Remove Automation Remove Business Intelligence Remove Metadata
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Narrowing the confidence gap for wider AI adoption

AI News

Avi Perez, CTO of Pyramid Analytics, explained that his business intelligence software’s AI infrastructure was deliberately built to keep data away from the LLM , sharing only metadata that describes the problem and interfacing with the LLM as the best way for locally-hosted engines to run analysis.”There’s

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How the right data and AI foundation can empower a successful ESG strategy

IBM Journey to AI blog

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Journey to AI blog

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It allows for automation and integrations with existing databases and provides tools that permit a simplified setup and user experience. Capture and document model metadata for report generation.

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9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality. Automation can significantly improve efficiency and reduce errors. This approach helps minimize disruptions and keeps data aligned with business needs.

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AI and the future of unstructured data

IBM Journey to AI blog

“ Gen AI has elevated the importance of unstructured data, namely documents, for RAG as well as LLM fine-tuning and traditional analytics for machine learning, business intelligence and data engineering,” says Edward Calvesbert, Vice President of Product Management at IBM watsonx and one of IBM’s resident data experts.

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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning Blog

To create and share customer feedback analysis without the need to manage underlying infrastructure, Amazon QuickSight provides a straightforward way to build visualizations, perform one-time analysis, and quickly gain business insights from customer feedback, anytime and on any device.

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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging.

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