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Enabling Integration and Interoperability Across the Grid with Knowledge Graphs

Ontotext

It also adds flexibility in accommodating new kinds of data, including metadata about existing data points that lets users infer new relationships and other facts about the data in the graph. Linking the data to related data in other collections and adding other data to this collection.

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5 surefire ways to derail a digital transformation (without knowing it)

CIO Business Intelligence

But there are common pitfalls , such as selecting the wrong KPIs , monitoring too many metrics, or not addressing poor data quality. Consider how it looks to nontechnical executives when every digital transformation initiative has customized dashboards, different KPIs, and metrics with underlying data quality issues.

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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.

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Addressing the Elephant in the Room – Welcome to Today’s Cloudera

Cloudera

Cloudera’s true hybrid approach ensures you can leverage any deployment, from virtual private cloud to on-premises data centers, to maximize the use of AI. Reliability – Can you trust that your data quality will yield useful AI results?

Big Data 107
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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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What Makes Data-in-Motion Architectures a Must-Have for the Modern Enterprise

Cloudera

Management involves utilizing tools to easily connect publishing and subscribing applications, ensure data quality, route data, and monitor health and performance as streams scale. This capability converts large volumes of raw data into contextualized data that is ready for use in a business process.