Key metrics are automatically displayed at the asset level. This helps teams proactively monitor and address data issues before they affect downstream processes or analytics. The “Health” tab at the asset level displays real-time data quality checks, such as freshness, whitespace count, non-empty count, positive count, value range, min, inner space, duplicate, space count,standard deviation. At the asset level, depreciation and warnings are also displayed based on the issues and alerts in DQLabs for the respective asset.

Column-level data quality metrics such as DQ score, total rows, passed rules, total rules, failed rules, valid rows, invalid rows, total alerts, total issues ensure granularity, making it easier to pinpoint specific areas for improvement, thereby improving precision in data cleansing efforts.

The integration highlights upstream issues, helping teams trace problems back to the source and ensuring a more efficient root-cause analysis. This transparency accelerates the resolution of issues and maintains trust in data.

The integration leverages Alation’s catalog update jobs to automatically push DQLabs metrics into Alation and vice versa. The workflow is streamlined through automated updates, eliminating manual data transfers and reducing human error.

By merging metadata and data quality metrics into a single platform, teams no longer need to jump between different tools to get a holistic view of the data’s health. The metadata pulled from Alation is displayed under semantics for domains and tags. This consolidated view improves efficiency, speeds up decision-making, and allows for more effective collaboration between teams. Custom fields and custom sections allow teams to tailor the data quality metrics to their specific needs. Whether it’s focusing on particular attributes or prioritizing certain data issues, teams can easily modify views to match their business priorities.

By integrating DQLabs data quality metrics directly into Alation, both technical and business users can collaborate more effectively. Data stewards can focus on data quality remediation, while business analysts can continue their work on data consumption without being bogged down by data quality concerns. Analysts and data stewards can gain contextual insights into data quality metrics right alongside the asset or column metadata in Alation. This shared visibility fosters communication and helps cross-functional teams work together to maintain high-quality, trusted data.

As businesses scale their data operations, the integration allows for seamless management of large datasets across multiple domains. Since metrics are automatically updated and pushed into the catalog, there’s no need for additional resources to handle data quality checks as the volume of data grows.