How do you maintain data quality?

2 Implement data quality checks

Once you have defined your data quality criteria, you need to implement data quality checks to ensure that your data meets those standards. Data quality checks are the methods and tools that you use to detect and correct data quality errors, such as missing values, duplicates, outliers, or inconsistencies. For example, some common data quality checks are data profiling, data cleansing, data validation, and data auditing. Depending on your data sources, formats, and volumes, you may need to use different types of data quality checks, such as manual, automated, or hybrid.

3 Document data quality processes

Another important step to maintain data quality is to document your data quality processes. Data quality processes are the procedures and workflows that you follow to collect, store, transform, and analyze data. Documenting your data quality processes helps you to keep track of the data quality activities that you perform, the data quality issues that you encounter, and the data quality improvements that you achieve. For example, some common data quality documentation are data dictionaries, metadata, data lineage, and data quality reports. Documenting your data quality processes also helps you to communicate and collaborate with other stakeholders, such as data providers, data consumers, or data auditors.

4 Monitor and improve data quality

The final step to maintain data quality is to monitor and improve your data quality over time. Data quality is not a one-time event, but a continuous process that requires constant attention and adaptation. Monitoring your data quality means to measure and evaluate your data quality performance against your data quality criteria and goals. For example, some common data quality metrics are error rates, completeness rates, accuracy rates, and consistency rates. Improving your data quality means to identify and implement actions and solutions that can enhance your data quality results and outcomes. For example, some common data quality improvement actions are data standardization, data enrichment, data integration, and data governance.