Data Analysis Mistake: Common sense may result in 180-degree wrong causation judgment
As business analysts and entrepreneurs, we pride ourselves on making data-driven decisions. However, a critical mistake we can fall into is just before we want to find the last missing link in our logic to the root cause, we use our common sense instead. This can lead to disastrous consequences, as illustrated by a personal experience that changed my approach to problem-solving.
For years, I suffered from acid reflux, assuming it was caused by excess stomach acid. I diligently followed the advice to take antacids, finding temporary relief but ultimately worsening my condition. Surprisingly, It turned out that the real underlying cause was insufficient stomach acid, causing the esophageal sphincter (the valve between the stomach and esophagus) to malfunction!! Addressing the root cause – increasing stomach acid production – ultimately solved the problem. Sometimes it's easy ("Common sense") to connect what we believe is the final link to the symptom, not knowing that this is the exact place, we tend to make our biggest mistakes. (Please do not take this as medical advice; it was just my own experience. Consult your doctor if you have a similar issue :D )
Another example for this mistake is the common belief that slowing down always minimizes car damage from potholes is a misconception rooted in everyday physics. In reality, decelerating can shift the car's weight forward, stressing the front suspension more upon hitting a pothole. Interestingly, maintaining or slightly increasing speed might allow the car to better navigate the pothole, minimizing damage due to the dynamics of the car's suspension and momentum principles. This highlights the intricate relationship between vehicle speed, suspension mechanics, and road conditions.
This experience underscores the importance of understanding the system and its dynamics before jumping to conclusions. In business analysis, this translates to a thorough investigation of the entire system, not just isolated observations.
Let me illustrate this with a client example. A company I advised attributed their lack of contract wins to a shortage of leads. Their solution? Invest heavily in marketing to generate more leads. However, a deeper analysis revealed a different story. The issue wasn't lack of quantity, but exactly the opposite.
An overabundance of leads overwhelmed the sales team, leading to inefficient allocation and neglected high-potential leads. Instead of increasing leads, the solution lies in implementing lead filtering to attract more qualified leads and optimizing internal processes to ensure the effective handling of incoming opportunities.
I highly suggest reading this article from HBR for more examples of the correlation/causation issue:
Also, suggest this book summary, "The Flaw of Averages":
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My suggestions for startup founders and managers:
1- Moving beyond surface-level data: Don't rely solely on readily available data points. Dig deeper to uncover the underlying mechanisms and relationships between various components of the system.
2- Challenging assumptions: Don't accept initial observations as the sole truth. Question every assumption and actively seek evidence to validate or disprove them.
3- Considering the entire ecosystem: Analyze the interconnectedness of different elements within the system and how they influence each other.
4- Embrace Counterintuitiveness: Be open to solutions that may initially seem contrary to conventional wisdom and go beyond common sense, because that's where we might ignore a missing link in our logic.
By understanding the system and its internal dynamics, we can make informed judgments, avoid costly mistakes, and ultimately deliver real value to our clients. Remember, the devil is often in the details, and true insights lie in a holistic understanding of the intricate workings of the business ecosystem.