Data Quality

This page will outline some best practices around data quality

Data quality is important to Secoda. Without data quality measures in place, this can result in data issues, inconsistencies, errors, and anomalies that impact the accuracy and completeness of data.

We currently have a handful of features in the product to help ensure you are maintaining the quality of your data and the quality of your data documentation.

• Monitoring that allows you to set thresholds on your data and get alerted when there are unexpected changes

• Column profiling that allows you to view the distribution of your data, the column count, and how many unique columns you have

• Stale data identifier that identifies and hides any stale data across your workspace

• Identify your undocumented resources directly in the UI to ensure that you're staying up to date with documentation standards

• Configure notifications and subscribe to resource changes so that you never miss alerting on schema changes that may impact downstream dependencies