What does "Augmented Analytics" refer to?

Augmented Analytics refers to the use of artificial intelligence to enhance data analytics processes. This includes automating various aspects of data preparation and analysis, which significantly improves efficiency and accuracy. By leveraging AI technologies, augmented analytics enables organizations to extract valuable insights from large, complex datasets more effectively than traditional methods.

The focus is on empowering users—whether they are data scientists, business analysts, or even non-technical users—to gain deeper insights without requiring them to have extensive knowledge of data science or advanced analytics. This approach combines the strengths of machine intelligence with user-friendly tools, making insights more accessible across an organization.

In contrast, other options address different aspects of data analytics or present a more limited view. For instance, while the first option emphasizes a collaboration between human intuition and machine intelligence, it does not capture the primary benefit of automating and enhancing analytics processes that augmented analytics delivers. The third option implies that non-AI systems perform analytics, which does not align with the concept of augmentation through AI. Lastly, the fourth option focuses on complete automation, which does not reflect the supportive and complementing role of AI in traditional analytics that augmented analytics embodies.