Cloud Data Governance and Catalog

Data profiling involves examining and analyzing data to produce useful summaries and insights. This process provides a high-level overview that helps identify data quality issues, potential risks, and overall trends. With these critical insights, organizations can take informed actions to improve data quality and leverage their data assets more effectively.

Data quality rules enable measurement of essential data dimensions, including the contextual accuracy of values, consistency among data points, and adherence to allowed formats.

Defining these rules allows business users to establish clear quality standards, which can then be automated and enhanced through AI and machine learning for technical implementation. The results of these quality checks are captured and visualized, enabling ongoing monitoring and trust in the data.

The scorecard offers a comprehensive view of data quality across your data ecosystem. It supports both aggregated analysis and detailed drill-downs into specific data quality rules, showing valid and invalid records. This empowers stakeholders to evaluate data trustworthiness and take action based on transparent, measurable metrics.