I would like to build on the theme of reasoning about what to measure. The goal of business analytics is to track what matters to the organization (what it is you are trying to manage) and respond to the measure in some way to gain improvement. The science of measuring outcomes and In manufacturing and some service delivery domains is statistical process control (SPC), SPC lies at the heart of the Six Sigma movement. Even so, there will be no need to have a 6-six sigma belt to participate in this discussion
. While there is reason to believe that not all of the Six Sigma practices do not apply all that well to our domain, the idea of tracking outcomes, applying statistical analysis to detect change change, and applying some sort of controls to affect the change applies in all business domains, including software and system development and delivery.
Briefly then, the outcomes are the operational goals and controls are the actions you take to achieve the outcomes, So naturally we need too kinds of measures.
- Outcome measures - tracking the measures of effectiveness of the business organization
- Control tracking whether - tracking whether the controls are in fact enacted.
Here is a thought experiment:. Imagine there is a potato chip factory with an operational goal of achieving the right amount of salt on its chips. There is an target amount and the factory needs to stay within small limits for market acceptance. So everyday they grab a sample of chips and record the saltiness. They apply salt by running the recently deep fried chips under a salt shaker. The two controls are the frequency of the shaker and the speed of the belt. Both the shaker frequency and belt speed are measured to confirm the controls are properly responded to. In this example the saltiness is the outcome measure and the shaker frequency and belt speed are control measures.
The simplest way, for me at least, to think about SPC is to measures trends in outcome measures and control measures to determine the likelihood that the controls are in fact affecting the outcome. In our potato chip example we might find that we cannot control the outcome well enough by the shaker and belt controls. In that case, we might look for some other factor to control, say the factory humidity.
If you look at many measurement programs in software and system you often find that outcome and measures are confused. In fact even sorting the measures into the two buckets is hard. No wonder measured process improvement for our domain has been so hard.Anyone have good examples of measurement patterns or antipatterns of measuring controls and outcomes?
Again stay tuned for more....