uplyst.Know when the evidence behind a decision changes
Help data and operations teams understand which decisions are affected when a source becomes late, incomplete or inconsistent.
Find the affected decisionDispatch source · freshness review
The connection is back.
Is the evidence complete?
- Transport status
- Restored at 09:12 UTC
- Last confirmed dispatch
- 09:02 UTC
- Affected interval
- 09:02–09:12 · reconciliation pending
- Dependent consumer
- Customer commitment review
See which decisions a data issue affects
A late table, an unexpected null rate or a schema change can affect several models and workflows. Data teams need to find those dependencies, while business users need to know whether the information is still suitable for their decision.
Data observability connects technical checks to business definitions, owners and consumers. The affected process and its urgency help teams prioritize the investigation.
Set expectations for each workflow
A monthly close and an inventory allocation have different requirements for current data. Expected update frequency, coverage and valid values define what each workflow needs from its sources.
Source health, pipeline status and content checks answer different questions. A successful job can publish incomplete records, and an active connection can carry no new business activity. These distinctions help teams investigate the actual problem.
Trace the issue to the people affected
When a check fails, identify the source, transformation and business concepts involved. Follow the known dependency path to affected measures and workflows. Notify the accountable data owner and give business users a clear status at the point where they would otherwise rely on the information.
| Observation | Useful context |
|---|---|
| Freshness breach | Which decision needs a more recent view? |
| Schema change | Which transformations depend on the changed field? |
| Quality exception | Which business rule defines a valid record? |
| Source outage | Which consumers have an approved fallback? |
Trace the source condition to the business consumer.
Source dependencies
- SourceDispatch events
The missing interval belongs to the physical dispatch process and its source owner.
- Business conceptOrder fulfillment
Event identity and timing determine which orders have a supported dispatch status.
- ConsumerCommitment review
Customer operations needs a visible evidence status before using the affected order view.
Follow corrections through downstream work
A source correction can require replay, recalculation or another review of decisions made during the incident. The affected period and the consumers of the original data establish which work needs attention.
Metrics, logs and traces help locate system failures. Data quality rules and business definitions explain how those failures affect the information used by a team. Both are needed to confirm that recovery is complete.
Restore the source.
Confirm transport, delivery and the affected interval. Reconcile replayed event identifiers before processing.
Revisit the decision.
Identify who used the original values and whether a corrected record requires a new business review.
Check that recovery reaches every dependent workflow
Delayed updates, changed fields and partial deliveries can leave dependent workflows using different versions of a record. Recovery needs to account for the original consumers and avoid repeating actions when corrected data is replayed.
Time to locate affected users, unresolved ownership and incomplete recovery records show where incident handling needs improvement. Tracking these measures keeps observability connected to the work that relies on the data.
Discuss your priorities with us.
- Prioritize data issues by business dependency
- Explain freshness and quality at the point of use
- Record the impact and recovery of a data incident