Quality considerations for tabular and structured data

Projects working with structured data, such as survey microdata, experimental data, administrative records and transactional datasets, require systematic quality checks throughout the research process.

Tabular data quality depends on both the accuracy of individual values and the consistency of the data structure. Common quality risks include missing values, duplicate records, inconsistent coding schemes, incorrect variable types and undocumented transformations.

During active research, data producers should routinely check that:

• variables use consistent formats and value ranges
• missing values are correctly coded and documented
• duplicate or unexpected records are identified
• derived variables are created consistently
• labels and metadata remain aligned with the data.

Running regular summary statistics, frequency checks and basic validation tests helps identify potential errors early, before they propagate through analysis workflows.

Practical tools for checking tabular data quality

Dedicated tools can support structured quality assessment for tabular datasets. One such resource is QAMyData, an open-source data health check tool developed by the UK Data Service. QAMyData runs automated tests on common social science data formats such as SPSS, Stata, SAS and CSV to identify potential quality issues, including missingness, duplication, inconsistent metadata and possible disclosure risks.

Using tools like QAMyData alongside project-specific validation procedures helps data producers improve data quality during the active phase and reduces the effort required later when preparing data for sharing.

Additional quality checks may also be implemented using statistical software workflows, database validation rules or scripted data pipelines, particularly for large or regularly updated datasets.