Calibrate estimation tools — Avoiding the Experience Trap

NOTE: 9th post of a series on an HBR article by Prof. Kishore Sengupta, et al on The Experience Trap.

Some interesting observations on the use of PM tools, with a specific focus on estimation.  It is a basic tenet of multivariate analysis to identify the variables most correlated with the desired answer — which implies that one must develop an enviroment-specific model — e.g., industry, solution, team skill, etc.  But…

Many organizations, however, simply import project management forecasting tools from other contexts and other companies. One software company we studied had just adopted a tool from an aerospace company.

My experience is that the model issues are relatively easy to fix, but that data availability and reliability are much stickier. 

Organizations compound estimation problems by basing their model assumptions on data from past projects without scrubbing the data first (that is, without accounting for any unusual circumstances encountered by those projects).

The authors ID a “best process” that restores some credibility to the estimates, inclding

[normalizing] outcomes in a three-step process that dentifies unusual events, roughly calculates their impact, and then deducts the impact from the results. The scrubbed values then go into the estimation models.

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