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Figure 3-3. Schedules

Most metrics are time-based. You’ll be looking at annual, monthly, or weekly reports of most metrics. Some are event-driven and require that you report them periodically. As part of your metric development plan, you will have to schedule at least three of the following facets of your metric:

Schedule for reporting. Look at the schedule from the end backward to the beginning. Start with what you need. Take into account the customers that you’ve identified to help determine when you will need the metrics. Based on how the metrics will be used will also determine when you’ll need to report it.

Schedule for analysis. Based on the need, you can work backward to determine when you’ll need to analyze the information to finalize the metric. This is the simplest part of the scheduling trifecta, since it is purely dependent upon how long it will take you to get the job done. Of course, the other variable is the amount of data and the complexity of the analysis. But, ultimately, you’ll schedule the analysis far enough in advance to get it done and review your results. I highly recommend you have at least one other pair of eyes review your analysis. Depending on the complexity of your data, you may need a quality check of the raw data used in the analysis also.

Schedule for collection. When will you collect the data? Based on when you will have to report the data, determine when you will need to analyze it. Then, based on that, figure out when you will need to collect it. Often, the schedule for collecting the data will be dependent on how you collect it. If it’s automated, you may be able to gather it whenever you want. If it’s dependent on human input, you may have to wait for periodic updates. If your data is survey-based, you’ll have to wait until you administer the surveys and the additional time for people to complete them.

Since you started at the end, you know when you need the data and can work backwards to the date that you need to have the data in hand. Depending on the collection method you’ve chosen, you can plan out when you need to start the collection process and schedule accordingly.

Nothing new here—if you want to achieve success, you must plan to succeed. Don’t do it when you “get the chance.” Plan it. Schedule it. If it’s worth doing, it’s worth planning to do it right.

And if it’s worth doing right, it’s worth making sure you can do it right more than once. But remember, the development plan isn’t just about repeatability, it’s about getting it right the first time by forcing yourself to think it all out.

Analysis

Documenting analysis happens when you think it does…during the analysis phase.

Figure 3-4. Analysis

This may be the most obvious section of the metric development plan so far. After data collecting, the next thing most people think of when I mention metrics is analysis. All of the statistics classes I’ve taken lead to the same end: how to analyze the data you’ve carefully gathered. This analysis documentation in the plan must include all metric data rules, edits, formulas, and algorithms; each should be clearly spelled out for future reference.

What may be in contention is the infallibility of the analysis tools. There are those that believe if you have accurate data (a few don’t even care if it’s accurate), you can predict, explain, or improve anything through statistical analysis. I’m not of that camp.

I have great respect for the benefits of analysis and, of course, I rely on it to determine the answers my metrics provide. For me, the design of the metric—from the root question, to the abstract picture, to the complete story—is more important than the analysis of the data. That may seem odd. If we fail to analyze properly, we will probably end up with the wrong conclusions and, thereby, the wrong answers. But, if we haven’t designed the metric properly to begin with, we’ll have no chance of the right answers—regardless of the quality of our analysis.

And if we have a