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Generative AI is pushing unstructured data to center stage

CIO Business Intelligence

When I think about unstructured data, I see my colleague Rob Gerbrandt (an information governance genius) walking into a customer’s conference room where tubes of core samples line three walls. While most of us would see dirt and rock, Rob sees unstructured data. have encouraged the creation of unstructured data.

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Data governance in the age of generative AI

AWS Big Data

Data governance is a critical building block across all these approaches, and we see two emerging areas of focus. First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses.

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SharePoint Premium highlights the hard road CIOs face with generative AI

CIO Business Intelligence

SharePoint Premium’s potential To understand why SharePoint Premium might actually matter, look no further than the fact that, in the typical enterprise, about 20% of all data is structured — the stuff that fits nicely into relational databases. To oversimplify a smidgen, call unstructured data “content” and think of it as atoms.

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Do I Need a Data Catalog?

erwin

The data catalog is a searchable asset that enables all data – including even formerly siloed tribal knowledge – to be cataloged and more quickly exposed to users for analysis. Three Types of Metadata in a Data Catalog. Technical Metadata. Operational Metadata. for analysis and integration purposes).

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Advancing AI: The emergence of a modern information lifecycle

CIO Business Intelligence

Although less complex than the “4 Vs” of big data (velocity, veracity, volume, and variety), orienting to the variety and volume of a challenging puzzle is similar to what CIOs face with information management. Beyond “records,” organizations can digitally capture anything and apply metadata for context and searchability.

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What is a data architect? Skills, salaries, and how to become a data framework master

CIO Business Intelligence

Established and emerging data technologies: Data architects need to understand established data management and reporting technologies, and have some knowledge of columnar and NoSQL databases, predictive analytics, data visualization, and unstructured data.

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Graphs on the Ground Part II: Knowledge Graphs in the Life Sciences

Ontotext

Companies in the life sciences face data challenges on two fronts: Volume. Organizations in this sector often deal with multiple repositories of millions of documents on top of proprietary data from labs and other internal sources. As with drug discovery, this data is typically a mixture of structured and unstructured sources.