Generative artificial intelligence (AI) is beginning to materially affect what organizations can do with their data. Tech Target brought this interesting information to us in their article, “Generative AI hype evolving into reality in data, analytics.”
Generative models can generate synthetic data that resembles real data, helping to augment a dataset and increase its size. Larger datasets often lead to better-performing machine learning models and more robust data analysis.
Generative models can also be used for data imputation, filling in missing values in a dataset. By learning the statistical dependencies between different features, generative models can make educated guesses about the missing data points.
While generative AI can offer valuable support for data analytics, its successful application requires careful consideration of the data quality, model selection, and potential biases present in the data. Additionally, the ethical implications of using generative models in data analytics should be taken into account, especially regarding data privacy and the use of synthetic data.
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