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of the raw data. You will be challenged on this, and rightfully so. People have their own expectations of what the answer to your root question should be. They will also have expectations regarding what the data should say about that question. Regardless of the answer, someone will think you have it wrong and check your data. Thus, you have to be accurate when you share the data. This requires that you perform quality checks of the data. It doesn’t matter if the errors are due to your sources, your formulas, or a software glitch. If your data is proven to be wrong, your metrics won’t be trusted or used. Most examples of inaccurate raw data can be found as a result of human error but even automated tools are prone to errors, particularly in the interpretation of the data. Errors can be found in anything from logging data incorrectly, mistakes in calculations, or assigning the wrong categories to information. If your categories are not defined properly, an automated system may report the data correctly, but it might be reporting the wrong data. If you are tracking time to resolve trouble calls, is the time equal to the time between the start and stop dates? Or is it the time from the call to the resolution (which may occur well before the day/time the trouble ticket is closed)? Are you using calendar days to track time or workdays? If you are reporting on availability, what is considered an outage? How do you determine the outage occurred, when, and for how long? The simple rule of thumb is to double (and triple) check your data. I find the best way to check my data is to have someone else look at it. I’m too close to the work to see the errors others see immediately.

When you’re starting, there is nothing more important than accuracy of your raw data.

Accuracy of your analysis. We’ll get into methods of analysis later, but for now, it is important for you to document the processes and steps you take to analyze the data. This will enable you to repeat the process—a necessity for consistency. A simple example is the use of formulas in spreadsheet programs. I do a lot of my metric work in spreadsheet programs because they are easy, user-friendly, and powerful. I use formulas to calculate everything from the number of elapsed days to the percentage of change over time. Whatever methods you use they must be documented. Anyone looking over your work should be able to replicate your work by hand (using pen, paper, and a calculator). This documentation is tedious but necessary. Your process must be repeatable. Your process must produce zero defects in the data, analysis, and results.

Your process and the resulting information must be error free.

Repeatability of your process. Yes, I already mentioned this in the accuracy of your analysis. But I it is worth emphasizing and clarifying that repeatability is critical not only for the analysis, but throughout the process to design the metric. The collection of the data must be repeatable—in a strict sense. The analysis of the data must be repeatable. The graphical representation must also be a repeatable step in the process. Each time, you should collect, analyze, and report the data in the same way. If you don’t document the process and ensure it is repeatable, you will lose the all-important trust of your audience. This repeatability is necessary throughout the process. It’s why we develop schedules. We want to do it the same way, at the same time intervals, and using the same tools. Consistency is critical.

Without repeatability you don’t really have a process.

To adhere to the tenants of good documentation, you’ll need to use a method for controlling versions of your data, analysis, and reports. You’ll need to store your information with backups. All of the documentation listed must be safeguarded against loss or tampering. You have to ensure the accuracy of the components and you can’t do this if you don’t control access to the information.

First, you’ll have to ensure the sources of your data are producing accurate information. When you have checked the data