New 2025 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions - Download Report

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Ensure Effective Data Quality Management in Collibra

Overview

Collibra Data Catalog is a centralized metadata repository solution designed to help organizations discover, describe, assemble, and govern their datasets. It creates an inventory of all data sources and provides detailed metadata about each, allowing users to easily find and trust the data they need to make informed business decisions. The metadata from the datasets provides essential information such as the structure of the data, asset creation dates, source details, column attributes, data formats, data ownership, data classifications, etc.

Integrating the Collibra catalog with DQLabs empowers organizations to make data-driven decisions that boost operational efficiency. By seamlessly pulling and pushing metrics between Collibra and DQLabs, users gain real-time insights into data quality, allowing stakeholders to quickly address issues and drive better business outcomes. This integration ensures that key metrics such as data quality scores, rows passed/failed, total rows, thresholds, results, and asset run completion dates are readily available in a central catalog. It enhances governance by enabling stakeholders to track, evaluate, and enforce data quality standards throughout the data lifecycle. By syncing DQLabs' data quality metrics with Collibra, organizations ensure their data governance framework remains comprehensive, accurate, and up-to-date.

Data Quality and Observability for Collibra

Centralized Visibility

By pushing metrics and data quality insights directly from DQLabs into Collibra, organizations ensure that data quality metrics (such as score, rows passed/failed, total rows, threshold, result, asset run completion date) are available in a central catalog. This facilitates better governance, where stakeholders can track, assess, and enforce data quality standards at every stage of the data lifecycle.

Automated Data Quality Updates

With bi-directional integration, DQLabs can automatically update Collibra with metrics on data quality after each catalog job runs. This reduces the manual effort required to maintain up-to-date data quality information in the catalog, improving operational efficiency.

Data Lineage and Traceability

Mapping metrics to the appropriate Collibra assets (using database names, schema names, table names, column names, and measure names) helps create a transparent lineage. Users can trace the quality of data back to its source and understand how issues in upstream systems affect downstream assets.

Custom Fields for Specific Data Quality Needs

The ability to create custom fields for measures in Collibra means that organizations can tailor the data quality metrics to their specific requirements, capturing all the necessary dimensions of data quality, such as score, thresholds, and run completion dates.

Contextual Data Quality Insights

DQLabs integrates directly into the Collibra catalog allowing users to access both governance metadata (like terms and domains) and data quality metrics within the same interface. This allows users to gain insights into the health of data in real-time, ensuring data is both governed and trusted.

Tailored Field Mappings

During the integration, users can customize which DQLabs metrics to push and pull to Collibra. This flexibility ensures that organizations can align the integration with their unique business needs, selecting the most relevant fields for their use cases.

Real-Time Metrics Synchronization

The integration ensures that as new data quality measures are created in DQLabs, they are automatically pushed to Collibra, keeping the catalog’s data quality information up to date. For larger organizations, this real-time synchronization is crucial to maintaining a high level of data quality at scale.

Better Semantic Layer Management

The integration also allows the pulling of semantic layer definitions such as domains and terms from Collibra into DQLabs. These pulled definitions become available under the respective pages, giving users a clearer understanding of how data quality is measured and managed within the broader semantic context.

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