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Exploring Methods of Conflating Data from Various Data Sources

Methods and Models II Track



When creating a cost estimate, it is not out of the realm of possibilities that one might be required to combine data from multiple sources to get an accurate estimate. Coleman, Braxton, Druker, et al. have presented research on which methods might be the best to combine opinions given by subject matter experts (SMEs). Additionally, they have also discussed what adjustments are needed to make the estimates provided by SMEs more accurate as studies suggest that SMEs will tend to underestimate the range of possible data values when appropriate feedback is not given to them. However, SME opinions are not the only multiple source data one may need to combine. Different sources such as empirical data or simulation data might also need to be conflated to determine an accurate cost estimate. This talk will discuss different methods that may be used to combine data from multiple sources and will explore these methods further through the use of a toy problem.


Ashley Moses
Booz Allen Hamilton
Dr. Ashley Moses is currently an analyst at Booz Allen Hamilton in the Business Analytics division located in the Washington D.C. area. She graduated with her Ph.D. in mathematics from St. Louis University in May 2010, were she studied under the tutelage of Darrin Speegle on topics in harmonic and fourier analysis. Prior to working at Booz
Allen, she was a professor of mathematics and statistics at Mary Baldwin College. While at Mary Baldwin College, she also served as the mathematics expert reviewer for two pharmacology books being published by Elsevier. In addition to analysis, she is also interested in statistics, programming, and playing Ultimate Frisbee.