Metrics_ How to Improve Key Business Results - Martin Klubeck [14]
Assume the data is wrong
Decide the analysis is wrong
Tweak the data (not the metric)
Some really wise managers decide that the metric is incorrect and try again. Unfortunately, they don’t realize that the problem is the lack of a root question or that the question they are working from is wrong. Instead of starting over again from the question, they try to redesign the metric.
The bottom line and the solution to this common problem? Pick your cliché, any of them work: We have to start at the top. We have to start at the end. We have to start with the end in mind. You can’t dig a hole from the bottom up. We have to identify the correct root question.
The root question will determine the level of the answer. If the question is complex enough and needs answers on a periodic basis, chances are you will need to develop a comprehensive metric. A question along the lines of “How is the health of (a service or little pigs)?” may require a metric to answer it, especially if you want to continue to monitor the health on a regular basis.
The vagueness of the question makes it more complex. Clarity simplifies.
As we design the metric to answer the root question, we realize that we need to have measures of the various components that make up an organization’s health. In the case of the three pigs (or humans for that matter), we may want indicators on the respiratory, circulatory, digestive, and endocrine systems. To say nothing of the nervous or excretory systems, bones, or muscles. The point is, we need a lot more information since our question was of wide scope.
If our question had a narrower scope, the answer would be simpler. Take the following question, for example: How is your weight-control going? The answer can be provided by taking periodic measures after stepping on a reliable scale. Unfortunately, rarely is the question this specific. If the first little pig is only asked about his weight, the other indicators of health are missed. Focusing too closely on a specific measure may lead to missing important information. You may be asking the wrong question, like the second little pig’s doctor, who only used three indicators and neglected to share the bigger picture with his porcine patient.
Perhaps you know about your blood pressure. Perhaps you had a full checkup and everything is fine—except you need to lose a few pounds. “How is your weight loss coming along?” may be good enough. If it is, then a metric is overkill. A measure will suffice.
When designing a metric, the most important part is getting the right root question. This will let us know what level of information is required to answer it. It will govern the design of the metric down to what data to collect.
Metric Components
Let’s recap the components of a metric and their definitions:
Data: Data, for our purposes, is the simplest possible form of information and is usually represented by a number or value; for example, six, twenty-two, seventy, true, false, high, or low.
Measures: Made up of data, measures add the lowest level of context possible to the data. Measures can be made up of other measures.
Information: Information is made up of data and measures. Information can be made up of other information. Information provides additional, more meaningful context.
Metrics: Metrics are made up of data, measures, and information. Metrics can be made up of other metrics. Metrics give full context to the information. Metrics (attempt to) tell a complete story. Metrics (attempt to) answer a root question.
Root Question: The purpose for the metric. Root questions define the requirements of the metric and determine its usefulness.
Recap
This chapter introduced a common language for metrics and their components. It also introduced the Data-Metric Paradox, in which we learned that we have to