement.
3️⃣ Analysed best practices – Studied academic research and industry standards on detecting poor survey responses, such as inconsistent answering patterns, copied text, and completion speeds that indicate disengagement.
4️⃣ Developed a flagging score methodology – Created a scoring system that assigns numerical values to various response issues, allowing more statistical approaches to determine appropriate cut-off points for automated cleaning and manual review.
5️⃣ Tested prototypes – Developed initial versions of the automated solution and tested them on real projects to validate effectiveness and refine user experience.
Solution Design
⚙️ Automated data cleaning with a scoring system – Introduced a weighted scoring system that combines multiple quality indicators (e.g., speeders, gibberish detection, flatliners, expletives) to determine response validity.
📂 Database archiving for transparency – Rather than deleting flagged responses permanently, we stored them centrally for easy retrieval and auditing, eliminating fragmented local files.
✅ Flexible review process – Provided researchers with an optional final validation report to maintain oversight while reducing manual effort.
📊 Better integration with fieldwork tools – Improved the alignment between automated checks and manual review processes to enhance adoption and trust.
🔍 Improved detection of poor-quality responses – Enhanced gibberish detection, added pattern recognition for flatliners, and refined speed thresholds to minimize the risk of false positives.
📝 Enhanced reporting for respondent removal – Allowed teams to specify the reason for removal and improved exports for communication with panel providers.
🌎 Better support for multilingual responses – Addressed language discrepancies in gibberish detection and open-text validation.
Result
Reflection
✅ 70% reduction in manual cleaning time – Monthly effort dropped from 100 to 30 hours.
✅ Higher quality insights – Standardized cleaning rules ensured data consistency.
✅ Empowered junior staff – Routine cleaning tasks were shifted from senior to junior researchers.
✅ Improved adoption – Gradual rollout and user control increased buy-in.
✅ More effective data review – Introduced statistical approaches for identifying outliers, making quality decisions more data-driven.
✅ Trust built through transparency – Improved reporting and review functionality led to increased confidence in automation.
While the project delivered significant efficiency gains, it also highlighted the importance of holistic problem-solving. The initial implementation was too aggressive in removing low-quality responses, leading to unforeseen cost implications due to increased panel recruitment expenses. This underscored the need to evaluate not just the technical solution but also the business impact before scaling changes.
One of the project's key strengths was the strong collaboration between product management, UX, operations, and developers. By working closely together, we were able to design an intuitive and effective solution that balanced automation with usability. The partnership ensured that technical feasibility, user experience, and business needs were aligned from the outset.
Additionally, user adoption proved to be a challenge. While we built a strong technical solution, we could have engaged users earlier in the process to ensure smoother transitions. A phased approach, beginning with a reporting tool before moving to full automation, might have increased comfort and adoption rates.
These insights have shaped my approach to product development—ensuring deeper stakeholder engagement, balancing automation with flexibility, and considering financial implications as part of the solution design from the outset.
On the more positive side, the collaboration between UX, product management and developers is something I emphasise in every project I have run since.