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Digital twin helps NTT Indycar deliver better race experience to fans

CIO Business Intelligence

When Marcus Ericsson, driving for Chip Ganassi Racing, won the Indianapolis 500 in May, it was in a car equipped with more than 140 sensors streaming data and predictive analytic insights, not only to the racing team but to fans at the Brickyard and around the world. Toward a smarter venue.

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The Impact of Healthcare BI Tools on Decision-Making and Patient Care

FineReport

Understanding Healthcare BI Tools The Role of Healthcare BI Tools Healthcare BI tools are instrumental in revolutionizing decision-making processes and patient care through the utilization of advanced data analysis and technology.

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Digital twin helps NTT Indycar deliver better race experience to fans

CIO Business Intelligence

When Marcus Ericsson, driving for Chip Ganassi Racing, won the Indianapolis 500 in May, it was in a car equipped with more than 140 sensors streaming data and predictive analytic insights, not only to the racing team but to fans at the Brickyard and around the world. Toward a smarter venue.

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Innovative data integration in 2024: Pioneering the future of data integration

CIO Business Intelligence

This article focuses on how these advancements are paving the way for data integration for the years to come in this ever-so-dynamic technological era. AI-powered data integration One of the most promising advancements in data integration is the integration of artificial intelligence (AI) and machine learning (ML) technologies.

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Are You Harnessing the Power of SaaS BI Tools for Dynamic Data Access?

FineReport

Defining Business Intelligence and SaaS Business Intelligence (BI) encompasses the technologies and strategies used for data analysis and decision-making within organizations. Additionally, there is a growing demand for advanced analytics and data visualization tools to make data-driven decisions.

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Debunking observability myths – Part 5: You can create an observable system without observability-driven automation

IBM Big Data Hub

The variety of technology in use also means you won’t always have a subject matter expert (SME) on hand to assist in the setup and configuration of new applications. Historical data and trends: Automated systems can efficiently store and analyze historical data, enabling trend analysis and pattern recognition.

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Winkenbach said that his data showed that “deliveries in big cities are almost always improved by creating multi-tiered systems with smaller distribution centers spread out in several neighborhoods, or simply pre-designated parking spots in garages or lots where smaller vehicles can take packages the rest of the way.”

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