GCP Cloud Digital Leader Practice Question

A retailer uses dashboards to show last month's sales trends but now wants software that can automatically suggest personalized product discounts to each shopper while they browse. Why would machine learning, rather than traditional business intelligence reporting, be needed for this new goal?

Machine learning primarily automates the creation of static historical reports, eliminating the need for visualization tools.
Machine learning works only with fully structured data, while business-intelligence tools can handle both structured and unstructured information.
Business-intelligence dashboards cannot connect to real-time data sources, whereas machine learning systems always require streaming inputs.
Machine learning can learn patterns from past shopping behavior and predict optimal discounts for each new visitor without manual rule creation.

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Answer Description

Traditional business-intelligence and data-analytics tools are designed mainly for descriptive insight: they aggregate and visualize historical, structured data so humans can understand what has already happened. Machine-learning systems, in contrast, discover patterns in large datasets and use those learned relationships to generate predictions or recommendations for previously unseen cases without being explicitly programmed for every rule. Because real-time, individualized discount suggestions require the system to predict a customer's future purchase likelihood on the fly, an ML approach is appropriate, whereas a BI dashboard that only summarizes past sales cannot provide this predictive capability.

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