Validate, verify, and ensure consistency across all data streams automatically.
Data qualification ensures every data stream meets defined standards for consistency, validity, and reliability before reaching your platforms. Without qualification, invalid data causes downstream failures and inaccurate AI models.
How Expanso implements each Gartner capability
Ensure data conforms to expected schemas and business rules.
Declaratively enforce schemas and business rules on any data stream. Non-compliant data routes to dead-letter queues at the source.
Check data against known good states and expected patterns.
Real-time validation against reference data, lookup tables, and historical patterns. Catch anomalies before they propagate.
Monitor pipeline health and data quality metrics continuously.
Monitor pipeline health, throughput, and errors across your entire distributed footprint from a single pane of glass.
Schema mismatches, null values, and type errors cause pipeline failures. Data teams get paged at 3am.
Validate schemas Invalid data caught immediately and routed to dead-letter queues for review.
Data quality issues discovered days or weeks later. By then, bad data has corrupted reports and AI models.
Real-time observability into every data stream. Quality metrics tracked from source to destination.
Same entity (customer, product, transaction) has different representations across sources. AI models see conflicting data.
Enforce consistency rules at origination. Standardize formats, units, and representations before data moves.
Set schemas, validation rules, and quality thresholds in declarative config. No custom code.
Rules enforce automatically across thousands of distributed sources. Consistent qualification everywhere.
Invalid data doesn't break pipelines - it routes to dead-letter queues for analysis and correction.
Validate customer records from POS, web, and mobile apps at origination. Catch duplicates and nulls before warehouse.
Verify sensor readings against expected ranges. Catch faulty sensors before bad data affects predictive maintenance models.
Validate transaction formats and amounts at source. Ensure consistency across payment systems.