Temporally consistent employee group labels

Analytical applications in large organizations across even intermediate time ranges are often made complex, costly or even impractical due to temporal inconsistencies in the available data. The ever-changing nature of organizations causes categorical labels in data to change over time. This is particularly true for HR data, as the organization adjusts to changes in skillsets, market and operations. This project aims at establishing automated methods of defining consistent employee group labelling across time. Such consistent labels will allow organizations to make better organizational decisions as more historical data becomes available for analysis.

Faculty Supervisor:

Daniel Coombs

Student:

Rebeca Cardim Falcão

Partner:

Visier Solutions Inc

Discipline:

Mathematics

Sector:

Information and cultural industries

University:

Program: