Remove Data Quality Remove Machine Learning 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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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. Let’s revisit the point about RAG borrowing from recommender systems.

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What Tools Do You Need To Manage Unstructured Data?

Smart Data Collective

Unstructured data represents one of today’s most significant business challenges. Unlike defined data – the sort of information you’d find in spreadsheets or clearly broken down survey responses – unstructured data may be textual, video, or audio, and its production is on the rise. Centralizing Information.

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Are enterprises ready to adopt AI at scale?

CIO Business Intelligence

AI’s ability to automate repetitive tasks leads to significant time savings on processes related to content creation, data analysis, and customer experience, freeing employees to work on more complex, creative issues. A data mesh delivers greater ownership and governance to the IT team members who work closest to the data in question.

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8 tips for unleashing the power of unstructured data

CIO Business Intelligence

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. Unstructured data resources can be extremely valuable for gaining business insights and solving problems.

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless. You get the picture.

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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.