A data science team was tasked with developing a predictive maintenance model for a manufacturing plant's machinery. The team immediately sourced sensor data, cleaned it, and built a technically robust model with 98% accuracy in identifying potential failures on a held-out test set. However, during the initial deployment meetings, it became clear that the model's output did not integrate with the maintenance department's existing workflow, and the predictions were not aligned with the specific component failures the business had prioritized for cost-saving. This has led to significant resistance from stakeholders. According to the Cross-Industry Standard Protocol for Data Mining (CRISP-DM) model, which phase was most likely neglected, leading to these adoption challenges?
The correct answer is Business Understanding. This initial phase of CRISP-DM is critical for defining the project's objectives from a business perspective, understanding stakeholder needs, and establishing the business success criteria. The scenario describes a model that is technically sound but fails to meet business needs regarding workflow integration and prioritized cost-savings, and faces stakeholder resistance. This indicates a fundamental disconnect between the data science work and the business's actual problem and operational context, which should have been established during the Business Understanding phase.
Data Preparation: This option is incorrect. The scenario states that the team successfully cleaned the data, suggesting this phase was performed adequately.
Modeling: This option is incorrect. The model's high accuracy (98%) on a test set indicates that the technical modeling activities were successful from a statistical standpoint. The problem is not with the model's predictive power but its business utility.
Evaluation: This is a plausible but incorrect answer. The Evaluation phase does involve assessing if the model meets business objectives. However, a failure in this phase is often a symptom of an inadequate Business Understanding phase. If the business objectives, operational constraints (like workflow), and success criteria were never correctly defined in the first place, the evaluation would be based on flawed premises. The root cause of the problem lies in the initial Business Understanding phase.
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What is the Business Understanding phase in CRISP-DM?
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How does poor Business Understanding affect model adoption?
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What are some key activities to perform during the Business Understanding phase?