Remove Artificial Intelligence Remove Business Intelligence Remove Data Silos
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Shaping the future: OMRON’s data-driven journey with AWS

AWS Machine Learning Blog

By analyzing their data, organizations can identify patterns in sales cycles, optimize inventory management, or help tailor products or services to meet customer needs more effectively. One key initiative is ODAPChat, an AI-powered chat-based assistant employees can use to interact with data using natural language queries.

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Build a financial research assistant using Amazon Q Business and Amazon QuickSight for generative AI–powered insights

Flipboard

Their information is split between two types of data: unstructured data (such as PDFs, HTML pages, and documents) and structured data (such as databases, data lakes, and real-time reports). Different types of data typically require different tools to access them. QuickSight also offers querying unstructured data.

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Composable analytics

Dataconomy

This modular approach allows businesses to assemble tools and techniques that perfectly fit their specific needs, rather than relying on less flexible monolithic systems. Composable analytics refers to an agile, adaptable framework for data analytics that allows users to create customized analytical environments using modular components.

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Supercharge your data strategy: Integrate and innovate today leveraging data integration

IBM Journey to AI blog

Data is the differentiator as business leaders look to utilize their competitive edge as they implement generative AI (gen AI). Leaders feel the pressure to infuse their processes with artificial intelligence (AI) and are looking for ways to harness the insights in their data platforms to fuel this movement.

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Customers and Banks Priorities Collide as AI Jolts Financial Industry

Smart Data Collective

The promise of significant and measurable business value can only be achieved if organizations implement an information foundation that supports the rapid growth, speed and variety of data. This integration is even more important, but much more complex with Big Data.

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IBM to help businesses scale AI workloads, for all data, anywhere

IBM Journey to AI blog

Supporting the data management life cycle According to IDC’s Global StorageSphere, enterprise data stored in data centers will grow at a compound annual growth rate of 30% between 2021-2026. [2] ” Notably, watsonx.data runs both on-premises and across multicloud environments. .

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata. Trusted, governed data is essential for ensuring the accuracy, relevance and precision of AI.