Which of these is considered a limitation of predictive modeling?

A reliance on accurate data is indeed considered a limitation of predictive modeling. The effectiveness of predictive models heavily depends on the quality and accuracy of the input data. If the data used for training the model is incomplete, outdated, or contains errors, the predictions made by the model can be significantly flawed. This reliance on high-quality data means that organizations must invest time and resources into data cleaning, validation, and maintenance to ensure their predictive models function effectively.

On the other hand, quick implementation time, high success rates, and universal application across industries do not typically represent limitations of predictive modeling. In fact, successful predictive models can often be implemented relatively quickly, with many industries reporting positive outcomes from their use. Moreover, while predictive modeling can be tailored to various sectors, it isn't universally applicable without customization to fit the specific context and data of each industry.