Data Applicability

Because cost estimates are usually developed with data from past programs, it is important to examine whether the historical data apply to the program being estimated. Over time, modifications may have changed the historical program so that it is no longer similar to the new program. For example, it does not make sense to use data from an information system that relied on old mainframe technology when the new program will rely on server technology that can process data at much higher speeds. Having good descriptive requirements of the data is imperative in determining whether the data available apply to what is being estimated.

To determine the applicability of data to a given estimating task, the cost estimator must scrutinize them in light of the following issues:

  • Do the data require normalization to account for differences in base years, inflation rates, or calendar year rather than fiscal year accounting systems?

  • Is the work content of the current cost element consistent with the historical cost element?

  • Have the data been analyzed for performance variation over time (such as technological advances)? Are there unambiguous trends between cost and performance over time?

  • Do the data reflect actual costs, proposal values, or negotiated prices, and has the type of contract been considered?

  • Are sufficient cost data available at the appropriate level of detail to use in statistical measurements?

  • Are cost segregations clear, so that recurring data are separable from nonrecurring data and functional elements (manufacturing, engineering) are visible?

  • Have risk and uncertainty for each data element been taken into account? High-risk elements are more likely to be underestimated.

  • Have legal or regulatory changes affected cost for the same requirement?

  • When several historical values are available for the same concept, are they in close agreement or are they dispersed? If they are in close agreement, are the definitions the same?

Once these questions have been answered, the next step is to assess the validity of the data before they can be used to predict costs.