Remove Data Quality Remove Enterprise Remove Unstructured Data
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The state of data quality in 2020

O'Reilly on Data

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

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Are enterprises ready to adopt AI at scale?

CIO Business Intelligence

The path to achieving AI at scale is paved with myriad challenges: data quality and availability, deployment, and integration with existing systems among them. Then there’s the data lakehouse—an analytics system that allows data to be processed, analyzed, and stored in both structured and unstructured forms.

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Unbundling the Graph in GraphRAG

O'Reilly on Data

A generalized, unbundled workflow A more accountable approach to GraphRAG is to unbundle the process of knowledge graph construction, paying special attention to data quality. Chunk your documents from unstructured data sources, as usual in GraphRAG. Link the extracted entities to their respective text chunks.

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Unlocking the full potential of enterprise AI

CIO Business Intelligence

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Reliability and security is paramount.

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Data trust and the evolution of enterprise analytics in the age of AI

CIO Business Intelligence

The foundational tenet remains the same: Untrusted data is unusable data and the risks associated with making business-critical decisions are profound whether your organization plans to make them with AI or enterprise analytics. Like most, your enterprise business decision-makers very likely make decisions informed by analytics.

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Handling real-time data operations in the enterprise

O'Reilly on Data

For big data, this isn't just making sure cluster processes are running. A DataOps team needs to do that and keep an eye on the data. With big data, we're often dealing with unstructured data or data coming from unreliable sources. They know how to operate the big data frameworks.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality.