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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. It also supplies insights to NBC’s production team.

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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. It also supplies insights to NBC’s production team.

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

datapine

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications. Did you know?

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

FineReport

From seamless data integration to intuitive visualizations, advanced analytics, self-service capabilities, and robust scalability and security, SaaS BI software empowers organizations to unlock valuable insights and drive informed decision-making.

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

IBM Big Data Hub

Automation streamlines the root-cause analysis process with machine learning algorithms, anomaly detection techniques and predictive analytics, and it helps identify patterns and anomalies that human operators might miss. Why is this a myth? This information is vital for capacity planning and performance optimization.

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

FineReport

In addition to security concerns, achieving seamless healthcare data integration and interoperability presents its own set of challenges. The fragmented nature of healthcare systems often results in disparate data sources that hinder efficient decision-making processes.

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

CIO Business Intelligence

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. AI-powered data integration tools leverage advanced algorithms and predictive analytics to automate and streamline the data integration process.