Trust in AI requires trust in data

Data reliability is paramount for Artificial Intelligence (AI). Accuracy and trust in AI generated insights is directly dependent on the quality of the underlying data. From predictive analytics to Natural Language Processing (NLP) advances such as Large Language Models (LLMs), AI revolutionizes how businesses operate and make decisions. For many, AI is a black-box, and risks, real or imagined, are challenging its use. The success of AI hinges precisely on trust, and trust relies on the ability for teams to understand, observe and act quickly on data quality (DQ). 

 

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