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Why Financial Services Firms are Championing Natural Language Processing

CIO Business Intelligence

But only in recent years, with the growth of the web, cloud computing, hyperscale data centers, machine learning, neural networks, deep learning, and powerful servers with blazing fast processors, has it been possible for NLP algorithms to thrive in business environments. Just starting out with analytics?

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

Data Lake 119
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The Reason Many AI and Analytics Projects Fail—and How to Make Sure Yours Doesn’t

CIO Business Intelligence

Blocking the move to a more AI-centric infrastructure, the survey noted, are concerns about cost and strategy plus overly complex existing data environments and infrastructure. Though experts agree on the difficulty of deploying new platforms across an enterprise, there are options for optimizing the value of AI and analytics projects. [2]

Analytics 137