Remove Metadata Remove Modeling Remove Structured Data Remove Unstructured Data
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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 is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

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Salesforce debuts Zero Copy Partner Network to ease data integration

CIO Business Intelligence

“The challenge that a lot of our customers have is that requires you to copy that data, store it in Salesforce; you have to create a place to store it; you have to create an object or field in which to store it; and then you have to maintain that pipeline of data synchronization and make sure that data is updated,” Carlson said.

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Making OT-IT integration a reality with new data architectures and generative AI

CIO Business Intelligence

Data remains siloed in facilities, departments, and systems –and between IT and OT networks (according to a report by The Manufacturer , just 23% of businesses have achieved more than a basic level of IT and OT convergence). Denso uses AI to verify the structuring of unstructured data from across its organisation.

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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Semi-structured data falls between the two.

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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. On the other hand, data lakes are flexible storages used to store unstructured, semi-structured, or structured raw data.

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The Role of AI and ML in Model Governance

Alation

These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. Currently, models are managed by modelers and by the software tools they use, which results in a patchwork of control, but not on an enterprise level. And until recently, such governance processes have been fragmented.