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    Measure how well your pipelines perform with metrics like throughput rate, data completeness, downtime, and more I categorize each metric into three buckets – performance, quality, and efficiency – each critical for maintaining a robust data infrastructure
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    Addressing data quality issues often starts with defining key metrics that reveal where data may be inaccurate, incomplete, or inconsistent Here’s how to get started Setting Data Quality Goals and Objectives Identify Clear Metrics: Identify data quality metrics that matter the most to your team, such as accuracy, completeness, timeliness, etc
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    KPIs and metrics are necessary to measure the quality of data Organizations can use the dimensions of data quality to establish metrics and KPIs for their specific needs Understanding how to identify and measure the quality of data using KPIs can make or break data initiatives





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