How do you approach data quality assurance and testing in your projects?

1 Data Quality Framework

Before you start testing your data, you need to define what data quality means for your project and how you will measure it. A data quality framework is a set of standards, rules, and metrics that guide your data quality assurance and testing activities. It should align with your project goals, stakeholder expectations, and data governance policies. A data quality framework typically covers dimensions such as completeness, validity, accuracy, consistency, timeliness, and relevance of the data.

2 Data Testing Strategy

Once you have a data quality framework, you need to plan how you will test your data throughout the project lifecycle. A data testing strategy is a document that outlines the scope, objectives, methods, tools, and roles of your data testing activities. It should include what types of data testing you will do (such as unit testing, integration testing, regression testing, performance testing, and user acceptance testing), how you will design and document your test cases and scripts, which tools and frameworks you will use to automate and streamline your data testing processes (like PyTest, SQLTest, dbt, or Great Expectations), how you will manage and report your test results and issues, and how you will coordinate with team members and stakeholders during the data testing phases.

3 Data Testing Environment

When performing data testing, it's important to consider the environment. A data testing environment should be isolated from the production environment and have its own access controls, security policies, and backup procedures. Additionally, it should have enough data volume, variety, and quality to represent the production data and support your test cases. The environment should also be similar to the production environment in terms of data sources, pipelines, transformations, models, and outputs. Lastly, it should be scalable, flexible, and easy to reset and refresh as needed.

4 Data Testing Execution

The next step in your data testing activities is to execute according to your data testing strategy and environment. This involves preparing test data by loading, generating, or masking it for test cases. Then, run the test cases by applying the scenarios and scripts to the data and verifying the expected outcomes. Record the results by capturing the pass or fail status, the actual outcomes, and any errors or anomalies that occur during the tests. Analyze the results by reviewing the logs, reports, and metrics to identify any data quality issues or defects that need to be fixed or escalated. Continue repeating the tests until you reach the desired level of data quality and performance.

5 Data Testing Monitoring

The final step is to monitor your data quality and performance after you deploy your data products and pipelines to the production environment. Data testing monitoring involves setting up data quality and performance indicators and thresholds that reflect your data quality framework and stakeholder requirements, implementing data quality and performance checks and alerts that run regularly and automatically on your production data, collecting and visualizing data quality and performance metrics and trends that help you evaluate and improve your data products and pipelines over time, as well as conducting periodic data audits and reviews that verify the compliance and effectiveness of your data quality assurance and testing processes and practices.

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?