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The greatest basis for fear is found in data that people believe addresses them at a personal level. This is why I recommend starting metrics programs at the effectiveness level.

When the data you are collecting (and later analyzing and reporting on) can be indicators about an individual, the fear factor becomes exponentially greater. It doesn’t even matter if you plan to use the data at an aggregate level, never looking at the individual. If the data could be used at the individual level, the fear is warranted.

Time to Resolve can be a good effectiveness measure, used to improve overall customer service. The purpose may be simply to achieve better customer service and, therefore, satisfaction. But if you fail to communicate this purpose, the root question, and how you will use the answers—the individuals providing the data will imagine the worst.

Be forewarned. Even if you have an automated system to collect the data (for example, the day and time the incident was opened and closed), the ones opening and closing the case in that system are still providing the data.

If the staff learns that you are gathering data on resolution speeds, they will “hear” that you are collecting data on how long it takes them to resolve the case. Not how long it takes the team or the organization to resolve most cases, but instead how long it takes each of them individually to do the work. And, if you are collecting data on an individual’s performance, the individual in question will imagine all of the worst possible scenarios for how you will use that data.

So your innocent and proper Time to Resolve measure, if unexplained, could create morale problems due to fear, uncertainty, and doubt.

If fear is born of ignorance, then asking for data without sharing the purpose makes you the midwife.

The boss says: “I only want the data so I know what’s going on.”

The worker hears: “I want data so I can see where you’re screwing up.”

The boss says: “I need the data so I can make better decisions.”

The worker hears: “I need data so I can decide who to fire, cut work hours from, or penalize.”

The boss asks: “What’s the problem with reporting on your performance? I should know about it, shouldn’t I?”

The worker wonders: “Yes, so why don’t you know about it? Why ask me for data about it? Why not just ask me? Why not spend some time with me?”

If you have spent any considerable amount of time in an organization with multiple layers of management, the preceding conversation may sound extremely familiar. Unfortunately, this conversation is not a unique one. I believe it (or something close to it) occurs in every workplace, every work day of the year.

We need to combat this common problem. To create a useful metric, you have to know, in advance of collecting the data, how the results (answers) will be used. It is essential for designing the metric properly and identifying the correct information, measures, and data. It is also essential if you want accurate data wherever human-provisioning is involved.

There is a simple enough test you can perform. Ask your friend, relative, coworker, or boss a simple, personal question. Better yet, ask your significant other a simple question like, “How much do you weigh?” Don’t do it yet. First read about the expected outcomes—before you get injured.

When you ask someone a personal question without offering any explanation as to the reason, one of the following five reactions will likely occur:

You are physically accosted. At least that’s what happened to me in the case when I asked my wife the same question.

You get data that accurately reflects what the data provider believes you want to hear.

You get data that accurately reflects what the data provider wants you to hear.

You get accurate data.

You get a question in return: “Why do you want to know?”

Most likely people will in turn ask you, “Why do you want to know?” It’s a natural reaction. Along with the expected response, you may also notice respondents