Big data is used to train and apply machine learning models in banking, insurance, and healthcare. Combining data in a data warehouse is crucial in the development of machine learning algorithms in industry, which creates a problem of data security and limited computing resources. Artificial neural networks in big data are known for their efficiency and effectiveness for small data sets. They cover search websites, ranking algorithms, recommendation and citation systems. The purpose of this paper is to present the progress, challenges, and opportunities for future research regarding the use of artificial neural networks in big data analysis, and the result of possible achievements of certain advances.

The Internet allows users to establish a direct connection with information on the World Wide Web. According to statistics, 2.5 quintillion bytes of data are produced every day, which has brought the world into the era of big data. The growth of this data continues to grow exponentially. Large amounts of data are collected daily in the industry, which creates a problem for the computing resources available for centralized aggregation of this data in the data warehouse. Traditional methods such as econometric, statistical, and mathematical models were used to process this information.

The advent of big data has presented researchers with a serious challenge to explore how best to effectively analyze these voluminous data sets. Artificial neural networks (ANN) are used for big data processing, because of their ability to solve complex real-world problems better than more methodologies that are traditional. The introduction of artificial intelligence techniques in industry to improve product quality, optimize planning, maximize production and increase customer satisfaction is consistently high. Modern companies are now moving from simple predictive Analytics to prescriptive Analytics and beyond.

Artificial neural networks are models based on the brain in the Central nervous system; they are usually represented as artificial nodes or “neurons” in different layers connected together by synapses. The study of the properties of artificial neural networks was applied to solve a wide and diverse set of tasks that had to be performed in difficult tasks using rules-based programming. Neural networks and artificial intelligence are also applied to web search in ranking results and relevance as a method of learning and adapting to different user interests.