Remove Contextual Data Remove Data Integration Remove Metadata
article thumbnail

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.

article thumbnail

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.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

If Curiosity Cabinets Were Knowledge Graphs

Ontotext

Knowledge graph technology can walk us out of the lack of context (which is basically absence of proper interlinking) and towards enriching digital representation of collection with semantic data and further interlinking it into a meaningful constellation of items.

article thumbnail

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

erwin

Your organization won’t be able to take complete advantage of analytics tools to become data-driven unless you establish a foundation for agile and complete data management. You need automated data mapping and cataloging through the integration lifecycle process, inclusive of data at rest and data in motion.