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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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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8 tips for unleashing the power of unstructured data

CIO Business Intelligence

Making the most of enterprise data is a top concern for IT leaders today. With organizations seeking to become more data-driven with business decisions, IT leaders must devise data strategies gear toward creating value from data no matter where — or in what form — it resides.

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5 tips for better business value from gen AI

CIO Business Intelligence

According to AI at Wartons report on navigating gen AIs early years, 72% of enterprises predict gen AI budget growth over the next 12 months but slower increases over the next two to five years. A second area is improving data quality and integrating systems for marketing departments, then tracking how these changes impact marketing metrics.

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