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Phonetic Analytics Technology and Big Data: Real-World Cases

Phonetic Analytics Technology and Big Data, illustration

Credit: agsandrew

Since the mid-2000S, few business topics have received as much attention as big data and business analytics,5,8,11,13 including unstructured data derived from social media, blogs, chat, and email messages. In addition to unstructured data, YouTube, Vimeo, and other video sources represent another aspect of organizations' customer services. A 2011 IBM survey of more than 4,000 IT professionals from 93 countries and 25 industries7 identified big data and business analytics as a major business trend for most organizations, along with mobile, cloud, and social business technologies. This trend is also reflected in a number of professional reports and academic journals, including McKinsey Quarterly and MIS Quarterly. The related skills can also potentially help give organizations a competitive advantage.

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Big data takes many forms, including Web and social-media data, machine-to-machine data, transaction data, biometric data, and human-generated data. Human-generated data is our focus here, including vast quantities of unstructured data (such as call-center agents' notes, voice recordings, email messages, paper documents, surveys, and electronic medical records). A number of call analytics technologies are available, including voice searching and indexing for call centers through company-specific phonic-indexing technology. One important application is real-time monitoring that, in a call-center setting, can help address agitated callers and get supervisors involved more quickly. Analytics can process hundreds of hours of audio files in a day, depending on server load, and provide organizations detailed reports on ways to improve customer calls and related job functions, detect problems in operational sectors, and even uncover root problems in products. These systems capture, categorize, store, and analyze unstructured data and can be customized for each customer to include language identification, audio entity extraction, and real-time monitoring.


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