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Data quality is the bedrock of any successful data governance program. Garbage in, garbage out, as they say! Data Quality KPIs measure the accuracy, completeness, consistency, and timeliness of your data. This helps you build trust in your data and make sure that decisions are based on reliable information. For example, the **Data Accuracy Rate** measures the percentage of data elements that are accurate. This could be calculated by auditing a sample of your data and comparing it to a known source of truth. Another important KPI is **Completeness Rate**, which tracks the percentage of data fields that are populated. High rates suggest that all required data has been captured. If you're missing important data, it can skew your analysis and lead to inaccurate conclusions. Then we have the **Data Consistency Rate**, which measures the degree to which data is consistent across different systems. Inconsistent data can lead to confusion and errors. For example, if a customer's address is different in your CRM and your billing system, you have a consistency issue. Last but not least, **Timeliness** tells you how up-to-date your data is. This is crucial for real-time decision-making. If your data is stale, you may miss important trends or opportunities. For example, how long does it take for sales data to be reflected in your reporting system?
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