Machine Learning Prescriptive Canvas for Optimizing Business Outcomes

TL;DR

This paper advocates for a prescriptive approach in data science projects, emphasizing the importance of directly modeling actions to optimize business outcomes rather than relying solely on predictive models.

Contribution

It introduces the Prescriptive Canvas, a step-by-step methodology to enhance framing, communication, and effectiveness of prescriptive analytics in business projects.

Findings

Prescriptive approach improves decision-making accuracy.

The Prescriptive Canvas facilitates stakeholder communication.

Implementation leads to better business impact.

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.