Limitations of Predative analytics (5)

 

Changing Conditions and Data draft

The usage of predictive analytics is very challenging as it needs historical data for it to be able to forecast future events even if there are changes in the present surroundings. The mixture of market fluctuations, changing customer tastes, and technological development results in traditional data losing its relevancy in the current situation. Data drift is a process where the predictive models become less accurate as they go because they detect some changes in the input data.

Data drift is characteristic of the situation when either input data or relations between data and results change. The model which is constructed for the purpose of forecasting consumer spending patterns before pandemics will now have to stop because buying habits of people change. The model will keep on generating false predictions as it is still using the old behavior patterns that are not relevant anymore.

One of the most important approaches to solving the problem of data drift is constant monitoring and updating of models. Organisations should provide their models with recent data and check on a regular basis if they are still working. By being vigilant and making such necessary changes, companies can continue to use their prediction instruments even in a rapidly changing environment.

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