What are the most effective data quality monitoring practices?

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 processes and procedures that you use to identify and correct any errors, inconsistencies, or anomalies in your data. They can be performed at different stages of the data lifecycle, such as during data collection, data entry, data cleaning, data integration, and data analysis. For example, if you are using an online survey platform, you may want to use features such as validation rules, skip logic, randomization, and captcha to prevent or reduce data entry errors, bias, and spam.

3 Use data quality tools

Data quality monitoring can be a complex and time-consuming task, especially if you are dealing with large or diverse data sets. Therefore, you may want to use data quality tools to automate or simplify some of the data quality checks and procedures. Data quality tools are software applications or platforms that can help you perform various functions related to data quality, such as data profiling, data cleansing, data transformation, data integration, data auditing, and data reporting. For example, if you are using multiple data sources, you may want to use a data integration tool to merge and harmonize your data and a data auditing tool to track and document any changes or issues in your data.

4 Establish data quality roles and responsibilities

Data quality monitoring is not a one-time or one-person activity. It requires ongoing collaboration and communication among all the stakeholders involved in the data lifecycle, such as data collectors, data processors, data analysts, and data users. Therefore, you need to establish clear data quality roles and responsibilities for each stakeholder and ensure that they are aware of and accountable for their tasks and expectations. For example, if you are working with a team of data collectors, you may want to assign a data quality manager who can oversee and coordinate the data collection process and a data quality reviewer who can check and verify the data before submitting it.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?