Taking on the Data Quality Crisis: My-Take Launches DQM™ (Data Quality Management System) for Insight Community+™
Automated data quality control with human-in-the-loop, purposely built for insight communities and ad-hoc projects.
WESTBOROUGH, Mass., Aug. 13, 2026 /PRNewswire/ -- My-Take has announced the launch of DQM™ for its Insight Community+™ platform, delivering automated data quality control with human-in-the-loop, purposely built for insight communities.
DQM™ combines automated survey protection checks, webcam-based biometric liveness verification, and a Data Quality Review Console to catch fraudulent, bot-driven, and low-quality responses before they reach your data.
At the core of DQM™ is My-Take's proprietary data quality framework, designed and built by our engineering team specifically for the Insight Community+™ platform.
The Data Quality Crisis
The market research industry is facing a data quality problem that continues to grow. Bad actors including bot farms, AI-generated survey responses, professional survey takers, and VPN-masked identities are degrading the reliability of research data. When the data is unreliable, the insights built on top of it are too, leaving business decisions to be made on a broken foundation.
The Problem in Numbers
40% of researchers cite data quality as their top challenge (2025 GRIT Insights Practice Report, Greenbook)
4-5% of all research records directly linked to fraud (Global Data Quality Initiative, 2025)
Why Insight Communities Are Better Positioned
Insight communities have a built-in advantage when it comes to data quality. Members are recruited, profiled, and verified by the brand or research team running the community. They participate in ongoing research over time, building a track record that researchers can monitor and evaluate. Unlike open panels, where respondents are largely anonymous and interchangeable, community members have an established relationship with the organization and a reason to stay engaged. Researchers know who their respondents are and can track behavior over time.
But better positioned does not mean invulnerable. Research teams have always invested significant manual effort in reviewing and protecting data quality within their communities. Community research still faces threats from bots, AI-generated responses, and low-effort participation, especially when fielding surveys to external audiences. DQM™ was built to close that gap, automating what previously required manual review and adding layers of protection that weren't possible before.
"The data quality challenge isn't going away on its own. Insight communities have always been better positioned because of the direct relationship between the researcher and the respondent, but better positioned isn't bulletproof. We built DQM™ to close that gap with automated, multi-layer protection that catches what manual review can't."
Todd Hoskins, Co-founder, My-Take
DQM™: How It Works
DQM™ works across three layers: automated survey protection checks that run on every response, optional biometric liveness verification at the point of entry, and a Data Quality Review Console for managing flagged responses. Access & Identity checks, Respondent Behavior Monitoring, Gibberish Detection, Biometric Liveness, and the full Review Console all function without AI. The remaining Content Analysis features are AI-powered and available to communities with AI functionality enabled.
Survey Protection Layers: Multi-Layer Survey Protection
Every time a respondent enters a survey on the Insight Community+™ platform, DQM™ runs automated checks across three categories:
Access & Identity
Unique IP Enforcement blocks duplicate IP addresses from entering surveys
Duplicate Device Prevention identifies and prevents repeat device access
Honeypot Threat Screening uses hidden fields to catch scraping scripts and bots automatically
Automation/Bot Detection detects headless and scripted browsers
Respondent Behavior
Speeder & Straightliner Flags detect rushed completion and patterned responses
Tab Switch & Copy/Paste monitors focus changes and clipboard activity
Typing & Mouse Analysis analyzes keystrokes, mouse movements, and anomalies
Content Analysis
Gibberish Detection identifies nonsensical or low-quality text (available to all communities)
AI Text Audit reads all responses to evaluate effort, language, and authenticity (AI-powered)
AI-Generated Content Rejection identifies and removes content produced by AI (AI-powered)
AI Image Auditor uses AI vision to verify uploaded images are relevant to the question asked (AI-powered)
AI Video Auditor evaluates video responses for relevance post-completion, flagging mismatched or low-effort uploads (AI-powered)
Biometric Liveness Verification [Optional]
DQM™ includes My-Take's proprietary liveness detection, which uses webcam-based checks to verify real human presence before survey entry. Respondents are prompted to perform actions that are difficult for bots or scripts to replicate programmatically. A second verification level captures blurred photos at the start and end of the check, and can confirm the same person completed both steps. Blurred images are available in the review console to aid decision-making without storing identifiable photos.
DQM™ also supports optional integration with Verisoul* for advanced identity verification, including facial biometric scanning and government ID checks, for research programs that require a higher level of respondent authentication.
Data Quality Review Console
Automated detection is only part of the equation. The Data Quality Review Console provides a centralized interface for managing flagged responses across both external collector traffic and internal community members, giving research teams full visibility and control over data quality decisions.
External Response Management
Flagged external collector responses can be auto-redirected, manually reviewed, approved, or rejected. Research teams can configure rules for how different types of flags are handled, from automatic rejection to manual review queues.
