We live in an era where businesses are driven by the force called data. With the help of the same, enterprises are able to efficiently acquire potential customers and sustain the existing ones as well. It can also be used to understand what customers need in a product or a service which leads to a satisfied base of customers with exemplary solutions and optimization of available resources. Now, one might ask, how can random facts and figures avail enterprises such significant insights? The answer to that is data analysis.
There are numerous methods of analyzing data and the most popular among them are Statistics and Machine learning. Both the terms are often considered as one and the same but in reality, even though their purpose is the same, their use cases are different. Statistics is the science of classifying and analyzing data by using mathematical equations. On the other hand, Machine Learning is one of the applications of Artificial Intelligence which enables the system to learn on itself with the use of data. While Statistics is used to collect, analyze, summarize, represent, and organize the data and transform it into useful insights, Machine Learning uses data to learn, predict, and enable the system to perform cognitive tasks in an unsupervised setting.
Businesses are highly incentivized from what statistics and machine learning bring to their table. Companies often stumble upon the process of choosing a suitable alternative in their processes. For example, should there be an in-house infrastructure for CRM or should the company outsource the service? With the help of statistics, they can find out the more efficient alternative. It is highly useful in operations especially in the inventory management, where companies can get an accurate forecast on how much material should be there in the stock. Moreover, it can be very useful in designing business strategies.
Machine learning, on the other hand, automates businesses by eliminating the manual day to day processes. This saves a lot of resources and brings productivity in the process. It also provides accurate predictions for enterprises which enables them to take better decisions. For example, With the help of machine learning, an enterprise can accurately calculate the Customer Lifetime Value. Hence, optimized resources!
Both statistics and machine learning aren’t new to the world and have been around for quite a while. However, while the former stands solid in its ground, there is a lot to come for the latter.
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