In 1847, Ignaz Semmelweis noticed something disturbing. Women giving birth in one maternity clinic at Vienna General Hospital were dying from childbed fever at dramatically higher rates than women in another. Between 1841 and 1846, nearly 2,000 mothers died in the clinic staffed by physicians and medical students—a mortality rate of roughly 10 percent.
Semmelweis began looking for differences between the two clinics. One stood out: physicians and medical students routinely moved between performing autopsies and examining maternity patients without washing their hands. He couldn’t explain exactly why this mattered. Modern germ theory was still about 14 years away. But he could see the pattern.
Semmelweis introduced a policy requiring medical personnel to wash their hands with a chlorinated solution before examining patients. In 1848, the first full year of the intervention, maternal mortality in the physicians’ clinic fell to about 1.3 percent. The evidence was compelling. The explanation wasn’t.
Semmelweis confronted a problem that organizations still face today: reality doesn’t become less real simply because we can’t explain it. He had strong evidence of a relationship without an adequate causal explanation for why that relationship existed. Sometimes the pattern arrives before the explanation.
Organizations encounter the same problem all the time. Customer behavior changes unexpectedly. Employee turnover begins increasing in one part of the organization. Sales decline in certain markets. Defects appear under seemingly unrelated operating conditions. Several variables begin moving together for reasons that aren’t immediately discernible. Our natural inclination is to ask why. That’s usually a good question. But sometimes we don’t have enough information to answer it yet.
When causation is beyond our reach, correlation can still move us closer to reality. This is where Exploratory Data Analysis becomes particularly valuable. Rather than beginning with an explanation and testing whether the data supports it, exploration begins by asking what the data might reveal. Look for correlations. Identify clusters. Search for anomalies. Use regression to discover relationships that warrant further investigation.
The objective isn’t to prove causation. It’s to discover patterns. A correlation matrix might reveal two variables that consistently move together. Clustering may expose groups nobody knew existed. Anomaly detection may identify observations that don’t behave like everything around them. None of these necessarily explains why something is happening. That’s okay. An unexplained pattern is still evidence.
Once an interesting pattern emerges, treat it as a provisional finding worthy of further investigation. Develop hypotheses. Gather additional evidence. Test possible explanations. Eventually, you may establish causation. But don’t require the explanation before you’re willing to learn from the observation. Reality is under no obligation to wait for our explanations.
What unexplainable phenomena are you dealing with in your organization right now? Don’t demand a causal explanation before you’re willing to trust what the evidence is telling you. Start by looking for patterns. Explore correlations. Identify clusters and anomalies. Ask what consistently moves together, even when you can’t yet explain why. Treat those patterns as clues, not conclusions. Sometimes the pattern arrives before the explanation.
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