Chapter 9: Step 6: Obtain the Data

Data are the foundation of every cost estimate. The quality of the data affects the estimate’s overall credibility. Depending on the data quality, an estimate can range anywhere from a rough guess to a highly defensible cost position. Reliable cost estimates are rooted in historical data. Estimators usually develop estimates for new programs by relying on data from programs that already exist and then making adjustments for any differences. Thus, collecting valid and useful historical data is a key step in developing a sound cost estimate.

The challenge of data collection is obtaining the most applicable historical data to ensure that the new estimate is as accurate as possible. One way of ensuring that the data are applicable is to perform checks of reasonableness to see if the results are similar. Different data sets converging toward one value provides a high degree of confidence in the data.

Performing quality checks takes time and requires access to large quantities of data. Collecting data is often the most difficult, time-consuming, and costly activity in cost estimating. It can be exacerbated by a poorly defined technical baseline or WBS. However, by gathering sufficient data, cost estimators can analyze cost trends on a variety of related programs, which gives insight into cost estimating relationships that can be used to develop parametric models.

Before collecting data, the estimator must fully understand what needs to be estimated. This understanding comes from the purpose and scope of the estimate, the technical baseline description, the WBS, and the ground rules and assumptions. Only after these tasks have been performed should the estimator begin to develop an initial data collection plan.