Your predictive maintenance system catches failures—but it also generates dozens of false alarms. Your maintenance teams are skeptical. The ROI is unclear. A new benchmark study of 10,000 CNC machines reveals why: traditional ML optimizes for statistical accuracy instead of business outcomes.
The cost? Approximately $80,000 per 2,000 machines annually.
You sometimes hear: "Data show more people die from the smallpox vaccine than from smallpox itself, so vaccination should be banned."
This is a misinterpretation of the data if you only compare totals. The mistake is ignoring considering counterfactual reasoning.
We live in the "data economy," with dashboards, models, and petabytes to give us insights. And yet data is dumb. Left to its own devices, raw data can't tell you why anything happens. It can tabulate, correlate, and predict (sometimes) with accuracy, but data does not understand causes and effects like humans do.
A recent WSJ op-ed reflects exactly what we see from Causify's customers.
For nearly a century, business leaders have relied on a simple rule: scale drives efficiency. In 1936, aeronautical engineer Theodore Wright showed that costs fall predictably every time production doubles. Experience compounds through repetition — build more, learn more, spend less. That logic built the modern world.