Metrics_ How to Improve Key Business Results - Martin Klubeck [9]
Measures
Figure 1-2 illustrates the next level of information: measures and how data is related.
Figure 1-2. Measures and data relationship map
Measures begin to give us a more useful picture by incorporating some level of detail. The detail may include units of measure (in 50%, “percent” is the unit of measure and the data is 50) and information regarding how the data relates to other data. To state “70 percent” is more useful than to simply state “70.” Even better, we may have “70 percent of 63 users.” Each measure is made up of one or more datum. These measures, like the data, can have different levels of interrelations. One of the bubbles (top left in Figure 1-2) depicts a grouping of data that lacks a parent measure. This data is grouped because it is related, but it doesn’t lead to a more meaningful measure. Demographics and height and weight are examples of this—data that may be useful, but doesn’t necessarily feed into a larger measure.
Other data are floating independently within the map. These are rogue data (any term that means “no connections” works) that may or may not have a use later.
Measures bring more clarity to the data by grouping them in true relationships and adding a little context. Still, without clear connections to an underlying purpose or root question (more on this later), measures are nothing more than dressed-up data.
Measures bring more clarity to the data by grouping them in true relationships and adding a little context.
Information
Figure 1-3 illustrates the first useful level of information—and that’s just what we call it, “information.” Information groups measures and data (as well as rogue data) into a meaningful capsule.
Figure 1-3. Data, measures, and information relationship map
Information takes measures and data and adds context. Notice that some data is not included in the information. Some data, regardless of how well it is collected, no matter how well you plan, may be superfluous. In the end, you may determine that the data does not fit or does not help to answer the root question you are working on. Information pulls in only the data and measures needed.
The context information brings to the data and measures is essential to moving indiscernible numerical points to an understandable state. With measures, we know that we are talking about percentages and that it is related to a number of users. Information adds context in the form of meaning, thus making the measures understandable: “Seventy percent of 63 users prefer the ski machine over the stair stepper.”
Information adds context in the form of meaning, thus making the measures understandable.
While information within the right context can be especially useful, a metric may be what is truly needed.
Before we go on to the next piece of the puzzle, it may help to look at an example of how actual information (data and measures) fits into the diagram. Figure 1-4 shows an example using information around Speed to Resolve.
Figure 1-4. Speed to Resolve relationship map
Metrics
Figure 1-5 illustrates a full story, a metric. It’s a picture made up of information, measures, and data. It should fulfill the adage, “a picture is worth a thousand words.”
Figure 1-5. Metrics as a picture. Illustration by Alyssa Klubeck.
We finally reach the all-important definition of “metric.” A metric is more than simply grouping multiple pieces of information together. Well, not really much more.
A metric, by my definition, is made