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Description
Data quality is not a destination but a continuous journey of evolution and prioritization. “Redux” for data quality refers to the ongoing activity of improving data quality as an iterative process. Creating high-quality data involves identification of issues related to data accuracy, completeness and consistency, remediation, continuous monitoring, and maintenance of these processes throughout the lifecycle of the data.
Key takeaways:
- Learn how to identify data quality issues and improve incrementally and iteratively.
- Understand how data quality can be augmented through automated rules recommendation, dashboards, observability, and statistics.
Target audience level: all levels
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