Accelerate Data Quality & Profiling

EIIG performs full-scan profiling and quality analysis in real time using a federated approach and central cache. Unlike traditional tools that rely on limited samples or resource-intensive full-table scans, EIIG provides the most accurate, timely view of data quality. It rapidly detects root causes without expensive computational overhead or impact on source systems.

Highly scalable yet nimble, EIIG’s solution has a minimal infrastructure footprint and zero impact on production systems. It eliminates traditional ETL by leveraging federation and a universal field dictionary to consolidate sources a virtual data fabric. Data teams can independently assess data freshness, completeness, timeliness, consistency, and accuracy—without extensive resource demands or cross-team approvals.

EIIG enables organizations to detect, correct, or remove inaccurate, incomplete, inconsistent, duplicate, or irrelevant data. Its write-back capability helps remediate quality issues at the source. This improves analytical accuracy, reduces operational errors, builds data trust, and supports AI readiness by minimizing noise and bias.

EIIG’s quality solution offers Dynamic Masking to allow analysts to profile and validate datasets without exposing sensitive information. By masking data only at access time, EIIG preserves the structural integrity and patterns needed for accurate quality checks—while ensuring PII and other confidential fields remain protected. This enables safe, role-based collaboration, reduces the risk of managing duplicate masked datasets, and supports privacy-by-design compliance.

EIIG uniquely combines data and metadata in a single virtual fabric. It ingests metadata from 70+ technologies, feeding it to the quality engine for profiling and analysis. The resulting metrics are then pushed to EIIG’s metadata platform, which features field-level lineage and a dynamic data catalog. This native integration enables more efficient delivery of trusted, governed data.

EIIG’s quality engine constantly profiles, analyzes, and cleanses data. Simultaneously, the metadata platform incorporates quality metrics, assigns scores, and continuously monitors for changes. The system automatically visualizes changes—such as pre- and post-transformation states—in its lineage graph and provides threshold-based alerts, empowering users to quickly identify and correct issues.