Data Analytics has always been subjected by a trial-and-error analysis, a method that becomes difficult when datasets are large and diversified. Handling huge volume of data usually leads to limited options in building analytical models. Processing large datasets in a reasonable amount of time does not always be supported with the traditional statistical methods. These traditional statistical solutions typically focus on fixed analytics that is limited to the available samples that are frozen in time, which often results in either transcending or unreliable conclusions.

Machine learning is a perfect alternative, to address this challenge which spotlights the development of speedy efficient algorithms. These algorithms enables real-time processing of huge volume of data , deliver accurate predictions of various types such as recommending right products, customer segmentation, detecting fraud and risks, customer retention etc. Machine learning support these functions by creating a set of algorithms that differ from the traditional statistical techniques. The emphasis is on real-time and highly scalable predictive models, using fully automated methods that make data scientist tasks easier.

To conclude, machine learning is finding its automated way to handle huge volume of enterprise data, extremely fast real-time insights, easily inferable by industry users and rapidly employable into production.