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What Is Data Modeling? Data Modeling Best Practices for Data-Driven Organizations

erwin

What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise.

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Real-time artificial intelligence and event processing  

IBM Big Data Hub

AI and event processing: a two-way street An event-driven architecture is essential for accelerating the speed of business. Non-symbolic AI can be useful for transforming unstructured data into organized, meaningful information. This helps to simplify data analysis and enable informed decision-making.

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Transforming Big Data into Actionable Intelligence

Sisense

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. Big data challenges and solutions.

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IBM and TechD partner to securely share data and power insights with gen AI

IBM Big Data Hub

As technology expands, at TechD , we know that the quality of generative AI (gen AI) depends on accurate data sourcing. A reliable and trustworthy data source is essential for sharing information across departments. This addresses data management, conversational interface and natural language processing needs with efficiency.

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IBM Watson and ESPN use AI to transform fantasy football data into insight

IBM Big Data Hub

If you play fantasy football, you are no stranger to the concept of data-driven decision making. Every football season, millions of articles, blog posts, podcasts and videos are produced by the media, offering expert analysis on everything from player performance to injury reports. But numbers only tell half the story.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

We’re living in the age of real-time data and insights, driven by low-latency data streaming applications. The volume of time-sensitive data produced is increasing rapidly, with different formats of data being introduced across new businesses and customer use cases.

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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content. This unstructured data forms the backbone for creating models and the ongoing training of generative AI, so it can stay effective over time. trillion in that year.