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(the customers were not paying for the service), usage did not reflect income to the organization. The manager had a very healthy big picture view of the mission of her department. If usage was “high,” that might reflect the instability of the IT systems, services, and products. It may also reflect the ineffectiveness of IT-related training. In most businesses and services, high usage would be a good thing. In the case of the Service Desk, high usage might be as bad as low usage. Also, if the usage was “low” it wasn't clear if this reflected that the training was excellent, and the systems stable or that the customer didn't find the Service Desk a meaningful, useful provider.

As with most measures, the information derived from it would have to be interpreted. Further investigation should be carried out rather than jumping to any conclusions. Any extremes could be a negative in this case—but the information was still useful. Since we had data, measures, and information from a decent span of time (over three years), we could determine if there were changes in the customers' patterns of usage. By looking at unique customers, we could determine if there were anomalies—up or down. These spikes may not indicate a good or bad thing. Instead they would indicate only that something had changed.

To be meaningful, I had to have longitudinal data for comparison; otherwise anomalies could not be identified. In some cases it isn't as important—but in the case of usage, it was. The key is to be able to determine anomalies—measures that fell out of the range of normal expectations. After we identified the proper measures and data points, we had to identify expectations.

The task is to identify anomalies—measures that fall out of the range of normal expectations.

This highlights an important facet of the metric program. Your information is only indicators and shouldn't be acted upon without further investigation. Rather than expect these metrics to be the impetus for action, it more often is the indicator that something is amiss, something has changed, or something needs to be investigated.

Customer Satisfaction was another example of data that was easy to attain. The department utilized a third-party survey company that sent survey invitations (via e-mail) to the customer of every closed trouble call. The vendor collected the responses, tabulated the results, and sent a weekly report to the department. I was given a monthly copy of the results for inclusion in the Report Card. All of the measures we had identified were collectable unobtrusively, with no disruption of the department's workflow. This is not always possible, but it is always a goal.

Since most of the data was attainable through automated systems, the data we had was readily available and had a high level of detachment from human error. The few places where humans did interact with the data, the team's desire to produce accurate information made us very happy to have this service as our flagship.

Recall the Metric Development Plan. We realized it was important to identify the source, the data (each component), how and when to collect, and how to analyze it. In the case of the Service Desk much of this was already done. Table 9-3 takes the categories and measures identified for the Service Desk and further breaks them down into the data needed, where that data will be found, and some basic analysis of the data. This analysis can be programmed into a software tool for display.

This was our starting point. We identified these measures easily. Some of them were already being collected and analyzed. As with most things, there were other options to choose from. After looking at the analysis, we reevaluated our draft of the measures.

Let's return to the Metric Development Plan, which consists of the following:

Purpose statement

How the metrics will be used

How the metrics won't be used

Customers of the metrics

Analysis

Schedules

A Picture for the Rest of Us

Prose

Purpose statement. Our purpose statement was defined for us—how can we communicate to our leadership