ESOMAR Research World’s Post

🔍 The Cost of Bad Data: Why Data Quality is More Important Than Ever 🛡️ In today’s fast-paced business world, data is everything—but only if it's accurate. Bad data can lead to misguided strategies that risk millions of dollars, all because of compromised insights. At C+R Research, we take data quality seriously. In a recent blog, Todd Eviston, Senior Vice President of Operations, explains how our Sentinel System combats bad actors in survey data, ensuring that the insights driving your business decisions are reliable and trustworthy. From pre-survey screening to post-survey analysis, our multi-layered approach to data integrity has removed up to 47% of fraudulent respondents in key studies, protecting your business from inaccurate insights. Discover how data quality impacts your strategy and how C+R is leading the charge in maintaining the highest standards in market research. 📖 Read the full blog here: https://bit.ly/3MG4Ujk #MRX #DataQuality #MarketResearch #Insights #AdvancedAnalytics #QuantitativeResearch #CRResearch

🚨 Is your data working for you or against you? 🚨 Bad data can cost your business more than just dollars—it can lead to flawed strategies, missed opportunities, and a compromised reputation. In our blog by Todd Eviston (SVP, Operations), he reveals how our Sentinel System identifies and eliminates fraudulent respondents, ensuring your insights are accurate and actionable. Discover how we’re staying ahead of the game in the fight for data quality, leveraging both human expertise and cutting-edge technology to protect your research integrity. 💡 📖 Read the full blog here: https://bit.ly/4ef0Kev #MRX #DataQuality #MarketResearch #Insights #AdvancedAnalytics #QuantitativeResearch #CRResearch

💡 With the ever-growing amount of data, is data minimization truly achievable? In a world where information overload is real, organizations need smarter strategies for managing data effectively. Explore the challenges and possibilities of data minimization in the latest IDC blog: https://bit.ly/3MT5vP4 #DataManagement #AllegiantNOW #DigitalTransformation

What spotting data quality issues feels like 🔍 Issues are sometimes obvious to the eye. But most of the time, they're hard to detect. Why? The top 3 reasons I've encountered are: 1. 𝗗𝗼𝗺𝗮𝗶𝗻 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗶𝘀 𝘀𝗰𝗮𝘁𝘁𝗲𝗿𝗲𝗱 𝗮𝗺𝗼𝗻𝗴 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀. Data teams are often responsible for data used by the entire business. But it's unfeasible for a single team to master the intricacies of every metric — e.g. building an intuition for what flagging that "churn for this segment looks weird."     2. 𝗜𝗻𝘀𝘂𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗰𝗼𝘃𝗲𝗿𝗮𝗴𝗲. Adding checks can be time-consuming. And even when checks are in place, it's easy for noise to wash out the signal. As a result, many teams are in the situation of missing "unknown unknowns" with an improperly fit monitored setup.     3. 𝗢𝘃𝗲𝗿𝗹𝘆 𝗰𝗼𝗮𝗿𝘀𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴. If the number of rows in a users table dips by 1%, is that an issue? Unclear. But if the number of users in a channel drops to 0, while the rest of the channels behave as expected, then it's an issue. Without monitoring at the appropriate level of granularity, this is difficult to spot. A slew of other reasons come to mind. Why do you think data quality issues can be tough to spot? And don't even get me started on the challenge of finding the root cause ... #dataengineering #analytics #dataquality

Catch the second part of our post about the value of data and how it can hold potential to drive new initiatives or support existing ones. Here are the next 3 contributions from Bob Corr, Paul Duggan and Lorenzo Lumanlan. https://lnkd.in/e-sftZbK

I love this post from one of our I&D team in the UK. Jade Matthieson has perfectly encapsulated how data on its own is not the value, organisations need to be able to tell a story about the data and the impact it can have on their performance for data to be turned into value. https://lnkd.in/efnt2-64

"Data quality serves as the bedrock upon which the edifice of meaningful analysis and informed decision-making stands tall." Data quality has risen up the market research industry agenda in recent years. Melanie Courtright outlines the importance of data quality to insight. https://lnkd.in/e_7wptgh #mrx #DataQuality #DataQuality #insight

Catch the second part of our post about the value of data and how it can hold potential to drive new initiatives or support existing ones. Here are the next 3 contributions from Bob Corr, Paul Duggan and Lorenzo Lumanlan. https://lnkd.in/ee3hsKWF

How MindForce Research is Tackling Data Quality Challenges in Market Research Many market researchers face the hurdle of ensuring truly authentic data. Even with data cleaning and fraud detection, challenges persist. Mindforce’s recent study across 18 countries involving over 100 market research professionals revealed: -> 78% face data quality issues despite existing techniques. -> Common culprits include bias, speeders, outliers, and inconsistent data. These roadblocks hinder the accuracy of insights that drive business decisions. At MindForce Research we're committed to data quality. That's why we developed the Data Quality Score Module, a data validation tool built on a robust statistical foundation. This module assesses six key parameters to identify poor-quality data at the respondent level, helping researchers differentiate between genuine and unreliable responses. The result? A bridge between data collection and processing, leading to bias-free data and actionable insights Ready to unlock the power of reliable data? Reach out to learn more about the Data Quality Score Module and how it can empower your market research. P.S. Share this post to help others overcome data quality challenges in market research!

https://lnkd.in/dtyqjc_r The second article of three. This describes data types and some of its uses. Again, thanks to Kelly Wright for her work on this project.