Back to AI-Ready DataAI-Ready Data: Pillar 2
AI-Ready Data: Pillar 2Invalid Data Shouldn't
Reach Your Warehouse
One malformed record breaks a pipeline. Thousands corrupt a model. Catch them at the source.
What Is Data Qualification?
Validating data before it moves. Schema checks, business rules, anomaly detection, all at the source. Invalid data routes to dead-letter queues instead of breaking pipelines.
Based on Gartner's AI-Ready Data framework
The Three Parts of Qualification
Schema & Consistency
The problem
Data must conform to expected structures. A string where you expect an integer breaks everything downstream.
How Expanso solves it
Expanso enforces schemas declaratively. Non-conforming records route to dead-letter queues: they never reach your warehouse.
- Declarative schema enforcement
- Type checking at origination
- Dead-letter routing
Validation & Verification
The problem
Data must match expected patterns. A negative price or future birthdate indicates a problem.
How Expanso solves it
Expanso validates against reference data, lookup tables, and business rules. Anomalies caught before they propagate.
- Business rule validation
- Reference data lookups
- Anomaly detection
Observability & SLAs
The problem
You need to know when data quality degrades. Silent failures are the worst failures.
How Expanso solves it
Real-time visibility into data quality across your distributed footprint. Alerts fire when quality drops.
- Quality dashboards
- Threshold alerting
- Pipeline health monitoring
Problems This Solves
Bad Data Breaks Pipelines
Before
Schema mismatches cause 3am pages. Root cause analysis takes hours, bad data buried in millions of records.
After
Invalid data caught at source. Pipelines don't break. Bad records route to dead-letter queues with full context.
Fewer pipeline failures
Quality Issues Found Too Late
Before
Problems discovered days later during analysis. Bad data already in reports, dashboards, ML models.
After
Quality checked continuously. Issues caught immediately. Bad data never reaches downstream.
Issues caught at origin
No Visibility Into Data Health
Before
Data quality is a black box. Teams don't know there's a problem until something breaks.
After
Real-time dashboards across all streams. Alerts when quality degrades. Problems visible before damage.
Real-time visibility
How It Works
Define Quality Rules
Schemas, validation rules, quality thresholds - all in YAML. No custom code.
Validate Everywhere
Rules apply across all sources. Consistent qualification, managed centrally.
Route Failures Gracefully
Invalid data routes to dead-letter queues with full context. Fix at your convenience.
Qualification in Practice
/ Retail
Retail Customer Data
POS, web, mobile records validated at origination. Duplicates, nulls, invalid formats caught before CDP.
Cleaner data for personalization
/ Energy
Energy Sensor Readings
Sensor data validated against expected ranges. Faulty sensors identified before corrupting models.
Reliable predictive maintenance
/ Financial Services
Financial Transactions
Transactions validated for format, completeness, business rules. Invalid records flagged.
Clean data for compliance
Why Qualification Matters
A pipeline runs fine for months. Then a source system changes a field from integer to string. Pipeline breaks at 2am. Engineer paged. Hours spent finding one malformed record among millions.
This is qualification failure.
The Fix
The source system knew the format changed. But by the time data reached your warehouse, that context was gone.
Expanso validates at the source. Problems caught immediately. Bad data quarantined, not propagated.
Invalid data goes to dead-letter queues with full context: what rule it violated, where it came from, when it arrived. Debugging takes minutes instead of hours.
