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Building a culture of data-driven decisions and insights with IBM Business Analytics

IBM Big Data Hub

Organizations are managing and analyzing large datasets every day, but many still need the right tools to generate data-driven insights. Even more, organizations need the ability to bring data insights to the right users to make faster, more effective business decisions amid unpredictable market changes.

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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?

Big Data 275
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The case for predictive AI

CIO Business Intelligence

Predictive AI uses advanced algorithms based on historical data patterns and existing information to forecast outcomes to predict customer preferences and market trends — providing valuable insights for decision-making. It leverages techniques to learn patterns and distributions from existing data and generate new samples.

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How Encored Technologies built serverless event-driven data pipelines with AWS

AWS Big Data

Encored develops machine learning (ML) applications predicting and optimizing various energy-related processes, and their key initiative is to predict the amount of power generated at renewable energy power plants. It allows for efficient data storage and transmission, as well as easy manipulation of the data using specialized software.

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Top 6 predictions for AI advancements and trends in 2024

IBM Big Data Hub

These applications are designed to meet specific business needs by integrating proprietary data and help to ensure more accurate and relevant responses. This trend signals a move toward more efficient and personalized AI-driven business solutions. This synergy enhances productivity and cost-efficiency.

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Minimizing Supply Chain Disruptions with Advanced Analytics

Cloudera

In summary, predicting future supply chain demands using last year’s data, just doesn’t work. Accurate demand forecasting can’t rely upon last year’s data based upon dated consumer preferences, lifestyle and demand patterns that just don’t exist today – the world has changed.

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How OLAP and AI can enable better business

IBM Big Data Hub

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. Early OLAP systems were separate, specialized databases with unique data storage structures and query languages.

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