Risk Management - Preventing Reversion: WOW Measures

What are WOW Measures?

A Wow Measure is a metric of performance that is used to measure the rapid change that occurs when implementing a phase change project as part of a Productivity Journey.

It is recommended that you always include a Wow measure when making predictions to stakeholders about the impact of the changes you want them to approve.

The general trend of a Wow Measure pattern is that the metric will show a sudden change in performance, i.e. if the metric is graphed there will be a step function in the performance after the change is implemented.

There are some common types of Wows that are meaningful in business:

  • A sudden step change
  • A Decaying Curve Pattern
  • Reduced Variability

Common combinations of the above include:

  • Higher highs and higher lows
  • Combinations of the above, e.g. the average getting higher and variability reducing
WOW From a Sudden Step Change
Step detection (also known as step smoothing, step filtering, shift detection, jump detection or edge detection) is the process of finding abrupt changes (steps, jumps, shifts) in the mean level of a time series or signal. The step detection problem occurs in multiple scientific and engineering contexts. A Wow Measure pattern can be a step change (up or down).
WOW From a Decaying Curve Pattern

A Wow Measure pattern may be a decaying curve. If a business is measuring missed due dates or milestones, it might halve in the initial three weeks after the change is implemented, halve again in another three weeks, then halve again in three more weeks. This would show a decaying curve on a graph. 

WOW From Reduced Variability

Dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered. On the other hand, when the variance is small, the data in the set is clustered.

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