datagovernance
By Jozef de Vries More than any other factor, the hyperabundance of accessible data has powered today’s surge in AI adoption and generative AI capability. Collecting, cleaning, organizing, and securing that data for AI and machine learning have become a project in itself—a governance endeavor in which AI tools themselves play an important role. The result can be an enormous improvement in data governance that benefits the entire enterprise. The database remains the foundational repository for data, but the ecosystem of AI-powered data governance tools is all over the place, including products ...
Info World
By Alex Watson The potential of generative AI has captivated both businesses and consumers alike, but growing concerns around issues like privacy, accuracy, and bias have prompted a burning question: What are we feeding these models? The current supply of public data has been adequate to produce high-quality general purpose models, but is not enough to fuel the specialized models enterprises need. Meanwhile, emerging AI regulations are making it harder to safely handle and process raw sensitive data within the private domain. Developers need richer, more sustainable data sources—the reason man...
Info World
By Anirban Ghoshal Snowflake has announced new capabilities for its Horizon suite, a built-in set of composite standards and compliance features. Horizon, showcased in November last year, came with features such as data quality monitoring, data lineage UI, differential privacy policies, enhanced data classification, and other additional authorizations and certifications. The new updates to the suite, which were announced at the ongoing annual Snowflake Summit, include a new Internal Marketplace, a feature that describes objects with the help of AI via Snowflake Copilot, and the general availab...
Info World
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