Data Quality
This page will outline some best practices around data quality
At Secoda, we understand the critical role that data quality plays in ensuring reliable analytics and decision-making processes. High data quality prevents inconsistencies, errors, and anomalies, thus enhancing the accuracy and completeness of your data.
Key Features Supporting Data Quality
Secoda provides a Data Quality Score out of 100, reflecting the quality of data connected to Secoda. This score helps you gauge the integrity and reliability of your data at a glance.
Monitoring Tools:
Set data quality monitors on your data and receive alerts when thresholds are breached. This feature helps you stay on top of unexpected changes in your data.
Gain insights into data distribution, count, and uniqueness within columns. This tool helps you understand data at a granular level and ensures that your data remains structured and query-ready.
Automatically identify and hide stale data within your workspace, ensuring that users interact only with the most current and relevant data sets.
Easily identify undocumented resources within the UI. Secoda aids in maintaining rigorous documentation standards, ensuring that all data assets are properly described.
Notification and Subscription:
Configure notifications to stay informed about resource changes. This ensures that you are immediately aware of any schema changes that might affect downstream dependencies, allowing for quick adjustments.
Why Prioritize Data Quality?
Improving data quality directly enhances the utility, accessibility, and reliability of your organizational data. By implementing robust data quality measures, Secoda helps organizations foster a culture of data trustworthiness and reliability. Our tools are designed to provide actionable insights, promote transparency, and drive better outcomes by ensuring that your data meets the highest standards of quality.
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