Use case · Data observability platformExpose data failures before they break decisions
Monitor data in real time, detect issues early, and keep analytics, AI, and operations running on trusted data
What teams actually miss
Data failures surface too late
A typical data incident timeline
- 00:00Data pipeline runs
- 00:14Schema changes upstream
- 02:30Events arrive late
- 08:10Dashboard numbers drift
- Day 2Business flags inconsistency
How Expanso interrupts the timeline
Incidents get shorter because discovery happens earlier
- Detects freshness, volume, and schema drift in real time
- Flags anomalies before dashboards update
- Preserves source and timing context automatically
- Pinpoints where behavior changed, not just that it did
Measurable outcomes
Real impact across analytics, AI, and operational systems
Faster detection of data failures across pipelines and streams
Data reliability for analytics, AI, and operational systems
Real-world impact
See how leading organizations achieve data observability at scale
When 150 ms is too slow
A major North American sports league lacked visibility into real-time player tracking pipelines. Silent data delays caused live graphics to lag behind gameplay. Expanso processed tracking data locally at each stadium, cutting graphics latency from 150 ms to 8 ms.
Zero live graphics outages across the season
Read full case study →12 million events, 4 analysts
A European OEM struggled to observe telemetry quality across 2.3M connected vehicles. Data noise masked real security threats. Expanso monitored telemetry at the source, detecting anomalies and validating events before alerts reached the VSOC.
847 actionable alerts instead of 12M
Read full case study →Turning 14.3 TB of logs into trustworthy signals
A top-25 US regional bank lacked visibility into log quality. Noisy data overwhelmed Splunk, and real issues surfaced late. Expanso monitored logs upstream, flagging anomalies, masking sensitive fields, and validating structure before ingestion.
4.1× faster security incident triage
Evaluate Splunk volume →Their AWS bill was higher than their drone fleet
A forestry company processed 2.7 TB/day of drone imagery without visibility into pipeline health. Clients waited 48–72 hours for maps. Expanso monitored extraction and processing locally, detecting data gaps and delays before cloud transfer.
99.4% of data stays local
Read full case study →Why Expanso
Observe anywhere
Cloud, on-prem, edge, and hybrid environments
Works with your stack
Integrates with existing tools without lock-in
Policy-driven monitoring
Rules replace manual checks. Observability scales without complexity
Built for real systems
From dozens to thousands of pipelines without increasing team size
Frequently asked questions
What is a Data Observability and Monitoring Platform?
It provides real-time visibility into data pipelines, streams, and quality, detecting failures and anomalies before they impact analytics or operations.
How is this different from logging or metrics tools?
Expanso observes the data behavior itself, including volume, structure, freshness, and validity, not just infrastructure signals.
Can Expanso detect silent data failures?
Yes. Schema drift, missing data, late arrivals, and partial failures can be detected upstream in real time, based on the checks you configure.
Where does Expanso run?
SaaS, on-prem, edge, or hybrid, with consistent observability and policies everywhere.
Does this replace my existing monitoring tools?
No. Expanso complements them by covering data-specific failures that traditional monitoring misses.
