Limitations of Predative analytics (3)

 

Limited Contextual Understanding

Predictive analytics leverage patterns and data for forecasting functions just it does so not know anything about the actual world. The procedure processes the available quantitative data as well as the earlier patterns, but it does not entirely get the situation. Though the predictive model shows fading sales as a reason for low demand, but it does not detect supply chain problems when certain pieces of data lack in it.

The predictive models are the wrong results generators because they do not have a practical understanding of what is literally going on in the real world. The models can hardly feel customer emotions, or keep track of the variable situations, or unanticipated changes in policies if only these specific data points are in the dataset. There is a big trouble in business decisions made only on the models' outcomes when the necessary information is not there, and this results in the oversight of the vital details.

The businesses that are making use of predictive analytics need to gather their staff and their AI systems to solve the problem. Human input serves as a very important part of the process of filling in the gaps in the systems that are carried out by the machine. The frequency of the data update for predictive models is concomitant with their accuracy growth directly. One of such tools that affect the decision-making routines positively is the predictive tools but organizations use it only as a complementary piece along with the other sources of information.

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