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The importance of data ingestion and integration for enterprise AI

IBM Big Data Hub

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.

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Five benefits of a data catalog

IBM Big Data Hub

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Why data governance is essential for enterprise AI

IBM Big Data Hub

It is really well done, but as someone who spends all my time working on data governance and privacy, that top left section of “contextual datadata pipelines” is missing something: data governance. A strong data foundation is critical for the success of AI implementations.

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Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

Outsourcing these data management efforts to professional services firms only delays schedules and increases costs. With automation, data quality is systemically assured. The data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders. Digital Transformation Strategy: Smarter Data.