This guy is an idol called San Simon that, in exchange for cigarettes and booze, guarantees good fortune.

This guy is an idol called San Simon that, in exchange for cigarettes and booze, guarantees good fortune.

Beliefs:

Beliefs act as a powerful filter that can cause communication problems. Deep-seated beliefs force the believer into a difficult position when it comes to challenging the status quo. When change occurs without the ability to challenge the rational for the change, it leads to confusion and possibly to conflict. This is a scenario where Good Numbers Go Bad. Beliefs aren’t necessarily based on mathematical or scientific fact. Once upon a time most people believed the world was flat. This belief constrained behavior. For example, a senior executive I knew firmly believed that education and training were not related to improved capability in his organization. If the organization stopped supporting training, his belief would potentially lower productivity, innovation, capability and potentially increase the need to outsource work. The workforce would not stay current or gain new skills.. Facts and the relationships between facts can are abridged through beliefs. Metrics professionals must continually create awareness so that everyone in the metrics equation keeps an open, questioning mind to extract the full value from numbers.

Just Plain Wrong:

One of the final classes of communication errors occurs when the metrics team publishes the information gleaned from a chart, graph or single number and it is wrong. In my mind, the most frightening words are “my interpretation of this graph is that the earth is flat.” Misinterpretation can be caused by a number of problems ranging from education and knowledge of the interpreter, active misinterpretation or the act of spreading misinformation (or that belief thing in the last section). Regardless of why the interpretation is wrong, damage is done. As soon as the misinterpretation is out there, the metrics program will be viewed as non-neutral and, potentially, biased. When measurement drives activity based on misinterpretation, the results can be erroneous business decisions with lasting implications. It can leave a bad taste in people’s mouths for a long time.

Zombie Hypothesis:

One of the worst errors made by humans is not publicly recognizing a mistake and trying to tough it out. The affliction can be encapsulated by the phrase “throwing good money after bad.” When applied to a metrics program, this affliction can lead to a scarcity of funds for metrics and SPI investment opportunities. Not facing up to your mistakes causes a scenario where Good Numbers Go Bad. The cost and effort needed to gather, analyze, report and react to the measures being collected will eclipse the value derived if you are living a lie. The Zombie Hypothesis is a variant of the Law of Crappy Process which implies that the worst, most incorrect data will become the de facto standard (real or perceived) for your measurement program. When you find a problem, recognize it, fix the process(es), then the definitions and re-implement the measurement. The effectiveness and efficiency of the measurement program will be improved. More importantly, you will inhabit the moral high ground of knowing you are measuring the right thing in the right way.