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The first decision we had to make was how to roll up the two different grades. We opted to give each grade (E, M, or O) a value. It was important to us to have our results be beyond reproach. Since we knew errors might seep in from many different quadrants, we had to ensure our intentions were never in question. Trying to make what could be a complex problem into something manageable, if not simple, we worked off a 10-point value scale. An E was worth 10 points; an M was worth 5 points; and an O was worth zero. We then averaged the numerical values. So if we had two values to use, as in the case of Availability we would get the following:

E = 10, M = 5, O = 0

An E and an M averaged 7.5 points

An E and an O averaged 5 points

An M and an O averaged 2.5 points

Now we took the calculated grade and turned it back into an evaluation against expectations. A grade of 8 or greater would be an E. A grade between 5 and 8 would be an M. A grade below 5 would be an O. So, another way to look at it is as follows:

An E and an M averaged 7.5 points, which was an M

An E and an O averaged 5 points, which was an M

An M and an O averaged 2.5 points, which was an O

We liked the way this worked. You had to exceed in a measure to balance out an Opportunity for Improvement. We felt this was in the spirit of “erring on the side of excellence” as my friend Don would say.

Err on the side of excellence.

A possible drawback was it made the Opportunity for Improvement a “bad” thing and the Exceeding Expectations a “good” thing. As I covered earlier, they are both anomalies. What we want are Meets Expectations. Another drawback was that the combined grades could hide anomalies. If you had an equal number of Es and Os it would roll up to Ms and look like you were doing just what was wanted—meeting expectations.

The positives were that we could show an overall grade, giving a “feel” for the health of the item. Another positive was that we could deal with the “hidden” grades by simply flagging any Ms or Es that had Os buried in the data. That would allow the metric customer to know where to dive deeper to find the Os and see what was happening in those cases.

The bad vs. good thing we could not overcome as easily. In the end we decided to deal with it on a case by case basis, ensuring that we stressed that both were anomalies. We accepted this because, no matter how much we stress the negatives to Exceeding Expectations, in the end, the fact that expectations were exceeded wasn't in itself a bad thing. It was only “bad” based on how you achieved the grade—like if you neglected other important work/services or applied too many resources to attain it. But if you achieved this grade by a process improvement or simply focusing properly on a different area, it was not only a good thing, but we could change the customers' expectations because we would be able to deliver at this higher level consistency. It could become a marketing point for our services over our competitors. Opportunity for Improvement, the other anomaly, couldn't be said to be the same. If you failed to meet expectations, most times the fact that you weren't meeting the customers' expectations made it a bad thing. Even if you found that it was due to natural disasters or things out of your control, the customer still saw it as a negative. So while I wanted both anomalies to elicit the same response—further investigation, the purpose of that investigation was clearly different.

One investigation was essentially conducted to see if the occurrence could be avoided in the future, while the other was to see if it could be replicated.

This led to the decision that we would roll up the values using the translation, and if there were Os below the level we showed, we marked the grade with an icon. (Note: As we continually worked to improve our tools and processes, we adapted the icon when there was any anomaly hiding in the grades with an E or an O!)

Figure 10-1 is the