Unlocking the Power of Generative AI with Knowledge Graphs: Five Considerations for Getting Started

Many organizations are realizing that they need analytics to stay competitive, including advanced analytics such as AI and generative AI.

Generative AI can be used to analyze data (e.g., for sentiment analysis or churn analysis) as well as for applications such as chatbots or for personalized marketing recommendations. Many of these use cases will require data and analytics professionals to utilize generative AI with their own company’s data to build analyses and applications that serve their own customers and operations.

To do this yourself, you will need your company data arranged in context and organized in a consumable way. Knowledge graphs are a way to help you connect your data based on its meaning and not where or how it’s organized so it can be used to train your AI models more easily and accurately.

This TDWI Checklist Report examines five key considerations and best practices for generative AI using knowledge graphs.

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