Fig Blog
Insights on causal analysis, business intelligence, and making better decisions with data.
Why Your BI Dashboard Can't Tell You Why Revenue Dropped
Dashboards show what happened. They can't tell you why. Here's how causal analysis closes the gap between seeing a problem and understanding it.
The 12 Analysis Algorithms Every Data Team Needs (And Shouldn't Build Themselves)
From Root Cause Analysis to Predictive Forecasting, here are the 12 algorithms that turn raw data into business decisions — and why building them in-house is a trap.
Semantic Layer vs Knowledge Graph: What Data Leaders Need to Know
A semantic layer ensures everyone agrees on what a metric means. A knowledge graph maps how metrics drive each other. Here's why you need both — and what becomes possible when you have them.
Concentration Risk: The Business Metric You're Probably Ignoring
If 3 customers account for 40% of your revenue, you don't have a revenue number — you have a risk profile. Here's how to measure and manage concentration across your business.
How to Set Up AI-Powered Metric Monitoring in 5 Minutes
Step-by-step guide to connecting your data, defining monitors, and getting automated root cause analysis every time a metric deviates — not just an alert, but an explanation.
From Correlation to Causation: Why AI Analytics Needs a Knowledge Graph
Most AI analytics tools find patterns in your data. Without a causal model, those patterns are just correlations that can mislead as easily as they inform. Here's what's different about causal AI analytics.