Principal Systems Research Analyst at Sandia National Laboratories
Thor Osborn focuses his data science efforts on analysis and modeling to inform executive leadership regarding key business decisions. Most of his recent work has been in modeling workforce movements, compensation, and document management. He holds a doctorate in biomedical engineering from the University of Washington and an MBA from the University of New Mexico, and has earned the Certified Analytics Professional designation. He has been awarded three US patents and has published on diverse topics including biological sensing, MEMS actuators, seeker systems, terrorism, and workforce analysis.
Track: Risk Management & Predictive Analytics
Monday, April 15, 1:50–2:40pm
Application of Text Analysis to Quality Control of Operational Document Sets
Business operations are typically guided by procedural and policy-oriented document sets that were developed over time by many contributors. As a document set grows and begins to tax the memory capacity of individuals, the risk that additional documents offer little incremental information increases. Overcrowding of the conceptual space in a definitional document set tends to confound classification. This confounding risk is especially important in Human Resource Management regarding job definition documents, because distinctions in compensation absent clear differentiation of qualifications and job duties expose the firm to legal, financial, and reputation risks. This presentation addresses the business case for differentiating the job description set and demonstrates an algorithmic text analytics-based approach for comparing document differentiation against a contextually derived minimum standard using the Kolmorogov-Smirnov test. Analysis, quality improvement actions, and resulting impacts to differentiability are shown using a corpus of 250 job descriptions representing a large healthcare services organization.