Predictive analytics
In simple words, it is the search for patterns in previously accumulated data for use in the future. The support of the full cycle of predictive analytics is most in demand: data structuring, data research, predictive modeling methods (forecasting, clustering, scoring), as well as an assessment of the model's effectiveness. The more data that were previously processed, the more optimal the decision will be to make, since the used regression and supervised learning algorithms must have a substantial number of variable values under the base.
When processing large data, the adoption of such an optimal solution can take considerable time. However, often the requirements of business problems are such that the necessary forecasts should be made not only optimally, but also instantly, in the event of emerging data relating to a particular operation, building on information already stored and previously processed. This condition poses a serious limitation to the developers and makes it necessary to revise already established tools, as not suitable for real-time procedures.
