Why Expertise Matters More Than Ever
It has never been easier to collect data.
With DIY research platforms on the rise and client-side research teams becoming smaller, more projects are being set up quickly and often with less specialist oversight. What used to involve experienced researchers carefully designing and managing fieldwork can now be done in a few clicks.
The challenge is that ease does not equal quality and whilst access has improved, oversight has often reduced. In many cases, experienced researchers simply do not have the time or resource to stay close to every stage of the process.
That is where the risk comes in.
Poor quality data and unreliable insight are becoming more common, leaving brands and retailers at risk of making decisions based on flawed data.
While technology has made things faster, it has not removed the need for careful recruitment, script checking and data validation.
In fact, in today’s environment, having an experienced fieldwork partner involved is often the difference between data you can trust and data you cannot.
What is driving the rise in poor quality data?
There are two clear shifts happening across the market research industry.
First, many client-side research teams are smaller than they used to be. Experienced researchers are often stretched across multiple projects, or no longer in place to oversee the detail.
Second, DIY research platforms and tech-led solutions have made research more accessible. That is not a bad thing in itself. But these tools often focus on speed and scale rather than the quality of data being collected.
The result is a growing volume of research that looks robust on the surface, but lacks the depth, scrutiny and control needed to ensure reliable outputs.
Why does poor data quality lead to poor decisions?
Market research is only as strong as the data behind it. If participant recruitment is rushed, if screening is too light or if survey scripts are not properly checked, issues can quickly build.
These might include:
• Participants who do not fully meet the target criteria
• Low engagement or inattentive responses
• Reliance on ‘unclean’ panel data
• Logic errors or unclear survey design leading to ambiguous responses
Individually, these may seem small. But combined, they can distort findings and lead to misleading conclusions.
For brands and retailers, this creates real risk. Decisions on product development, pricing, messaging or customer experience may be based on data that does not truly reflect their audience.
How can you spot poor quality fieldwork?
Not all quality issues are obvious at first glance. That is why strong fieldwork processes matter.
Some common warning signs include:
• Data that looks too clean or too consistent
• Very fast completion times without scrutiny
• Limited visibility on recruitment or sampling methods
• Lack of detailed and multi-layered validation processes and quality checks
High-quality fieldwork is transparent. It shows how participants were recruited, how data was checked and what steps were taken to ensure accuracy.
Why having an expert in the room matters more than ever
As research becomes more accessible, the role of experienced fieldwork partners becomes more important.
Expertise is not just about running fieldwork. It is about asking the right questions before fieldwork even begins.
At Face Facts, that means:
• Carefully reviewing materials up front to identify issues early
• Applying robust screening and verification in participant recruitment
• Running thorough script checks to avoid logic errors
• Monitoring data quality throughout fieldwork, not just at the end
• Removing and replacing low-quality completes where needed
This level of scrutiny does take time and effort, but it’s worth it as we see fieldwork as the foundation of good research. When that foundation is solid, everything built on top of it becomes more reliable.
Can DIY research still deliver quality results?
DIY platforms can absolutely play a role in modern research. They are useful for speed, iteration and early-stage exploration.
However, they are not a substitute for expertise. Without proper oversight, there is a real risk that quality is compromised.
The most effective approach is a balanced one – using technology where it adds value, while keeping experienced researchers close to every stage, from design and recruitment through to fieldwork and quality control.
Final thought
The current shift towards faster, more accessible research is not going away. But neither is the need for high-quality data.
If anything, the growing risk of poor quality insight makes it more important than ever to have experienced people involved. Because when decisions matter, the data behind them needs to be trusted.
If you are planning research and want to make sure your data stands up to scrutiny, we would love to help. Get in touch with Face Facts to see how we can support your next project with careful, high-quality fieldwork and human /senior led management.
FAQs
What is poor quality data in market research?
Poor quality data refers to responses that are inaccurate, inconsistent or not from the intended audience. This often comes from weak screening, disengaged participants or a lack of quality checks.
Why is data quality getting worse in market research?
Smaller client-side teams and the rise of DIY research platforms mean less oversight and fewer quality controls, increasing the risk of unreliable data.
How can poor data impact business decisions?
Poor data leads to flawed insights, which can result in ineffective strategies, wasted budget and missed opportunities.
What are the signs of low quality fieldwork?
Common warning signs include very fast completion times, overly consistent answers, unclear recruitment methods and limited visibility on quality checks. At Face Facts, we build in ongoing monitoring and validation throughout fieldwork, not just checks at the end, to ensure data quality is protected.
How can you ensure high quality market research data?
High quality data comes from careful recruitment, strong validation and ongoing monitoring. At Face Facts, this is supported by ISO 27001 information security, MRS Fair Data standards, RAS accredited recruitment, our commitment to the Global Data Quality pledge and consistent senior oversight on every project.