Data analytics has become an indispensable tool for organizations across various sectors, driving decision-making processes and uncovering valuable insights. However, the quality of data analytics hinges not only on the quantity and accuracy of data but also on the methodologies used to analyze it. In recent years, generative artificial intelligence (AI) has emerged as a game-changer in data analytics, offering innovative approaches to data generation, augmentation and analysis. Tech Target brought this interesting topic to us in their article, “Generative AI capabilities increase data analytics value.”
Data scarcity, anomalies and imbalance are common challenges in data analytics, particularly in fields such as healthcare, finance and cybersecurity. Generative models, such as autoencoders and variational autoencoders, can reconstruct input data and measure reconstruction errors to identify anomalies. By leveraging generative AI for anomaly detection, organizations can enhance their ability to detect novel threats, abnormalities or errors that traditional methods might overlook, thereby bolstering the quality and reliability of data analytics outcomes.
As generative AI continues to advance, its impact on data analytics quality is poised to grow, ushering in a new era of data-driven innovation and discovery across industries.
The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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