Evaluating Benefits: Getting Statistical (Part 2)
Continued from Evaluating Benefits: Getting Statistical (Part 1).
Setting Objectives Based on Probabilistic Benefit Outcomes
Best practices in benefits realization management include clearly defining expected outcomes, creating measurable objectives and monitoring project and program outcomes (even after project and program closure) to determine whether the organization and its stakeholders obtained the benefits expected from the investment. But it’s also a best practice to avoid making investment choices based on benefits that may be obtained only 50% of the time. And yet, some organizations do just that by describing uncertain benefit outcomes in deterministic ways.
If an organization makes an investment choice using only the most likely benefit outcome (which is the statistical mode), or using only the expected value of a benefit (which is the statistical mean, or average), then--if the benefit has bell-shaped properties--roughly one-half the time, the organization will be disappointed with its investment choice. Bell-shaped uncertainties have expected values (arithmetic averages) that occur at or near the top of a bell-shaped probability curves. There’s about a 50% chance that an actual benefits outcome will be less than the expected value (and about a 50% chance it could be more).
Here’s a better approach: evaluate quantitative benefits using a
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