What is a significant risk of using overly complex models in data analytics?

Using overly complex models in data analytics significantly increases the risk of overfitting. Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new, unseen data. Complex models, by their nature, have a greater capacity to capture various patterns in the training set, including those that are not representative of the general trend. This can lead to high accuracy on the training data but poor generalization capability when faced with real-world data.

While decreased interpretability, reduced computational efficiency, and higher accuracy on training data are valid concerns with complex models, they do not capture the essence of why overfitting is the most pressing risk. Overfitting undermines the model's ability to make accurate predictions outside the training dataset, which is often the primary goal of data analytics. Therefore, the primary concern with overly complex models is that they may not perform well in practice, limiting their usefulness in real-world applications.