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Unbundling the Graph in GraphRAG

O'Reilly on Data

at Emory reported that their graph-based approach “significantly outperforms current state-of-the-art RAG methods while effectively mitigating hallucinations.” reported that GraphRAG in LinkedIn customer service reduced median per-issue resolution time by 28.6%. Chunk your documents from unstructured data sources, as usual in GraphRAG.

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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. Theyre impressive, no doubt.

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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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The Rise of Unstructured Data

Cloudera

The rate of data growth is reflected in the proliferation of storage centres. For example, the number of hyperscale centres is reported to have doubled between 2015 and 2020. And data moves around. Cisco estimates that global IP data traffic has grown 3-fold between 2016 and 2021, reaching 3.3 of that data is analysed.

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The state of data quality in 2020

O'Reilly on Data

Just 20% of organizations publish data provenance and data lineage. Adopting AI can help data quality. Almost half (48%) of respondents say they use data analysis, machine learning, or AI tools to address data quality issues. Can AI be a catalyst for improved data quality?

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO Business Intelligence

Before LLMs and diffusion models, organizations had to invest a significant amount of time, effort, and resources into developing custom machine-learning models to solve difficult problems. In many cases, this eliminates the need for specialized teams, extensive data labeling, and complex machine-learning pipelines.

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Datasphere manages and integrates structured, semi-structured, and unstructured data types.