A retailer wants to extract themes and customer sentiment from millions of free-form email messages in near real time. Which business advantage of machine learning over traditional business intelligence is this scenario demonstrating?
Guaranteeing 100 percent prediction accuracy with a small training dataset.
Replacing all human subject-matter expertise with fully automated decisions.
Eliminating the need for any data-quality or governance processes.
Unlocking insights from large volumes of unstructured data at scale.
Machine learning models can be trained to recognize patterns in unstructured data-such as text, images, or audio-and automatically generate insights at scale. In this case, ML unlocks value from raw customer emails (unstructured text) without the need for manual tagging or rule creation. Traditional business intelligence tools typically rely on structured data and predefined reports, so they cannot easily handle large volumes of unstructured text. The other options are misconceptions: ML does not guarantee perfect accuracy, it still requires high-quality data, and it benefits from, rather than completely replaces, human domain expertise.
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How does Google Cloud's NLP (Natural Language Processing) solutions help analyze unstructured data?
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Why is unstructured data challenging for traditional business intelligence tools?
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What types of insights can machine learning provide from customer emails?
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What is unstructured data?
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How does machine learning analyze unstructured text?
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Why is machine learning better than business intelligence for processing unstructured data?
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