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The Problem of Personalization: AI-Driven Analytics at Scale

This Article originally appeared in the November/December 2017 issue of IT Pro, published by the IEEE Computer Society.

Despite all the investment and technology, organizations continue to struggle with personalizing customer and employee interactions. Many projects provide meaningful results in test environments, only to fail in production due to the high degree of human intervention required— whether during hypothesis development, data modeling, data preparation, or testing and finetuning. It is simply impractical and unsustainable to drive many analytic applications because it takes too long to produce usable results in the majority of use cases. However, new and innovative approaches to using artificial intelligence (AI) and machine learning (ML) now enable accelerated personalization with fewer resources. The result is more practical and actionable customer insights that can be put to work without acts of heroics.

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Earley Information Science Team
Earley Information Science Team
We're passionate about enterprise data and love discussing industry knowledge, best practices, and insights. We look forward to hearing from you! Comment below to join the conversation.

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