In the rapidly evolving landscape of generative artificial intelligence, data is the cornerstone that fuels innovation, drives decision-making and shapes user experiences. However, the sheer volume, variety and velocity of data present unique challenges in terms of governance and classification. Effective data governance ensures that data is accurate, secure and used ethically, while data classification organizes this information into meaningful categories for efficient management and utilization. In the realm of generative AI, these practices are not just beneficial—they are essential. This topic was inspired by the blogpost, “Navigating data governance and classification in generative AI with NetApp” from NetApp.
As AI systems become more sophisticated and integrated into various aspects of business and society, the need for robust data management frameworks will only grow. By implementing strong data governance and classification practices, organizations can ensure that their AI initiatives are built on a foundation of accuracy, security and ethical use, ultimately leading to more reliable and trustworthy AI systems.
At the end of the day, content needs to be findable, and that happens with a strong, standards-based taxonomy. Data Harmony is our patented, award winning, AI suite that leverages explainable AI for efficient, innovative and precise semantic discovery of your new and emerging concepts, to help you find the information you need when you need it.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.
