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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. It required a different data platform solution. It was Datawarehouse.

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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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How two software companies are using IBM watsonx for their enterprise generative AI solutions

IBM Journey to AI blog

In the year since we unveiled IBM’s enterprise generative AI (gen AI) and data platform, we’ve collaborated with numerous software companies to embed IBM watsonx™ into their apps, offerings and solutions.

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

IBM Journey to AI blog

Watsonx.data will be core to IBM’s new AI and Data platform, IBM watsonx, announced today at IBM Think. “IBM and Cloudera customers will benefit from a truly open and interoperable hybrid data platform that fuels and accelerates the adoption of AI across an ever-increasing range of use cases and business processes.”

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

IBM Journey to AI blog

Your data strategy should incorporate databases designed with open and integrated components, allowing for seamless unification and access to data for advanced analytics and AI applications within a data platform. With the right data, you can create a data-driven organization that drives business value and innovation.

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