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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.

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

datapine

The complex and ever-evolving nature of logistics makes it an essential use case for big data applications. You can use big data analytics in logistics, for instance, to optimize routing, improve factory processes, and create razor-sharp efficiency across the entire supply chain. Did you know?

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

FineReport

When combined, SaaS BI tools enable users to conduct comprehensive data analysis using modern cloud BI technology , providing access to all data sources and the ability to compile online dashboards from mobile devices.

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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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The Power of Graph Databases, Linked Data, and Graph Algorithms

Rocket-Powered Data Science

You should still get the book because it is a fantastic 250-page masterpiece for data scientists!) (You should still get the book because it is a fantastic 250-page masterpiece for data scientists!) I publish this in its original form in order to capture the essence of my point of view on the power of graph analytics.

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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. ” – Eddie Castillo, Head of Marketing, ExaVault Inc. Why is this a myth?