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6 ways generative AI can optimize asset management

IBM Big Data Hub

Every asset manager, regardless of the organization’s size, faces similar mandates: streamline maintenance planning, enhance asset or equipment reliability and optimize workflows to improve quality and productivity. These foundation models, built on large language models, are trained on vast amounts of unstructured and external data.

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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.

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Apache Ozone – A Multi-Protocol Aware Storage System

Cloudera

Apache Ozone is compatible with Amazon S3 and Hadoop FileSystem protocols and provides bucket layouts that are optimized for both Object Store and File system semantics. This blog post is intended to provide guidance to Ozone administrators and application developers on the optimal usage of the bucket layouts for different applications.

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The blueprint for a modern data center 

IBM Big Data Hub

Use data and automated precision to produce results What is automated precision? Automation will play a pivotal role in transforming the data center, where scale and complexity will outpace the ability of humans to keep everything running smoothly. billion in 2022.

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5 Ways Layered Navigation Improves Business Intelligence Strategies

Smart Data Collective

However, with the ever-increasing volume and complexity of data, it’s essential to have an effective data navigation system to optimize your BI strategy. Layered navigation is a powerful tool that can improve your BI strategy by providing better access to relevant data and insights.

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

IBM Big Data Hub

Historical data and trends: Automated systems can efficiently store and analyze historical data, enabling trend analysis and pattern recognition. This information is vital for capacity planning and performance optimization.

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How OLAP and AI can enable better business

IBM Big Data Hub

Initially, they were designed for handling large volumes of multidimensional data, enabling businesses to perform complex analytical tasks, such as drill-down , roll-up and slice-and-dice. Early OLAP systems were separate, specialized databases with unique data storage structures and query languages.

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