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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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What is data architecture? A framework to manage data

CIO

Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects.

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How companies around the world apply machine learning

O'Reilly Media - Data

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

This is where Delta Lakehouse architecture truly shines. Specifically, within the insurance industry, where data is the lifeblood of innovation and operational effectiveness, embracing such a transformative approach is essential for staying agile, secure and competitive. This unified view makes it easier to manage and access your data.

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The future of data: A 5-pillar approach to modern data management

CIO

It was not alive because the business knowledge required to turn data into value was confined to individuals minds, Excel sheets or lost in analog signals. We are now deciphering rules from patterns in data, embedding business knowledge into ML models, and soon, AI agents will leverage this data to make decisions on behalf of companies.

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Enhancing customer care through deep machine learning at Travelers

CIO

And we recognized as a company that we needed to start thinking about how we leverage advancements in technology and tremendous amounts of data across our ecosystem, and tie it with machine learning technology and other things advancing the field of analytics. But we have to bring in the right talent. more than 3,000 of themâ??that

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The evolution of data science, data engineering, and AI

O'Reilly Media - Data

In particular, we examined the evolution of key topics covered in this podcast: data science and machine learning, data engineering and architecture, AI, and the impact of each of these areas on businesses and companies. Continue reading The evolution of data science, data engineering, and AI.