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

CIO Business Intelligence

Of late, innovative data integration tools are revolutionising how organisations approach data management, unlocking new opportunities for growth, efficiency, and strategic decision-making by leveraging technical advancements in Artificial Intelligence, Machine Learning, and Natural Language Processing.

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Introducing watsonx: The future of AI for business

IBM Big Data Hub

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. Today we have one of the most comprehensive portfolios of enterprise AI solutions available. Watsonx.ai

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How to build a successful hybrid cloud strategy

IBM Big Data Hub

AWS, Google Cloud Services, IBM Cloud, Microsoft Azure) makes computing resources—like ready-to-use software applications, virtual machines (VMs) , enterprise-grade infrastructures and development platforms—available to users over the public internet.

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Smart manufacturing technology is transforming mass production

IBM Big Data Hub

artificial intelligence (AI) applications, the Internet of Things (IoT), robotics and augmented reality, among others) to optimize enterprise resource planning (ERP), making companies more agile and adaptable. Ensure that sensitive data remains within their own network, improving security and compliance.

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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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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. Cost-effectiveness: Manual observation may require dedicated personnel, leading to increased labor costs.

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Minimizing Supply Chain Disruptions with Advanced Analytics

Cloudera

Advanced analytics and enterprise data are empowering several overarching initiatives in supply chain risk reduction – improved visibility and transparency into all aspects of the supply chain balanced with data governance and security. . Advanced analytics empower risk reduction . Improve Visibility within Supply Chains.

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