Data profiling is the process of examining, analyzing, and creating useful summaries of data. The process yields a high-level overview which aids in the discovery of data quality issues, risks, and overall trends. Data profiling produces critical insights into data that companies can then leverage to their advantage.
Below you can see a demo of 'How a Data Analyst Uses Discovery Capabilities to Shortlist Data Assets with Good Data for a Marketing Analytics Project' and a guide on 'How to Automate and Scale Data Discovery and Identification'.
Data quality rules enable the measurement of various data quality dimensions, such as:
Contextual accuracy of values (correctness, accuracy)
Consistency among values (consistency)
Allowed format of values (representational consistency, accuracy)
Please find the following resources below:
Interactive Demo: How business users can define data quality rules in CDGC, that can be automated and transformed into technical rules using AI/ML through cloud data quality. The demo also showcases how the results of the data quality execution can be captured in CDGC using intuitive dashboards.
Videos: Data Quality
Detailed Guides: Data Quality Accelerators & How to Generate Trust in Data Governance with Data Quality.
Scorecard provides comprehensive insights about the quality of data in your Data Ecosystem, and allows both aggregated analysis and detailed drill-downs into respective Data Quality Rules, providing Valid/Invalid records and improving trust.
Please find the following resources below:
Guide: 'Data Quality Metrics and Scorecards'.
Videos: 'Data Quality Rule Occurrence and Score Card Rule Occurrence' and 'Data Quality Automation or Data Quality Rule Template'.
