Data Science and Engineering

Estimating Fungibility Between Skills by Combining Skill Similarities Obtained from Multiple Data Sources

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This paper proposes an approach to estimating fungibility between skills given multiple information sources of those skills. An estimate of skill adjacency or fungibility or substitutability is critical for effective capacity planning, analytics and optimization in the face of changing skill requirements of an organization. The proposed approach is based on computing a similarity measure between skills, using each available data source, and combining these similarities into a measure of fungibility. We present both supervised and unsupervised integration methods and demonstrate that these produce improved outcomes, compared to using any single skill similarity source alone, using data from a large IT organization. The skills’ fungibility matrix created using this approach has been deployed by the organization for demand forecasting across groups of skills. We discuss how the fungibility matrix is deployed to generate skill clusters and present a forecasting algorithm that additionally incorporates past/future engagements and a mechanism to quantify uncertainty in the forecast. A possible extension of this work is to use the fungibility measure to cluster skills and develop a skill-centric representation of an organization to enable strategic assessments and planning.