Limitations of predictive analytics

  What is predictive analytics? Transforming data into future insights | CIO

A valuable tool, predictive analytics uses mathematical algorithms and historical data to predict future events. Like any instrument, though, it has its limitations. The calibre of the data utilised for analysis presents one difficulty. Predictions may be off if the data is biassed, out-of-date, or incomplete. An additional constraint is to the presumption that historical patterns will persist in the future, which may not consistently hold true, especially in quickly changing settings. Furthermore, it can be challenging to comprehend and apply predictive analytics models successfully without specific understanding due to their complexity and difficulty. Furthermore, it might be difficult to account for unexpected changes when expectations are disrupted by unforeseen events or outliers. Predictive analytics is still a useful tool for making decisions despite these drawbacks, but it's crucial to approach its results with caution and understanding of its boundaries.

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