What is Data Quality and how can I improve it?

In Market Research, Data Quality is assumed table-stakes.  But, the reality is that today it’s more difficult to achieve than ever.  Marketing, insights and market research Pros may be spending millions of dollars on useless results that ultimately mislead clients on a product-market opportunity.  

In today’s Tech-Driven Insights industry, more “DIY” research is occurring than ever before.  This is democratizing Market Research.  But it’s also giving massive responsibility to professionals without proper training or context.  

In this blog series, I’ll define Data Quality drivers in MRX Surveys and outline how to build an ongoing strategy in your research operations by understanding 4 key topics.  

1. Expectations: What should I expect on this survey for Data Quality?
2. Fraud Mitigation: What technology will I Implement for fraud detection and mitigation?
3. Analytics: How can I Measure what Matters?
4. Expertise: Employ Professional Market Research Experience to bring it all together. 

Let’s dig in with Topic 1 here.  

Topic 1: So, what is Data Quality anyway and what should I expect in my surveys? 

To paraphrase a past Marketing evolution blog post, “data quality indicates how reliable a dataset is across a few dimensions including: Completeness, consistency, Accuracy, validity.”  

In our world of Survey Research, the “data” we talk about are the answers to survey questions.  The “quality” is an evaluation if those are from real people, answering in a truthful way, after giving some thoughtful consideration.  

Data quality stems from all aspects of the survey process- survey experience, sample methods, sample technology, respondent incentives, and consumer attention span.  

From a high level, I apply a simple 5-point construct to assess if my next survey is optimized for data quality:

1. Survey Experience: Would your family member get through this ok without completely zoning out?  
2. Sample Methods: Am I clear on how the sample is making it to my survey?
3. Sample Technology: Does my sample vendor supply mix have a “class” of supply that is premium quality and am I getting it via the tech?
4. Respondent Incentives: Does the Cost per Interview (CPI) I’m paying reflect an incentive that’s worthy of the survey I’m fielding? 
5. Consumer Attention Span: Have I factored some probable competition (social media, text, etc.) in the attention of the respondents as they’re in the survey?

With these 5 points, I’m really just trying to set my own expectations on what data quality level is achievable on any given survey.

In our next post, we’ll jump to topic 2: Fraud in the Sample Ecosystem.

Please click here for Part 2 of 5.