Machine Learning Prescriptive Canvas for Optimizing Business Outcomes
Abstract
Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements these predictions should become decisions. However, such a two-step approach is not only sub-optimal but might even degrade performance and fail the project. The alternative is to follow a prescriptive framing, where actions are "first citizens" so that the model produces a policy that prescribes an action to take, rather than predicting an outcome. In this paper, we explain why the prescriptive approach is important and provide a step-by-step methodology: the Prescriptive Canvas. The latter aims to improve framing and communication across the project stakeholders including project and data science managers towards a successful business impact.
Cite
@article{arxiv.2206.10333,
title = {Machine Learning Prescriptive Canvas for Optimizing Business Outcomes},
author = {Hanan Shteingart and Gerben Oostra and Ohad Levinkron and Naama Parush and Gil Shabat and Daniel Aronovich},
journal= {arXiv preprint arXiv:2206.10333},
year = {2022}
}
Comments
accepted to ACMKDD Workshop 2022