Chances are, you don’t understand averages like you think you do.
There’s a great t-shirt out there that reads, “There are 10 types of people in this world. Those that understand binary, and those that don’t.”
If you don’t get the joke, ask any computer nerd to explain it to you. Data, similar to this joke, can be misunderstood.
You have to understand the basics of data before you can really understand the meaning.
Averages – helpful or harmful?
The world is ruled by averages. Think about the majority of metrics that are posted in your news feed. Every day we discuss average temperatures, average salaries, average speeds, average life expectancy, average prices, etc.
While averages can be meaningful, they also can be extremely misleading. If you are making decisions based off of an average, you better know what the data really says.
Mistake #1: Not knowing what your average is measuring
An average can be viewed as a guide to how well a process is performing. The more uncontrolled variables in the mix, the less likely the average will be a good predictor of the process.
For example, if you were measuring the process for how long you were going to live (average life expectancy), you would get a number like 78.7 years for a male living in the United States. But as we all know, not all U.S. males will live to exactly 78.7 years old. There are just way too many variables that control whether or not that is accurate for any single individual.
If, on the other hand, you were measuring something with far fewer variables – say, the time it takes a patient to see a doctor in an emergency room (or any process within your business) – you might be tempted to use an average as a good indication for how the process is working. You would like to think of that average as the amount of time any person can expect to wait if they are coming into the emergency room.
But that’s not enough.
For your data (the average door to doctor time) to be meaningful, the process has to be stable. It has to be performed consistently and reliably. If one nurse performs their triage by asking the patient 3 quick questions, but the next nurse performs their triage by asking 15 detailed questions… what does your data look like?
Your average will be a misinterpretation of what is really going on: 2 different processes. (For more on defining your process, read: Every business owner must know THIS one thing to be successful )
When your average is based on multiple processes, the reliability of that data goes down. Any decision you make will be based on faulty information.
Mistake #2: High or low performers
Once you are sure that everyone is performing the process in the same way, then the next step is to look deeper into the data. (A simple tool like a histogram can help.)
Going back to our emergency room example, if you know that your average door to doctor time is 60 minutes, and you want to decrease that time, what do you do?
If you only rely on your average to guide you, you might be working on the wrong thing.
Often, leaders will work on motivating employees, micro-managing them, provide incentives to get them to “work harder”, or even change the process all-together.
But what if that average is misleading? What if, instead of poorly motivated nurses, or a poorly developed process, the department instead had 3 nurses who were struggling with the new computer system? Every time they triage a new patient, it takes them at least three times as long to document their questions appropriately.
And what if these three nurses skew the data dramatically?
As a leader, you may have just wasted your money giving an incentive to the other employees that don’t need it. You may have yelled at the group, disengaging them, to get them to “work harder.” Perhaps you changed a perfectly good process, and now the staff have to get used to a new one (again).
Instead, all that was really needed was for you to address these three nurses in some meaningful way (additional training, change their assignments, etc.) That’s on you.
Know your outliers in your averages and see if there is a pattern.
Work smarter, not harder
The phrase “work smarter, not harder” applies to leadership as well. When leaders don’t understand data correctly, they put undue pressure on the entire organization and make decisions that are fruitless. Averages, while important, are just the tip of the data iceberg. If your main data focus is on averages, and you don’t know what is under the surface, you are the Titanic waiting to sink.
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This is the second of a 4 part blog series on data, its problems, and how to use it to get more value out of your business. It will be followed up with our very first podcast – Process Pitches – where we will be inviting Six Sigma/Lean/data experts and business leaders to share some of their stories from the business world about the struggles leaders have understanding data to its fullest.Start Now