Collecting cleaner data with query management by Diego Menchaca

Hi, my name is Diego Menchaca. I’m the founder of Teamscope, a data collection app for field and clinical research. 

Lessons Learned: An M&E professional’s worst nightmare is to realize after data collection, that data is plagued with errors and cannot be used. This realization often comes at a point where teams have returned from the field, and resources have been spent. At that moment, unfortunately, it’s too late, and the damage is done.

To mitigate the risk of “garbage in garbage out”, the first measure we adopt is to use a data collection solution that supports data validation. 

Data validation means checking the accuracy and quality of data, this can be done manually after data has been collected or automatically if data is gathered digitally. When using input data validation, constrain rules can confirm that our data matches specific criteria or that required fields have not been left empty.

Most data collection tools prevent the enumerators from advancing or saving if the data entered does not match the validation criteria, and this can be an issue. 

But what happens if that data was indeed correct, the information asked for the required field is not available at that moment, or the validation rule is actually incorrectly setup? If only there was a way to immediately know when erroneous data has been collected.

Meet your new superhero: Query Management.

Hot Tip: What is Query Management?

Query management is the ability of data collection systems to identify data entries with issues and isolate them into a report. For every out of range or inconsistent value, the data capture tool generates a data query. Each data issue becomes an entity in itself and thus can be tracked over time to see if it is still present, or if it has been resolved by someone in the team. 

A query management system allows M&E practitioners to detect and address data issues immediately, and it substantially minimizes and potentially eliminates the risk of invalid data remaining unnoticed. 

How can I get started with query management? 

While it is possible to set up data validation on Excel or SPSS to scan your data for erroneous entries, this quality control usually occurs after data collection has finished. Excel or SPSS unfortunately do not support query management. 

If you would like to learn more about query management and give it a try, you can use Teamscope, a data collection app for clinical and field research. With Teamscope you can create mobile forms with data validation and see in real-time which of your enumerators have entered data with issues.

Teamscope is free for up to 5 users, and offers paid plans for larger teams and extended functionalities. 

Rad Resource:

Query Management diagram

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