All Posts

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.

Download Now

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.

Recent Posts

[Earley AI Podcast] Episode 34: Doug Kimball

Taking Control of your Data: How Knowledge Graphs Help to Optimize your Business Guest: Doug Kimball

Accelerating Data and Analytics Capabilities Age of Generative AI: How Governance is a Key Enabler

The underlying principles of Artificial Intelligence have been evolving over decades. Recent advances have created nothing short of a revolutionary breakthrough in information management. Generative AI is in the public consciousness and corporate applications are promising but require certain guardrails and decision-making policies and processes. While “governance“ is a term that brings to mind bureaucratic structures with little practical on-the-ground application, a correctly designed decision-making framework driven by business process/outcome measures and KPIs provides a critical component of data analytics and AI programs.

[Earley AI Podcast] Episode 33: Ben Clinch

Exploring the Power of Collaboration in Data Science Guest: Ben Clinch