Limitations of predictive analytics



 A potent tool that may help businesses in making data-driven decisions is analytics that is predictive. It should be taken into consideration that it has a few constraints as well. 

Firstly, the quality and amount of data used in developing models for prediction have a significant impact on their accuracy. The models might not be reliable or trustworthy if the data is biassed, incomplete, or unrepresentative of the issue being studied. 

Second, unexpected events and changes in the environment are not taken into account by statistical analysis, however the fact that these factors can greatly affect how accurate the predictions are. Automatic model results can also be hard to analyse, and those who are not technical may find it difficult to understand and apply the findings. In the final analysis, taking advanced analytics into action might take a lot of time and resources, needing specialised knowledge and infrastructure.

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