Navigating through data quality challenges in market research: exploring the roadblocks to reliable insights and actionable data In market research, data cleaning and validation are industry standards. Similarly, fraud detection is also actioned by most of the market research companies through various platforms that track location, IP address, device, proxies, etc. However, ensuring the authenticity and genuineness of the collected data still remains a challenge. To explore this issue, we conducted a study across 18 countries, comprising 103 market research companies and independent consultants, involving 117 participants. These participants included 37% top and senior-level management, 32% market research consultants, and 31% middle-level management. The study highlighted that 78% of market research professionals encounter data quality challenges, despite using various techniques. Issues arise from bias (36%), speeders (37%), outliers (31%), mono answers (21%), junk/bad data (27%), data inconsistency (26%), and data discrepancy (18%). These challenges hinder the accuracy of insights. Industry experts have been meticulously working to overcome these challenges. After extensive efforts, we developed a solution using a statistical perspective to improve data quality: the Data Quality Score Module. It comprises 6 parameters: outliers, speeders, mono answers, junk/bad data, data inconsistency, and data discrepancy, including fraud detection. This acts as a bridge between data collection and data processing by flagging the poor-quality data at the respondent level, which helps researchers distinguish between authentic and unreliable responses. Let’s work together in overcoming the existing data quality challenges and move towards bias-free data. #MarketResearch #DataQuality #ResearchInsights #SurveyResults #DataChallenges #SamplingMethods #DataIntegrity #ResearchConsulting #IndustryTrends #ProfessionalInsights #DataManagement #BusinessResearch #QualityData #MarketAnalysis #ResearchProfessionals
Ayush Upadhyaya
1mo
I'm grateful for the opportunity to work on this innovative project from the ground up. The Data Quality Score Module is a game-changer, offering significant improvements in data authenticity and reliability for the market research industry.
CertusFood ERP
1mo
Fascinating insights into the persistent data quality challenges in market research! The Data Quality Score Module seems promising. Eager to learn more about its practical implementation and potential impact on industry standards. Let's collaborate on driving data accuracy and reliability!
Jayesh Bhilkar
1mo
Congratulations for the new and innovative phase for the data analysis and research quality part !! Keep growing and will look forward to the work experience for growing error free data 👍
Pradyumna Parida
1mo
Working on this tool was a significant endeavor for our team. With proper guidance and mentorship, we succeeded in creating a module that helps researchers identify data quality gaps. Proud of our collective effort!
The Data Quality Score Module sounds like a game-changer for tackling challenges in market research industry. Addressing issues like bias and data inconsistency at the respondent level is crucial for reliable insights. Excited to see its impact on achieving bias-free data!
Rama Pathak
1mo
Great development!! It will make data analysis more transparent by identifying and correcting errors, leading to more reliable insights and will automate the data quality assessment process, saving time and resources that would otherwise be spent on manual data checks.
Maintaining data quality is a science. You have to be methodical. Understand the psychology of the collector and respondent to address the issues. Most of the agencies don't follow the required process rather they follow set process which is just a formality.
Surabhi Singhal
1mo
Thanks for sharing this. Its well structured, I have a few questions: 1) What all data types are included in this? (certain text data issues seem to be missing) 2) The overlapping entries which are 441, this is a very common issue especially in an unbiased data where one requires data balancing
Business Ops Analyst 📊 @AdPushUp | Business Analyst | Business Intelligence Analyst | Excel | Spreadsheet | SQL | Power BI | Python | Storytelling with Data| Visualization
1moThis tool can be indispensable for data professionals and game-changer. Data wrangling often consumes significant time and effort, making it a tedious process. With a tool like this, we can set a clear threshold for data quality, allowing us to focus our efforts on high-quality data and avoid wasting time on poor-quality data. It would streamline our workflow and make our data analysis much more efficient and effective.