What is data quality and why is monitoring crucial?
Data quality refers to the stewardship, usability, freshness, and accuracy of data. High-quality data is essential for generating reliable insights and making informed decisions. Monitoring data quality is crucial as it provides insights into the state of your data, helping identify and address issues promptly. This ensures that your data remains trustworthy, accessible, and valuable for decision-making.
Data stewardship: Involves assigning clear ownership, tagging data appropriately, and addressing questions, ensuring that data is responsibly managed and accessible.
Data usability: Refers to the quality of documentation, including descriptions for resources, columns, and schemas, making data understandable and user-friendly.
Data reliability: Monitors the timeliness of data updates, ensuring that data remains current and relevant for use.
Data accuracy: Measures the integrity of data through tests for nullness, uniqueness, and other checks to maintain reliability across datasets.
These dimensions collectively support high standards of data quality, empowering organizations to confidently utilize data for analysis and strategic decisions.
Secoda’s Data Quality Score provides users with actionable suggestions to improve the quality of core assets.
How does Secoda ensure data quality and monitoring?
Secoda provides a robust suite of tools to monitor and ensure data quality, offering users insights into the accuracy, completeness, and timeliness of their data. These built-in features, including observability and monitoring tools, help organizations maintain high standards of data quality and optimize their data ecosystem.
Data observability monitoring: Secoda’s no-code monitoring tools allow users to set thresholds and track the status of critical tables, directly supporting data accuracy and reliability. By setting targeted monitors, users can proactively manage data health, catching issues like null values or duplicates early to maintain high-quality standards across core tables. These insights ensure that data remains accurate and reliable for decision-making.
Data quality scores (DQS): DQS combines multiple data health indicators into a single score, giving users a quick overview of data quality across their assets. By integrating insights from monitoring features, such as accuracy checks and freshness alerts, DQS becomes even more precise, enabling users to easily identify and prioritize high-quality datasets for self-service analytics.
Usage analytics: Secoda’s usage tools provide a comprehensive view of how data is used across the organization. By surfacing insights on which tables to deprecate or which resources need maintenance, Secoda ensures efficient use of data assets.
Query monitoring: Track query performance, usage, and dependencies to identify and optimize slow queries, improving efficiency and reducing costs associated with data processing.
Job monitoring: Secoda centralizes and tracks jobs across various data tools, enabling streamlined workflows and improved team collaboration.
Cost monitoring: This feature provides a detailed breakdown of data stack expenses, enabling organizations to manage costs effectively and maximize the ROI of their data resources. Users can set no-code monitors on high-cost assets to receive proactive alerts on unusually expensive queries or resources, helping teams stay ahead of budgetary concerns and optimize spending.
Alerting: Users can receive alerts on schema changes and new incidents through the app, Slack, or email, allowing for faster triaging and resolution of issues.
Add maximum value monitor on a core asset to get alerted on unexpected anomalies.
What data quality solutions does Atlan offer?
Atlan offers basic data quality features that include usage metadata tracking and notifications for changes. While these features help users stay informed about updates, Atlan does not provide comprehensive, built-in tools for proactive data quality monitoring.
Usage metadata: Tracks basic data usage patterns, offering insight into how assets are utilized within the organization.
Change notifications: Alerts users to changes in data assets, such as downstream lineage updates or modifications to metadata descriptions, helping them stay informed.
While these features aid in tracking certain data changes, Atlan lacks advanced monitoring tools, such as real-time data health tracking or quality scoring, making it more suitable for reactive data quality management rather than proactive assurance.
How do Secoda and Atlan compare in terms of data quality and monitoring?
Secoda and Atlan both offer tools for data quality management, but they differ significantly in their approach. Secoda’s comprehensive suite of proactive monitoring features, including Data Quality Scores (DQS), query and usage monitoring, and real-time alerts, provides a robust framework for ongoing data health assurance. This proactive approach enables organizations to prevent data quality issues before they impact decision-making. In contrast, Atlan’s tools are more reactive, focusing on tracking changes with basic usage metadata and notifications but lacking built-in, real-time quality monitoring capabilities.
Proactive vs. reactive: Secoda’s tools allow for proactive data quality management, while Atlan’s solutions address issues after they occur.
Comprehensiveness: Secoda’s data quality monitoring features offer a complete, real-time approach to data quality, whereas Atlan’s offerings are limited to basic alerts and metadata tracking.
Which platform is better suited for organizations prioritizing data quality?
For organizations where data quality is a priority, Secoda’s comprehensive and proactive approach makes it a better choice. Secoda’s advanced monitoring features, such as DQS, detailed usage analytics, and cost monitoring, support high data quality standards, making it ideal for complex data ecosystems that require real-time oversight. While Atlan provides basic alerting and tracking, its limited approach to data quality monitoring makes Secoda the more suitable option for organizations seeking robust, end-to-end data quality management.
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