Survey of Step 6

Process Tasks

  • Create a data collection plan with emphasis on collecting current and relevant technical, programmatic, cost, and risk data.
  • Investigate possible data sources.
  • Collect data and normalize them for cost accounting, inflation, and quantity adjustments.
  • Analyze the data for cost drivers, trends, and outliers and compare results against rules of thumb and standard factors derived from historical data.
  • Interview data sources and document all pertinent information, including an assessment of data reliability and accuracy.
  • Store data for future estimates.

Best Practices

The estimate is based on a historical record of cost estimating and actual experiences from other comparable programs.

  • The estimate is based on historical data and the data are applicable to the program.

  • The data are reliable.

  • There is enough knowledge about the data source to determine if the data can be used to estimate accurate costs for the new program.

  • If EVM data are used, the EVM system has been validated against the EIA-748 guidelines.

The estimate is adjusted properly for inflation

  • The cost data are adjusted for inflation so that they could be described in like terms and to ensure that comparisons and projections are valid. The final estimate is converted to budget year dollars.

Likely Effects if Criteria Are Not Fully Met

  • Without sufficient background knowledge about the source and reliability of the data, the cost estimator cannot know with any confidence whether the data collected can be used directly or need to be normalized or otherwise modified.

  • Unless cost estimators know the factors that influence a program’s cost, they may not capture the right data.

  • If cost estimators do not determine whether proposal data deviate from other similar data, they may introduce bias into the cost estimate.

  • If outliers are removed from a data set without justification, the data may not capture the natural variation within program costs.

  • If data are not properly normalized, the data set may be inconsistent with other data used in the estimate, the effects of external influences may not be removed, and comparisons and projections may not be valid.

  • When adjusting for inflation, if the index used is not correct the resulting estimate could overstate or understate the cost of the program.

  • Unless data are documented and archived for future use, more effort will be required to develop and document the current cost estimate, and cost estimates for future programs will not benefit from the research and analysis already conducted.

  • Lack of historical data will leave the cost estimator without insight into actual costs of similar programs, including any cost growth since the original estimate. As a result, the estimator will be prevented from challenging optimistic assumptions and bringing more realism to the cost estimate.

  • If it cannot be established that EVM data are from a compliant system, analysts will lack the necessary assurance that the EVM data are free from errors and anomalies that can skew and distort analyses.