The breakthrough AI that finds the simplest truth hidden in complex data.
When your metrics change, there are always multiple possible explanations. The hard part isn't finding correlations—it's figuring out which factors are actually responsible.
Every data point belongs to multiple groups simultaneously. A sale in "East Region" is also part of "All Regions," making it impossible to cleanly separate causes.
Your product is part of the market. If you drove growth, traditional methods count it twice—once as "market" and once as "your product."
When sales of Jewelry in the South region double, it could be explained by any of these factors:
The challenge: South-Jewelry belongs to all of these groups simultaneously. Traditional analytics can't disentangle which factor is actually responsible.
FactorPrism® uses advanced AI techniques drawn from signal processing and compressed sensing to solve this problem. Our proprietary algorithms automatically find the simplest set of factors that best explains your data—separating overlapping effects and eliminating double-counting.
The result: you get a clean decomposition where every driver is counted exactly once, the total adds up correctly, and the explanation is as simple as the data allows. No manual analysis, no spreadsheet gymnastics, no guesswork.
Compressed sensing is a breakthrough mathematical technique that revolutionized fields like medical imaging and digital photography. It's what allows MRI machines to create detailed scans in a fraction of the time, and how your smartphone can take sharp photos in low light.
The core insight: when you're looking for the truth hidden in complex, noisy data, the simplest explanation that fits is usually correct. This is Occam's Razor, implemented mathematically.
FactorPrism® applies these same principles to business analytics. Instead of reconstructing images from sensor data, we're reconstructing the story of what drove your metrics to change. The math finds the simplest true explanation either way.
This approach solves problems that traditional analytics fundamentally cannot.
Credit goes to the right level of the hierarchy
Overlapping factors are properly disentangled
Prefers fewer, more powerful explanations
Works with thousands of factors
A retail company saw revenue grow 12% year-over-year. The question: was this a company-wide trend, or did specific regions drive it? Traditional analysis and FactorPrism® gave very different answers.
FactorPrism® doesn't just correlate—it decomposes. By finding the smallest set of non-overlapping factors that explain your data, we reveal the true drivers that traditional analytics systematically miss or misattribute.
Try FactorPrism® free on Snowflake Marketplace and see what's really driving your business metrics.
Get it on Snowflake Marketplace