How do you test data quality after cleaning?

Data profiling for me is looking for any outliers, missing values, or odd patterns in the frequency distributions, summary statistics, and histograms for each column. In terms of data management and analytics, data profiling is a fundamental step. It enables businesses to make wise decisions, enhance the quality of their data, and increase the value of their data assets. It helps with the underlisted... Migration and Integration of Data: Diagram Design: Knowledge of Data Distribution Recognizing Redundancy Exploration and analysis of the data Risk evaluation and compliance Data security and privacy: Modeling and machine learning: Designing a dashboard and a report Troubleshooting and Debugging: Documenting and governing data:

Data Validation is Important , as it helps in eliminating Incorrect/Junk Data which is not useful, or which can lead to Incorrect Output. Data Validation can be done as part of Integrations, or before performing and Analytics.

Testing data quality after cleaning is a critical step to ensure that your data is accurate, complete, and reliable for analysis or any downstream tasks. Here are some general approaches that I would try out to test the data quality Domain Validation, Completeness Check, Consistency and Accuracy, Duplicates Detection, Referential Integrity, Pattern Validation, Statistical Analysis, Data Visualization, User Testing, and Documentation. Keep in mind that data quality is an ongoing process. Even after initial cleaning and testing, you should have processes in place to monitor and maintain data quality as new data comes in or as changes occur in your data sources

In my experience, After data cleaning ???, it's vital to ensure data quality. Start with data profiling for basic statistics ??, then use cross-validation to prevent overfitting ??. Comparative analysis compares cleaned and original data ??, while visual inspection spots anomalies ??. Validate domain rules ?, check for completeness ??, ensure consistency ??, and maintain referential integrity ??. Apply statistical tests ?? and involve users for testing ??. By combining these things, We can establish a comprehensive framework for testing the quality of our data after the cleaning process. This approach ensures that our data remains accurate, reliable, and fit for its intended use.

Schema validation can be enforced without utilizing any third party tools; only valid data is stored while failures are fixed followed by pipelines rerun. Further Data Quality checks can be performed for selected datasets on a need basis.