Brief Simple History of Performance Measures

I like to say that performance measures should be “actionable,” in that they are useful for a decision that needs to be made. To be useful, the measures need to provide information useful to, fit the decision style of, and be easily understandable by a decision-maker. When I develop performance measures, I first work with the decision-makers to understand the decisions they face and what information will be useful for their decisions. Then, I try to develop measures that fit the needs of the decision-makers. This may seem obvious as desirable goals for performance measures, but this has not always been the case.


The history of performance measures does not reveal this obvious understanding of performance measures. First in the evolution of performance measures were data-driven measures where one looked for data and constructed measures from this data. Of course, just because data is available does not mean that the measures satisfy the needs of decision-makers. Next came model-driven measures where an established framework is used to link measures to company goals. Model-driven measures were a major advancement over data-drive measures since they related measures to where an organization wanted to go. The model-driven approach is often useful for formulating and implementing strategy. However, when applied to the broad range of an organization’s activities, the models may not fit the decision-making styles of all managers. Also, the models have a tendency to give equal emphasis to all measures, and linkages among measures in different parts of the models are not always clear. I call what I develop decision-driven measures. Decision-driven measures focus on understanding the decisions needs of an organization and then on providing actionable performance measures and a measure framework customized to fit the decision needs of the particular organization.

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Comment by Mary Adams on March 25, 2014 at 1:48pm

Thanks Jim - As you say, this should seem obvious but the difference is critical. In fact it's one of the basic tenets of knowledge economics: knowledge (in this case, metrics) only have use when it solves a problem.

There's a lesson in here for those of us focused on intangibles measurement--that we need to focus on solving problems, not applying standard frameworks.

Keep these gems coming!!

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