D. Determining customer sentiments from call center voice recordings.
This is the only option directly addressing unstructured data in the context of the question. This involves analyzing unstructured data (audio recordings), which requires machine learning techniques like natural language processing (NLP) and speech-to-text models to extract insights like customer sentiment. Machine learning models can process and interpret unstructured data, such as audio, text, or images, to unlock business value.
Here's why the other options are incorrect for this specific question:
A. (Analyzing tabular records of product defects to predict future maintenance cycles)
This involves structured data, such as tabular records, which are already organized in a clear format (rows and columns). While machine learning is applicable here, it does not involve unstructured data. Therefore, it doesn’t fit the context of the question.
B. (Recommending new products based on previous purchases)
Recommender systems often use structured or semi-structured data, such as user IDs, product IDs, and purchase history, to make predictions. While this scenario uses machine learning, it typically involves structured data, not unstructured data.
C. (Monitoring financial transactions to identify potential fraud and risk)
Fraud detection typically analyzes structured data, such as transaction logs (amounts, timestamps, account IDs). While advanced machine learning techniques can be applied here, the data is structured, so it does not align with the question's focus on unstructured data.
D. Determining customer sentiments from call center voice recordings.
This scenario uses machine learning to analyze unstructured data (voice recordings) to extract insights (customer sentiments). By understanding customer sentiments, businesses can make informed decisions to improve their products or services, enhance customer satisfaction, and ultimately drive business value.
D: This scenario involves processing and analyzing voice recordings, which are a form of unstructured data. Machine learning techniques, particularly those related to natural language processing (NLP) and speech analysis, are used to analyze the voice data to extract insights about customer sentiments. This application of machine learning helps businesses understand their customers better, improve customer service, and tailor their strategies accordingly.
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