Bigeye Expands Data Quality Capabilities with Three New Features

As data observability matures, enterprises need platforms that can handle the complexity of real-world data environments. Critical information lives across dozens of systems: modern cloud warehouses alongside Oracle databases and Informatica jobs that have powered operations for decades. The data quality rules and governance frameworks built over years represent institutional knowledge that can't be abandoned as organizations embrace observability and modernization.

The challenge isn't choosing between old and new. It's making comprehensive data observability work across this distributed landscape. Bigeye's three new capabilities: customizable data quality dimensions, join rules, and asset linking, solve for this by enabling enterprises to leverage existing rules and processes while embracing the automation and intelligence that modern data observability provides.

Introducing three new capabilities

These features enable enterprises to embrace the benefits of data observability without losing the value of their existing body of data quality rules:

• Customizable data quality dimensions allow customers to tailor how data quality is defined and reported within their organizations
• Join rules support creation and validation or rules across multiple tables and databases, capturing data quality issues that only emerge in relationships between datasets
• Asset linking associates specific metrics and rules with individual columns, enabling precise reporting on critical data elements

Custom data dimensions: Align with your governance framework

Organizations have specific data quality frameworks that reflect their industry requirements and compliance needs. A financial services company's approach to categorizing data quality differs significantly from a healthcare organization's taxonomy.

Bigeye's customizable data quality dimensions let you organize metrics, rules, templates, and deltas using the categories your team actually relies on, whether that's Freshness, Validity, Uniqueness, or dimensions specific to your governance model.

While the Bigeye platform includes standard dimensions such as pipeline health, completeness, uniqueness, validity, and distribution, enterprises can now configure which metrics and rules roll up into each category.

This flexibility helps teams ensure that data quality reporting aligns with existing governance workflows and integrates seamlessly with tools like data catalogs and Snowflake Shares.

Join rules: Monitor across your entire data ecosystem

Sometimes, data integrity issues emerge only when examining relationships between datasets. A customer record in your CRM needs to match billing data in your ERP. Financial transactions flowing from Oracle to Snowflake must maintain referential integrity throughout the journey. 

Join rules enable validation of data consistency across joined tables, even when those tables span different databases. Using custom SQL logic applied to combined datasets, you can create sophisticated integrity checks that address enterprise complexity. The capability supports all join types (LEFT, RIGHT, INNER, FULL OUTER) with column filtering and table aliasing to make SQL writing more efficient.

Whether you need to verify that approved transactions have corresponding ticket numbers, ensure currency amounts match between source and target systems, or validate that customer names are consistent across platforms, join rules provide the flexibility to enforce complex business logic across your entire data ecosystem. This cross-source validation capability also addresses a current gap in the market: other similar data observability tools don’t offer these capabilities.

Asset linking: Report on what matters most

When monitoring virtual tables or complex transformations, it can be difficult to report on the specific physical columns—your critical data elements (CDEs)—that actually matter to the business. CDEs are the important columns that enterprises need to track for governance and compliance, but they're often obscured when monitoring happens through virtual tables or transformed datasets.

Asset linking enables explicit mapping of metrics to the physical columns they're protecting. A rule running on a virtual table can now be reported against the underlying CDE it's monitoring, solving the disconnect between observed metrics and the physical columns they represent.

The feature provides intelligent default linking based on metric configuration, with manual override options when needed. Integration with Alation's Health tab means that when you link an asset to a metric, that metric appears on the asset's Alation Health tab. A new DIM_LINKED_COLUMNS table in Snowflake Share enables effective reporting on CDEs, ensuring that linked columns appear in the right places for comprehensive governance reporting.

Real-world validation

These capabilities weren't developed in isolation. A Fortune 100 financial services company recently made this exact transition, migrating thousands of rules from IBM Information Analyzer to Bigeye. The reason? They needed a platform that could handle their complexity while integrating fully with their existing data stack.

This migration demonstrates the proven path for large-scale enterprise adoption, maintaining existing governance frameworks while gaining the automation, scalability, and intelligence that modern data teams need. Bigeye offers comprehensive migration support through professional services to help enterprises make similar transitions seamlessly.

Enterprise ready foundation

While many data observability platforms focus exclusively on the modern data stack, Bigeye addresses the full enterprise reality. Our extensive legacy connector support includes enterprise systems that the biggest companies in the world still use every day: Oracle Database, Microsoft SQL Server, MySQL, PostgreSQL, Informatica PowerCenter, IBM DB2, IBM DataStage, Microsoft SSIS, SnapLogic, Qlik Replicate, and dozens of others.

The roadmap continues expanding this coverage with additional OLTP systems (AlloyDB), OLAP platforms (Vertica, IBM Netezza, SAP HANA), ETL tools (Azure Data Factory, Talend), and business intelligence platforms (IBM Cognos, MicroStrategy, SAP Business Objects, Qlik Sense). This comprehensive connectivity ensures enterprises can monitor data quality across their entire technology stack, not just the modern components.

This approach contrasts with other tools that race to add minimal support for trending technologies but rarely provide the deep integration enterprises need for their established systems. 

"Enterprises don't want to throw away years of investment in data quality practices just to modernize," said Kyle Kirwan, Chief Product Officer at Bigeye. "With these new capabilities, Bigeye makes it possible to carry forward existing rules and processes while also unlocking the benefits of advanced observability."

Ready to see how these capabilities can modernize your data quality approach? Get a demo to explore the new features, or contact our professional services team to discuss migration assistance for your existing data quality rules and processes.