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When is data too clean to be useful for enterprise AI?

CIO Business Intelligence

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

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Building tools for enterprise data science

O'Reilly on Data

The proliferation of models is still a theoretical consideration for many data science teams, but Gordon and his colleagues at Salesforce already support hundreds of thousands of customers who need custom models built on custom data. Continue reading Building tools for enterprise data science.

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AI Adoption in the Enterprise 2021

O'Reilly on Data

During the first weeks of February, we asked recipients of our Data & AI Newsletter to participate in a survey on AI adoption in the enterprise. We also asked what kinds of data our “mature” respondents are using. Most (83%) are using structured data (logfiles, time series data, geospatial data).

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

CIO Business Intelligence

Predictive insights: By analyzing historical data, LLMs can make predictions about future system states. Structured outputs: In addition to reports in natural language, LLMs can also output structured data (such as JSON). This enables proactive maintenance and helps prevent potential failures.

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Unbundling the Graph in GraphRAG

O'Reilly on Data

Entity resolution merges the entities which appear consistently across two or more structured data sources, while preserving evidence decisions. 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.

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Apache Sqoop: Features, Architecture and Operations

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Apache SQOOP is a tool designed to aid in the large-scale export and import of data into HDFS from structured data repositories. Relational databases, enterprise data warehouses, and NoSQL systems are all examples of data storage.

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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.