Paper Number

1436

Paper Type

Completed

Description

Recent advances to machine learning (ML) and its rapid proliferation spur the wide-spread development of advanced analytics applications. Nonetheless, the capabilities of (ML) can be stalled due to limited or missing data. In this regard, the production of artificial data offers a promising solution. However, its full potential is yet to be unleashed since it's frequently misunderstood or overseen. We attribute this to a lack of practical guidance on when and how to employ artificially generated data. Against this backdrop, we draw on two streams—namely, method engineering and design science to develop "GenFlow", a novel method useful to practitioners as well as researchers. The utility is demonstrated in retrospect for previous work and empirically accessed for the context of employee attrition.

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Dec 11th, 12:00 AM

Designing a Method to Nudge Analytics with Artificially Generated Data

Recent advances to machine learning (ML) and its rapid proliferation spur the wide-spread development of advanced analytics applications. Nonetheless, the capabilities of (ML) can be stalled due to limited or missing data. In this regard, the production of artificial data offers a promising solution. However, its full potential is yet to be unleashed since it's frequently misunderstood or overseen. We attribute this to a lack of practical guidance on when and how to employ artificially generated data. Against this backdrop, we draw on two streams—namely, method engineering and design science to develop "GenFlow", a novel method useful to practitioners as well as researchers. The utility is demonstrated in retrospect for previous work and empirically accessed for the context of employee attrition.

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