Ensuring data quality and trust
Insight Communities provide access to research participants – real people – but data quality still needs to be addressed. According to the STRAT7 Researchbods Insight Community Buyers survey (2025), 81% of Insight Community buyers are concerned about data quality.
Among current Insight Community buyers, 20 out of 26 agree that data quality is crucial when selecting a supplier, and 17 out of 26 say they prioritise partners with clear policies for identifying and removing poor-quality data.
What does data quality mean?
Data quality in market research is not a new concern. In the past, low-quality human responses and non-human responses alike have been at the centre of many a heated debate in the industry. From neutral responses to false recall, there are many instances where participants unintentionally muddy the waters with their feedback.
How much of a problem are bots and AI-generated participants for Insight Community data?
In addition to poor-quality human responses, Insight Communities are also home to responses from bots and AI-generated participants – in other words, digital personas pretending to be human.
When asked about the prevalence of AI-generated responses in their Community, the picture from Insight Community buyers in our survey was mixed: almost equal numbers reported seeing an increase as those who said they had not.
Interestingly, synthetically generated samples don’t appear to be the cure-all for improving speed to insight or data quality in quant research. When asked whether synthetically boosted survey samples were of interest to their organisation, buyers were split into three near-equal groups: those in favour, those against, and those unsure.
What should Insight Community buyers look for in a supplier to assess their commitment to data quality?
- Robust identification and validation of Members at sign-up.
- Clear policies for identifying and removing poor-quality data – policies that are regularly reviewed and updated as fraudulent agents become more sophisticated.
What can Insight Community suppliers do to minimise poor-quality data?
At STRAT7 Researchbods, we recognise that data quality is paramount for our clients and the wider insight community. We’ve made it our mission to set the standard for integrity and authenticity.
That’s why every Insight Community we manage benefits from two-factor authentication and STRAT7’s Data Integrity principles and models. These systems are designed to identify and remove poor-quality survey responses, spot bots and AI-generated content and ensure that only real, authenticated participants contribute to our research.
We’ve also invested in resourcing a specialist business unit – our Data Collection Division – with the sole responsibility of tackling industry-wide data collection challenges. Our human champions are armed with validated technological tools to ensure the integrity of your data.
We’re also exploring new ways to further validate participants at Community sign-up, reducing the risk of participant fraud and strengthening trust in our data.
While synthetic data boosts promise speed and scale, it feels like the jury is still out on their ability to deliver high-quality, actionable insight. That’s why we’re investing in proof-of-concept projects to stress-test this emerging technology, building on the findings from the recent STRAT7 whitepaper on synthetic data. Our commitment is to stay ahead of the curve, ensuring our clients can rely on the quality and authenticity of every data point we deliver.
About the author
Sarah Askew is Head of Brand and Innovation at STRAT7 Researchbods, where she leads on platform and toolkit innovation across their insight communities and custom research solutions. She’s also part of STRAT7’s AI Innovation Lab team.
