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## Baseline Releases and Data Validation

In order to measure project activities, the quality assurance department of the organization issues benchmarks from time to time. This could be actual effort variance over planned effort, overplanned actual schedule variance, organizational productivity ratio, defect density, etc.

Bases are issued quarterly or periodically. An improvement in schedule compliance or a reduction in defect density will mean that the organization has made improvements. For example an exemption might state that organization I’s base schedule variance is 8.9 percent and organization II’s base schedule variance is 5 percent. The results may be that the organization has improved adherence to the schedule by 3.9 percent or in other words projects are delivered to customers on time.

Now let’s understand that such an analysis was performed on two different data sets at two different times. One data set measured effort, schedule, and other project metrics during Quarter I, the second data set measured effort, schedule, and other project metrics during Quarter II. The mean may have changed significantly, or the variance may have changed or in other words, comparisons cannot be made on the same data.

For example, during Q1 the company may undertake large scale projects and during Q2 the company may undertake medium scale projects. In the case of large projects, estimates tend to have more variance and therefore the mean of all schedule changes is reduced.

Similarly, the variance between individual data points in the distribution can vary greatly due to data collection methods. During Q1 the facility may use a manual data collection system and during Q2 the facility may use data entered into an automated time management system by employees.

When making decisions about infrastructure, it is important to see if there is a significant change in the parameters associated with the metric being evaluated for system improvement. Specifically, a measure such as schedule variance is associated with several parameters such as project complexity, staff training level, working hours etc. ensure that the context in which the metric is linked does not change significantly.

For example, the variation of the average schedule of 9 percent where the size of the projects 20 FP should not be compared with the schedule of 5.9 percent and the size of the project 10 FP or the project or the schedule between 9 percent and the staff. with low skill should not be compared to projects with an average schedule variance of 5.9 percent with high skill workers.

In such cases one must use a statistical regression equation that shows the schedule as a function of several other variables to scale the values to a common baseline.

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