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The most important and indispensable technologies of our time are Big Data and machine learning. Machine learning automatically trains computers based on data. It is possible by transferring data to the computer, which it uses to increase its performance while performing tasks. Big Data is the main data source for machine learning, so their connection is critical.

Big Data is a large data amount that is difficult to analyze and process. Also, it is difficult for users to understand and use such large amounts of data. Machine learning applications must process large data amounts quickly and efficiently. However, machine learning algorithms can simplify this process by automatically detecting patterns in the data.

These 2 technologies often complement each other. When used together, machines can be taught to recognize patterns in complex data sets and make accurate predictions. Therefore, modern companies are increasingly implementing solutions for working with data.

Big Data

Data can come from a variety of sources, including social media, internet traffic, sensor readings, and customer behavior. Big Data is used for various purposes, such as improving marketing performance by analyzing website visitors’ behavior or to predict customer needs, etc. However, the key purpose of using Big Data is to increase company productivity.

Big Data is widely used in various fields of activity. The healthcare sector is an active user. Physicians have an access and a possibility to analyze patient data. They allow to track the symptoms of patients, identify non-obvious patterns, make a more accurate diagnosis and effectively treat patients.

Machine learning

Machine learning is a field of Artificial Intelligence that provides training for computers using data. Very often, companies use this technology to predict customer behavior. For example, machine learning algorithms allow to analyze the previous behavior of the client and determine the likelihood of his re-applying to the company.

Also, machine learning algorithms are able to detect traces of fraud, which is also a common purpose for its use. By identifying patterns in data that indicate fraud, companies can prevent high investigation costs and fines.

Using machine learning and Big Data is beneficial. Big Data provides huge amounts of training data that is essential for machine learning algorithms. This contributes to the creation of more accurate forecasts. Also, Big Data improves the accuracy of machine learning algorithms by providing additional information about the data. For example, the analysis of historical data about stock prices helps to determine a more accurate forecast price.

These technologies are interconnected as Big Data can be used to train machine learning models. This, in turn, facilitates the discovery of patterns in the data, which allows to make accurate forecasts, better understand customers, conduct qualitative analysis and increase the overall company efficiency.