Over the past decade, organizations have poured immense effort into building systems that harness the power of big data and traditional analytics. These systems, spanning from dashboards to predictive models, have helped analyze historical data, detect patterns, and forecast future events. However, there's a fundamental problem with this approach: it stops at correlation. It can tell you what is likely to happen, but not why.
For the past decade, the AI and data science industry has been shaped by the dominant narrative of big data. From ad-tech to recommendation systems, the idea has been simple: more data leads to better models, so companies should focus on big data, scaling their infrastructure, deep learning, and tooling.
In the age of AI, every business leader hears the same message: use data to make better decisions. But most systems today still make brittle, black-box predictions without understanding the why behind the what. The reality? We're still in the early innings of AI-powered decision-making, and the biggest unlocks lie ahead.
Judea Pearl's Ladder of Causality has three levels: association (observing correlations), intervention (predicting effects of actions), and counterfactuals (imagining alternate outcomes). It's important because it shows that true causal understanding goes beyond data patterns, enabling reasoning about change and "what if" scenarios—essential for science, decision-making, and artificial intelligence.
The shift from correlation to causation isn't coming: it's already here. And the companies making it are seeing results that traditional data science can't match.