Member Survey Review
Flagged member responses are held in Review status with rewards withheld until an admin approves or rejects. This prevents low-quality respondents from receiving incentives for flagged submissions while giving research teams the ability to make informed decisions about each response.
Strike System
Rejected surveys can trigger strikes against a member account, with automatic probation after a set threshold. The strike system creates long-term accountability within the community, discouraging repeat low-quality behavior and protecting overall community data integrity.
Rejection Report Exports
Flagged and fraudulent responses can be exported from the Data Quality Review Console for collector credit and refund processing, giving research teams a clear audit trail and documentation for managing external sample provider relationships.
Researcher-Configured Quality Controls
Beyond the automated checks, DQM™ gives research teams direct control over how quality is enforced. Three quality presets (Lenient, Standard, and Strict) can be customized and applied independently to different response paths within the same survey. Community members who have already been vetted can run through a lighter set of checks, while external panel respondents get a stricter evaluation. Each preset is fully customizable at the community level and can be overridden on a per-survey basis.
Researchers can also set up logic-based quality traps using contradictory answer detection, which flags respondents whose answers contradict each other within the same survey. If a respondent says they are unaware of a brand but later names it as a favorite, the system flags that response for review. These traps are researcher-defined, letting teams target the specific inconsistencies that matter for their study.
"DQM™ was purpose-built by our team. The data quality crisis affects anyone who relies on survey data, and we think the combination of an insight community model with the kind of automated protection DQM™ provides is the strongest answer out there right now. And this is just the start. We have more on the roadmap that will keep pushing the standard forward."
Rich Armstrong, Co-founder, My-Take
Built on Insight Community+™: Your All-in-One Continuous Research Operating System
DQM™ builds on the foundation of My-Take's Insight Community+™ platform, a proprietary, fully integrated research operating system. Insight Community+™ combines:
Multi-methodology capabilities: Qualitative, quantitative, CX, and UX research, optimized for all devices
Versatile research hub: Execute both community and external ad-hoc projects with the same comprehensive toolset
Flexible service layer: DIY, Hybrid, or Full Service options supported by My-Take's team of community experts
Collaborative AI throughout: Including MyAI Assistant for system-wide intelligence
Automated data integrity: Now including DQM™ for multi-layer quality control
Expanding Portfolio of AI-Powered Research Tools
My-Take continues to develop proprietary AI tools that work alongside DQM™ and the broader platform.
MyAI Collaborative Assistant
An intelligent, collaborative AI agent embedded directly into the platform and purpose-built for research environments. MyAI works alongside researchers as an intelligent thought partner, analyzing data, generating insights, and providing recommendations across both community management and reporting workflows. Unlike general-purpose AI tools, MyAI understands research context, drawing on community data, past studies, and study objectives.
SmartDeck™ Reporting and Analysis
An AI-powered reporting solution that combines advanced analysis with automated client-branded slide formatting, transforming how researchers create presentation-ready PowerPoint reports. SmartDeck™ analyzes study objectives and data to generate compelling slide headlines, actionable insights, and executive summaries, freeing researchers to focus on crafting meaningful narratives.
Video and Text Analysis
The platform automatically identifies and organizes recurring themes across survey video and open-ended questions, interpreting sentiment throughout. AI auto-generates impactful highlight reels organized by theme, bringing the most compelling customer feedback moments to life for stakeholders.
Learn more about Video and Text Analysis →
Conversations AI-Infused Mobile-First Surveys
A mobile-first, chat-based survey format that makes the research experience simpler and more appealing, especially for specific market segments like Gen Z. AI auto-moderation probes for deeper responses, AI-generated personalized questions adapt to each participant, and AI response feedback fosters natural conversational interactions.
Learn more about Conversations →
Availability
DQM™ is available to My-Take clients across all service levels. Core protection layers, including Access & Identity, Respondent Behavior, Biometric Liveness Verification, and the Data Quality Review Console, are available to all communities. Content Analysis includes Gibberish Detection for all communities, with AI-powered features available to communities with AI functionality enabled. To learn more or to request a demo, contact My-Take at my-take.com/contact.
About My-Take
My-Take is a Greater Boston-based insight technology and analysis company specializing in online insight communities. My-Take's Insight Community+™ is a proprietary, fully integrated continuous research operating system that brings together multi-method research, collaborative AI, and flexible service models within a single platform. My-Take has been recognized on GreenBook's GRIT Top 50 Most Innovative Market Research Suppliers list for six consecutive years. For more information, visit my-take.com.
Industry statistics cited from available reports and webinars by Greenbook (GRIT) and the Global Data Quality Initiative.
*Verisoul is a registered trademark of Verisoul, Inc. Its inclusion does not imply any affiliation with or endorsement by My-Take.
SOURCE My-Take
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