Every decision maker should understand the power and pitfalls of averages. Averages are a commonly used statistical indicator used in decision-making, but they can be both a powerful ally and a deceptive trap. Understanding when to use averages and when to dig deeper into data is crucial for effective decision-making to improve your business.
Averages smooth out variation — For better or worse
Averages help to smooth out variations in data, which can be beneficial or harmful, depending on the type of variation. When variation represents meaningful signals, —such as shifts in customer preferences or performance trends—, relying solely on averages can lead to inaction and missed opportunities. For example, if customer satisfaction scores are fluctuating, averaging them might obscure low-level cases that require immediate intervention. It also hides high-level cases to learn from.
Conversely, when variation is merely noise, —such as random fluctuations due to measurement imprecision—, averaging is a valuable tool to avoid knee-jerk reactions. Consider quality control in manufacturing: if every slight variation in machine output prompted an adjustment, inefficiencies would skyrocket. Statistical methods like Control Charts and Measurement System Analysis (MSA) help distinguish between meaningful variation (special cause) and noise (common cause), allowing decision-makers to act appropriately.
Data-Driven Decision making: the right approach
Bad decision-making is a major source of waste in organizations. While data-driven decision-making is vital, an over-reliance on averages and aggregated data can lead to costly mistakes.
One of the biggest issues is assuming data follows a normal distribution when it often does not. Many business metrics are skewed or contain outliers, making averages and standard deviations misleading. Imagine analysing customer spending: the presence of a few high-spending customers can inflate the average, creating a false sense of overall profitability.
Balancing the power and pitfalls of averages
While averages can simplify complex data and offer useful benchmarks, decision-makers must be cautious. The key is not to abandon averages but to complement them with additional statistical measures and visualizations. Examining data quality, identifying outliers, and understanding the actual shape of the data distribution ensure a more complete understanding of the situation.
By recognizing when averages help and when they hinder, decision-makers can make more informed, effective choices—leading to better business outcomes.
Same data, new insights
Argon&Co consultants are operational experts with extensive senior leadership experience. We are specialists in “looking differently at data”, while ensuring business relevance. We have supported leaders at various levels in many organizations in re-reviewing their own data, almost always resulting in surprising new insights and business opportunities. If you would like to learn more, feel free to contact us.
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