Monte Carlo asks if the data is broken. FactorPrism asks why the number moved.
Data observability and business-metric diagnosis are different jobs, and they hand off cleanly. Rule out a broken pipeline first; then find the factors behind a real change.
Ready for your own data? Install from Snowflake Marketplace
The health of your data
Monte Carlo is a leading data observability platform. It detects freshness, volume, schema and quality anomalies, traces incidents through lineage to where the pipeline broke, and warns you before a broken table corrupts a dashboard. If the question is "can I trust this number?", Monte Carlo is built for it.
A different questionWhen the data is right and the number still moved
"Revenue is down six points and the data is accurate. What drove it?" is not a data-quality question. FactorPrism answers it: the factors behind the change across your dimensions and their combinations, each placed where it acts, adding up to the change exactly, inside your Snowflake account.
| Monte Carlo RCA | FactorPrism | |
|---|---|---|
| Category | Data observability, incident root cause | Business-metric factors |
| Core question | Is the data broken? Where in the pipeline? | The data is right. Why did the number move? |
| Output | Incident, lineage, location of the break | A Finding: ranked factors, where each acted, how much each added |
| Adds up to the metric's change | Not its job | Yes, exactly |
| Combinations (region × product × channel) | Not applicable | Yes |
| Where it runs | Connects to your stack | Snowflake Native App, read-only on one object; data never leaves your account |
Two stages of one investigation
Monte Carlo clears the data
No freshness or quality incident behind the move.
FactorPrism explains the move
Which factors drove it, where, and how much, reconciled to the total.
The owners act
Each factor goes to the people who run that part of the business.
Related: All comparisons · Snowflake built-ins · Sisu alternative
Honest answers
If Monte Carlo says my data is healthy, do I still need FactorPrism?
Yes — that's exactly when you need it. Healthy data that still moved is a driver-attribution problem. FactorPrism tells you which factors and intersections account for the change, reconciled to the total.
Does FactorPrism do data-quality monitoring?
No. FactorPrism assumes the data is correct and explains why the business metric moved. For pipeline health and incident detection, a data-observability tool like Monte Carlo is the right fit.
Healthy data, unexplained move?
That is exactly when FactorPrism helps. Bring one question to a guided evaluation.