Your teams spend weeks figuring out what fields mean. What if data arrived self-describing?
Based on Gartner's AI-Ready Data framework
Data needs meaning. A field called 'temp' could be temperature, temporary, or template ID.
Expanso adds semantic tags and business context at the source. Logs get parsed. Events get enriched. Data arrives self-documenting.
You can't improve what you don't measure. Quality issues caught late are expensive.
Expanso validates schemas, detects anomalies, and measures quality at the source. Bad data flagged before it moves.
Where did this data come from? What happened to it? Auditors ask. You need answers.
Lineage tracked from the source device, not from S3. Every transformation logged. Complete audit trail.
Logs arrive as raw strings. Engineers spend days figuring out what fields mean, what units are used, whether values are valid.
Data arrives with semantic tags, business context, documentation. Engineers start analysis immediately.
Bad data reaches your warehouse. Teams discover nulls, duplicates, type mismatches during analysis. Cleanup is expensive.
Issues caught at source. Invalid data routes to dead-letter queues. Only clean data reaches platforms.
Lineage starts when data lands in your warehouse. You can't prove what happened before. Auditors aren't happy.
Complete lineage from source to destination. Every transformation logged. Auditors get the full picture.
Lightweight agents run wherever data is created - edge devices, gateways, servers, sensors.
Declarative config defines context to add, quality checks to run, lineage to track. No custom code.
Downstream platforms receive data with context, quality metrics, lineage attached. No post-processing.
Raw sensor readings get machine IDs, timestamps, unit conversions, operational context at the source.
Patient telemetry tracked from device to archive. Chain of custody proven for compliance.
Events enriched with customer context, risk scores, regulatory flags at origination.