Verifying data integrity in Sigma involves a two-pronged approach: leveraging built-in features and incorporating custom logic. Sigma offers data validation for specific column types like text, number, and date. You can define acceptable value ranges or create custom lists to restrict what users can enter into these fields. This helps catch basic errors during data entry within Sigma's Input Tables.

For more complex scenarios, Sigma allows you to create calculations based on existing data. By building formulas that check for inconsistencies or expected data patterns, you can identify anomalies that might slip through basic validation. Imagine calculating a customer's average order value and then comparing it against individual order amounts. Significant deviations could flag potential errors or outliers requiring further investigation. While Sigma itself doesn't offer deep data cleaning functionalities, these strategies empower you to ensure a good foundation for your data analysis within the platform.

Integration with Sigma allows users to verify data through Secoda. An Automation consists of Triggers and Actions. Triggers activate the workflow based on defined schedules, such as hourly, daily, or custom intervals. Actions encompass various operations, including filtering and updating metadata. Users can stack multiple actions to create customized workflows that meet their team's requirements. Secoda enables bulk updates to metadata in Sigma.

Secoda's integration with Sigma allows for seamless verification of data. By serving as an index of your company's data knowledge, Secoda consolidates your data catalog, lineage, documentation, and monitoring into a single data management platform. This integration ensures efficient and reliable data governance, enabling users to easily verify the accuracy and reliability of their data through Secoda's AI-powered capabilities.