Real-Time data cleansing: How to adopt a “Data Quality Over Quantity” mindset - getWizer
How accurate and informed your research-based decisions will be, greatly depends on the quality of the data your research produces. It’s a no brainer, right? Well, tell that to the research team that has just collected thousands of open-ended responses and now faces the arduous task of ‘cleansing’ that data.
Data quality in market research is a big issue. Bad or ‘dirty’ data produces poor results and undermines the validity of the entire data set and therefore the research and any insights derived from it.
getWizer’s real-time data cleansing ensures your research is free from bad, unusable responses that could skew and invalidate your insights.
Importance of Filtering Responses
Data cleansing is the process of identifying and then removing inaccurate, irrelevant or meaningless information from your raw data. This can include filtering out unqualified respondents and those that did not properly engage with the questions (answered too fast or gave repetitive, inconsistent, nonsensical, irrelevant or unrealistic answers).
Importantly, not all problematic research results are deliberate. They may include duplicate responses from people accidentally answering a survey twice or not realizing that their submission had been completed. Responses can also contain misspellings, slang and incomplete sentences. As a result, the majority of data needs to be cleansed to some extent.
An Automated 3-Layer Approach
Using people to cleanse data manually is far from optimal. It’s costly, time consuming and best suited to smaller data sets. Computer-based data cleansing, using artificial intelligence allows us to use algorithms to clean data far more quickly and cost-effectively. getWizer uses three layers of algorithms to achieve this by examining each response to ensure a thoroughly clean data set.
Firstly, this ensures the quality of each panel sample is very high. Panelists must represent a broad cross section of the population, including all demographic groups and regions. So, once panelists fill out their information this is scanned, checked and verified. The user then has full control to filter the approved panelists into categories to ensure they match all the variables required for the specific panel they will be assigned to.
getWizer works exclusively with a selected list of high-quality panel providers. This ensures the profile of the respondents answering our surveys are always a match for the specified target audience.
The third layer is to check the quality of the data and cleanse it. The aim here is to identify problem data, such as curse words, typos and gibberish, etc. All the things you want to avoid when presenting data for a report.
Real-Time Data Cleansing
Data cleansing is traditionally done manually and after the responses of a panel have been completed. This is a slow, laborious process. If the sample size is significantly reduced as a result, due to the elimination of respondents, it also means the process of panel provisioning has to begin again to make up the shortfall.
Generally, when undertaking panel research, 5-10% of responses will be of unusable quality. The need for data cleansing, therefore makes this time-in-field portion of the research process the longest to complete. However, if you can eliminate manual labor and negate the requirement for any unnecessary additional panel selection cycles, the impact on the cost, efficiency and time to insight will be significant.
getWizer’s real-time automation of data cleansing assures quality, lowers cost, eliminates the need for manual labor and reduces overall time-in-field. It also ensures data quality throughout the process, as bad data is eliminated in real time. This is achieved by the use of algorithms using a mix of approaches, including natural language processing, to identify high-quality text in terms of relevancy and readability. The performance of the algorithms can also be improved, if necessary, by the manual verification of correct and incorrect analysis, enabling it to learn your specific requirements. Our real-time data cleansing capabilities also automatically amends typos, variants of a name and understands the use of slang.
This process also works for image-based responses. Sometimes researchers prefer to say, “Instead of telling me, show me.” This means they need to be able to deal with large amounts of user-generated content. Respondents can take pictures, and researchers need to be able to process these. But how?
Image Tagging and Categorization
getWizer uses machine vision algorithms to examine the content of the images uploaded by respondents and then automatically tags and categorizes them. This innovative approach allows research teams to quickly gain insights from images uploaded during the research process and eliminates the need for someone to go through hundreds of poor quality images taken by smartphone and try to sort through them.
Our advanced, real-time data cleansing offers speed without compromising quality. Researchers have in the past been used to trade offs and compromising. There once was a need for quick, cheap and dirty research. This is no longer the case. Now you can get quick, cost effective and clean research that is automatically filtered to ensure it meets the highest quality standards.
Real-time data cleansing is a key USP of the getWizer platform. Its advanced algorithms sets it apart from other available solutions making it unique when it comes to ensuring data quality. This means that when your data is presented via the getWizer platform it will be ready for you to analyze and run statistics on. The result will be high quality, clean data that will bring you the insights needed to empower your decision making.
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