Coalesce Catalog

Use natural language search to query 'customer data for email marketing in the US'.

Review the search results and identify relevant data assets, such as customer segments and email lists.

Explore the data lineage to understand the source and transformations of the data.

Assess the data quality metrics to ensure the data is accurate and complete.

Connect Coalesce Catalog to the new data warehouse.

Configure metadata extraction to automatically capture schema information, data types, and descriptions.

Use AI to generate documentation for each table and column, including business definitions and usage examples.

Publish the documentation to a centralized repository for easy access by all users.

Use data lineage to trace the data back to its source.

Review data quality metrics at each step of the data pipeline to identify anomalies and inconsistencies.

Investigate the data transformations to identify potential errors.

Implement data quality rules to prevent future occurrences of the issue.

Use Coalesce Catalog to identify all data assets that contain personal data.

Implement data access controls to restrict access to sensitive data.

Configure data masking to protect personal data from unauthorized access.

Generate reports to demonstrate compliance with GDPR regulations.

Identify all data team data sources.

Identify the source of bottlenecks.

Implement the proper data access controls

Generate reports to demonstrate compliance

Identify all data sources

Use Coalesce Catalog to generate data documentation

Ensure data quality metrics at each data pipeline

Implement data quality rules

Use AI to generate documentation for each table and column

Make sure all data sources are identified

Data lineage to trace the data back to its source

Investigate the data transformations to identify potential errors.