Step 6 “Obtain Data” is the foundation of every cost estimate. How good the data are affects the estimate’s overall credibility. Depending on the data quality, an estimate can range anywhere from a mere guess to a highly defensible cost position. Credible cost estimates are rooted in historical data. Rather than starting from scratch, estimators usually develop estimates for new programs by relying on data from programs that already exist and adjusting for any differences. Thus, collecting valid and useful historical data is a key step in developing a sound cost estimate.[1]

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 Work Breakdown Structure (WBS), and the ground rules and assumptions. Once the boundaries of the estimate are known, the next step is to establish an idea of what estimating methodology will be used. Only after these tasks have been performed should the estimator begin to develop an initial data collection plan. [1]

For more information see: GAO Cost Estimating and Assessment Guide – Chapter 10

Tasks that are conducted during this step include: [1]

  • 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, learning, 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

Data Collection
Data collection is a lengthy process and continues throughout the development of a cost estimate and through the program execution itself. Data can be collected in a variety of ways, such as from databases of past projects, engineering build-up estimating analysis, interviews, surveys, data collection instruments, and focus groups. There are many types of data that need to be collected.

Types of data that should be collected during this phase include: [1]

  • Technical: define the requirements for the equipment being estimated, based on physical and performance attributes, such as length, width, weight, horsepower, and size.
  • Schedule & Program: provides parameters that directly affect the overall cost. For example, lead-time schedules, start and duration of effort, delivery dates, outfitting, testing, initial operational capability dates, operating profiles, contract type, multiyear procurement, and sole source or competitive awards must all be considered in developing a cost estimate.
  • Cost Data: includes labor dollars (with supporting labor hours and direct costs and overhead rates), material and its overhead dollars, facilities capital cost of money, and profit associated with various activities.

Data Normalization
The purpose of data normalization (or cleansing) is to make a given data set consistent with and comparable to other data used in the estimate. Since data can be gathered from a variety of sources, they are often in many different forms and need to be adjusted before being used for comparison analysis or as a basis for projecting future costs. Cost data are adjusted in a process called normalization, stripping out the effect of certain external influences. The objective of data normalization is to improve data consistency, so that comparisons and projections are more valid and other data can be used to increase the number of data points. [1]

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Updated: 7/29/2017

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