Metrics_ How to Improve Key Business Results - Martin Klubeck [44]
“The data doesn’t break out that way,” said the manager. “How am I supposed to know the types of cases each of you close?”
The employee replied, “Ask?” He was silent a moment. “If you had asked me or anyone else on the floor why I take longer to close cases and why I close fewer cases you’d find out a few things. I close fewer cases because I take longer to close my cases. The other analysts give me any cases that they can’t resolve. I get the hardest cases to close because I have the most experience. I am not slow, inefficient, or incompetent. Just the opposite. I’m the best analyst you have on the floor.”
The manager looked uncomfortable.
The employee continued, “So, tell me, what do you want me to change? If you want, I won’t take any cases from the other analysts and I’ll let the customers’ toughest problems go unresolved. Your call. You’re the boss.”
Needless (but fun) to say, the boss never bothered him about his time to resolve again. And luckily for all involved, the boss did not remain in the position much longer.
The only proper initial response to metrics is to investigate.
Earlier, I offered the view that metrics were not facts. You can give metrics too much value by deciding that they are facts. This is dangerous when leadership decides to “drive” decisions with metrics. This gives metrics more power than they deserve. When we elevate metrics to truth, we stop looking deeper. We also risk making decisions and taking actions based on information that may easily be less than 100 percent accurate.
Metrics are not facts. They are indicators.
When we give metrics some undeserved lofty status (as truth instead of indicators) we encourage our organization to “chase the data” rather than work toward the underlying root question the metrics were designed to answer. We send a totally clear and equally wrong message to our staff that the metrics are what matter. We end up trying to influence behavior with numbers, percentages, charts, and graphs.
The simplest example may be in customer satisfaction surveys. Even direct feedback provided during a focus group interview has to be taken with a grain of salt. And when we look at truly objective data, there is always room for misinterpretation. Objective measuring tools can have defects and produce faulty data.
Most times good managers (as well as good workers and good customers) know the truth without the data. Investigate when you see data that doesn’t match your gut instinct. Investigate when the data agrees too readily with your hunch.
One of the major benefits of building a metric the way I suggest is that it tells a complete story in answer to a root question. If you’ve built it well, chances are, it’s accurate and comprehensive. It is the closest thing you’ll get to the truth. But, I know from experience, no matter how hard I try there is always room for error and misinterpretation. A little pause for the cause of investigation won’t hurt—and it may help immensely.
Metrics Can Be Wrong
Since there is the possibility of variance and error in any collection method, there is always room for doubting the total validity of any measure. If you don’t have a healthy skepticism of what the information says, you will be led down the wrong path as often as not. Let’s say the check-engine light in your car comes on. Let’s also say that the car is new. Even if we know that the light is a malfunction indicator, we should refrain from jumping to conclusions. My favorite visits to the mechanics are when they run their diagnostics on my check-engine light and they determine that the only problem is with the check-engine light.
Perhaps you are thinking that the fuel-level indicator would be a better example. If the fuel gauge reads near empty, especially if the warning light accompanies it, you can have a high level of confidence that you need gas. But the gas gauge is still only an indicator. Perhaps it’s a more reliable one than the