Gluttony is over-indulgence to the point of waste. Gluttony brings to mind pictures of someone consuming food at a rate well beyond simple need. In measurement, gluttony then can be exemplified by programs that collect data that has no near-term need or purpose. When asked why the data was collected, the most common answer boils down to ‘we might need it someday…’
Why is the collection of data just in case, for future use or just because it can be done a problem? The problems caused by measurement gluttony fall into two basic categories. The first is that it wastes the effort of the measurement team, and second because it wastes credibility.
Wasting effort dilutes the measurement team’s resources that should be focused on collecting and analyzing data that can make a difference. Unless the measurement program has unlimited resources, over collection can obscure important trends and events by reducing time for analysis and interpretation. Any program that scrimps on analysis and interpretation is asking trouble, much as a person with clogged arteries. Measures without analysis and interpretation are dangerous because people see what they like in the data due to clustering illusion (cognitive bias). Clustering illusion (or clustering bias) is the tendency to see patterns in clusters or streaks of in a smaller sample of data inside larger data sets. Once a pattern is seen it becomes difficult to stop people from believing that the does not exist.
The second problem of measurement gluttony occurs because it wastes the credibility of the measurement team. Collecting data that is warehoused just in case it might be important causes those who provide the measures and metrics to wonder what is being done the data. Collecting data that you are not using will create an atmosphere of mystery and fear. Add other typical organizational problems, such as not being transparent and open about communication of measurement results, and fear will turn into resistance. A sure sign of problems is when you begin hearing consistent questions about what you are doing, such as “just what is it that you do with this data?” All measures should have a feedback loop to those being measured so they understand what you are doing, how the data is being used and what the analysis means. Telling people that you are not doing anything with the data doesn’t count as feedback. Simply put, don’t collect the data if you are not going to use it and make sure you are using the data you are collecting to make improvements!
A simple rule is to collect only the measurement data that you need and CAN use. Make sure all stakeholders understand what you are going to do with the data. If you feel that you are over-collecting, go on a quick data diet. One strategy for cutting back is to begin in the areas you feel safest [SAFEST HOW?]. For example, start with a measure that you have not based a positive action on in the last 6 months. Gluttony in measurement gums up the works just like it does in a human body; the result of measurement gluttony slows down reactions and creates resistance, which can lead to a fatal event for your program.