How do you validate data accuracy in your warehouse?

1 Data profiling

Data profiling is the process of examining the source data and the target data in the warehouse to understand their characteristics, such as data types, formats, values, ranges, distributions, patterns, and anomalies. Data profiling helps you identify and resolve any data quality issues, such as missing, incorrect, inconsistent, or duplicate data, before or during the data integration process. Data profiling can be done manually or using specialized tools that can automate and simplify the process.

2 Data reconciliation

Data reconciliation is the process of comparing the source data and the target data in the warehouse to ensure that they match and reflect the same information. Data reconciliation helps you verify that the data integration process has not introduced any errors, such as data loss, corruption, or duplication, during the extraction, transformation, and loading (ETL) stages. Data reconciliation can be done at different levels of granularity, such as record level, column level, or aggregate level, depending on the complexity and volume of the data. Data reconciliation can be done using queries, scripts, or tools that can perform the comparison and generate reports.

3 Data auditing

Data auditing is the process of monitoring and tracking the data quality and accuracy in the warehouse over time. Data auditing helps you detect and correct any data quality issues that may arise due to changes in the source systems, business rules, or user requirements. Data auditing can be done using various techniques, such as data lineage, data quality metrics, data quality rules, data quality dashboards, and data quality alerts. Data auditing can help you maintain and improve the data accuracy in your warehouse by providing feedback and insights on the data quality performance.

4 Data testing

Data testing is the process of verifying and validating the data accuracy in the warehouse using predefined test cases and scenarios. Data testing helps you ensure that the data in the warehouse meets the specifications and expectations of the end users and stakeholders. Data testing can be done using various methods, such as unit testing, integration testing, regression testing, user acceptance testing, and performance testing. Data testing can be done using tools that can automate and streamline the testing process.

5 Data review

Data review is the process of involving the end users and stakeholders in the data accuracy validation process. Data review helps you get feedback and confirmation from the data consumers and owners on the quality and accuracy of the data in the warehouse. Data review can be done using various methods, such as data sampling, data visualization, data analysis, and data reporting. Data review can help you enhance and refine the data accuracy in your warehouse by incorporating the user perspectives and preferences.

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?