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Data: 15 and 35

Measures: 15 mpg and 35 mpg

Information: Miles-per-gallon achieved using unleaded gasoline in a compact car: 15 mpg in the city, 35 mpg on the highway

Metric: The metric that would logically follow would be a picture (charts or graphs in most cases) that tells a story. In this case the story may be a comparison between the fuel efficiency of different compact car models (miles per gallon), combined with other indicators used to select the right car for you.

Root Question: What is the best car for me?

The use of data, measures, and information are more relative than hard and fast. I don’t mean to dictate inflexible definitions that will keep you from getting to the metric. The goal is to develop metrics—answers—to our questions.

The data could include the miles-per-gallon tag. Measures could include “in the city” and “on the highway.” Information could distinguish between the various cars’ make and model. The major point to take away is that additional meaning (and context) are provided as we progress from data to measures to information. Also, metrics make a full story of this and much more information.

Let’s look at another example, illustrated in Figure 1-7. Using a customer service desk as our model, we can identify each of the components listed.

Root Question: Is the service desk responsive to our customers?

Data: 1,259 per month

59 per month

Responses on a 1–5 scale

Measures: The number of trouble calls

The number of abandoned calls

The length of time before the caller hung up

The survey responses

Information: Percentage of total calls that were abandoned, by month

Percentage of total calls that were abandoned, by year

Metric:

Figure 1-7. Percentage of service calls abandoned, by month and by year

Looking at Figure 1-7, the responsiveness of the service desk for the past year has been well within expectations. During March, July, and August, however, the percentage of calls abandoned were above expectations (more than 20%). These three spikes are worth investigating to determine both the cause and the likelihood that these could be a recurring problem. Also of note is the steady increases leading up to these spikes. April and October were excellent months for responsiveness and should be analyzed to see if the causes are repeatable.

Is this a metric? Yes, this qualifies in our taxonomy as a metric because it tells a story in response to a root question.

Is this a good metric? No, it definitely can be better. It can tell a more complete story. Looking back at the information, we can also incorporate the survey responses on “time to answer” to determine the customers’ perceptions of the service desk’s responsiveness. Another important component of the metric should be the percentage of abandoned calls under 30 seconds. This standard could vary; it could be under 15 or 45 seconds. It depends on different factors. What is the customer listening to during the time on hold? How long does a person typically stay on the line before he realizes he dialed the wrong number? How short of a wait is considered not to be a lost opportunity? But improving this metric without first addressing the root question is, as a friend recently put it, like putting icing on a rock. It might look good enough to eat, but it’s not. We can keep improving this metric so it looks better, but it won’t satisfy unless we go back to the root question.

The metric, like its components, are tools that can be used to answer the root question. We will address the proper use of these tools later. For now, it’s enough to have a common understanding of what the components are and how they relate to each other.

The Data-Metric Paradox

There is an interesting paradox involving the components of metrics and their relationship to the root question used to derive them.

Data, the easiest to understand, identify, and collect, should be the last item to develop. The most complex and difficult component, the root question, has to come first. As our