Data Quality

Data Quality

Data quality is the cornerstone of reliable insights and informed decision-making. At Veridata Insights, we understand that accurate, consistent, and trustworthy data drives business success. This page explores the principles, best practices, and tools that ensure your data meets the highest standards, empowering you to turn information into actionable intelligence. Whether you’re managing large datasets or refining analytics processes, our approach to data quality helps you minimize errors, enhance efficiency, and build confidence in every decision.

Mapping Survey Fraud & Security Features

Survey fraud is increasingly complex. Veridata Insights addresses this with a four-step strategy: integrating software from three industry-leading fraud detection firms into SampleTap to block or monitor suspicious participants; using programmatic checks within surveys; applying human review by experienced project managers; and factoring in client feedback to track emerging trends.

The Fence – a Triple Layered Line of Defense to Detect & Prevent Fraud

The Fence combines the strengths of three industry leading firms arming us with superior automated survey fraud detection.

Survey fraud is increasingly complex. Veridata Insights addresses this with a four-step strategy: integrating software from three industry-leading fraud detection firms into SampleTap to block or monitor suspicious participants; using programmatic checks within surveys; applying human review by experienced project managers; and factoring in client feedback to track emerging trends.

Programmatic Checks and Security

Leveraging powerful tech is one step, however, in order to stay ahead of the most aggressive fraudsters, it is important to think about security within the survey itself.

• Invisible Security Passkey: Creates an invisible “handshake” between SampleTap and our programming platform so that we can instantly recognize if someone has tampered with their entry links to the survey.

• Hidden Questions: These are a unique, open ended, question that we place within the survey. It is invisible to the human eye, but a bot will “see” and answer it just like any other question.

• Copy Paste Detection and Prevention: Encourages survey responses which are more likely to be in the words of the survey participant.

• AI-Probing: The ability to leverage AI to review an open ended response in real-time and ask additional questions in order to encourage a more robust response.

Human Review

• Review of leading questions: When reviewing the survey screener, recommending changes to ensure that the questions do not unintentionally lead a respondent to the response that will allow them to continue in the survey.

• Open End Review: A manual review of open-ended survey responses checking for batched responses or similar indicators of fraudulent activity.

• Red Herrings: Inserting an unusual or obviously incorrect option within a question or adding a fabricated question that attentive respondents should easily recognize as invalid.

• Logical Inconsistencies: Mechanism that identifies contradictions or illogical patterns in a respondent’s answers. It ensures that responses across related questions are logically consistent.

• Screen Recording: Before respondents enter a survey, we ask them a few questions, including one open ended response. If certain participants seem to be answering programmatically, we can visually review their screen when they were answering the questions.

• Length of Interview Management: Determining the correct thresholds from soft launch data to identify unusually short durations or unusually long durations for survey completion. This helps to weed out those who are inattentive, confused or fatigued.

Client Feedback Loop

While internal checks help identify poor-quality respondents, our clients are experts in their fields of study. They can identify suspicious patterns, unrealistic responses, or demographic inconsistencies that may not be obvious to our team or easy to catch programmatically. When clients flag quality issues, it creates a feedback loop that holds sample sources, like Veridata Insights, accountable. Tracking these issues over time allows us to monitor performance, enforce standards, and remove underperforming sources.  This iterative approach fosters a culture of continuous improvement and helps maintain compliance with industry ideals.

What Is Data Quality?

Data quality refers to the accuracy, consistency, and reliability of data used for analysis and decision-making. High-quality data ensures that insights are trustworthy and actionable.

Why Is Data Quality Important?

Poor data quality can lead to incorrect conclusions, wasted resources, and missed opportunities. Maintaining strong data quality standards helps businesses make informed decisions and build customer trust.

What Are the Key Dimensions of Data Quality?

Accuracy: Is the data correct and free from errors?
Completeness: Does the dataset include all necessary information?
Consistency: Are data formats and values uniform across sources?
Timeliness: Is the data up-to-date and relevant?

How Does Veridata Insights Ensure Data Quality?

Veridata Insights uses advanced validation processes, automated checks, and continuous monitoring to maintain data integrity. This includes:

Data cleansing and normalization
Real-time error detection
Compliance with industry standards

What Are the Benefits of High-Quality Data?

Improved decision-making
Enhanced customer experience
Increased operational efficiency
Reduced risk and compliance issues