Volume because the number of data to be studied by these new systems is substantial. It is counted in terabytes. For information, the technical-scientific installations would produce the most data in the world. Velocity because to be effective, data processing must respond to a demand for speed. This is when a company sets up a fraud management system or banking system. The variety because this data can come from all types of systems: meteorological, online shopping, and publications on social networks. These are complex data from the Internet, most often in text and image format. Veracity because this data is used by sensitive systems such as banks, governments, or the stock market. In these areas, an error is impossible. Trust is, therefore, a key point in the management of Big Data. 

The Promises Of Big Data: In companies, the management of Big Data is a real challenge. When this data is well managed and processed in the right way, it can:

• Develop the trust of their customers by focusing their customer service on quality
• Get more complete answers because the amount of information is more important. This means more confidence in the data and, therefore, a different approach to solving problems.
• Optimize the efficiency of operational processes
• Renew their Business Model by discovering new sources of revenue
• Strategic Goals
• Development of methods for the implementation of predictive analytics, development of an automatic search for vulnerabilities in web applications
• Development of new technologies for storage and analysis of big data
• Development of breakthrough fundamental research
• Development of an educational platform and stimulation of the growth of the number of specialists
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• Creation of infrastructure to form an effective computing base of the widest possible range for existing and new applied problems

There are four main methods of Big Data analysis

• Descriptive analytics- It is the most common. It answers the question “What happened?” analyzes real-time and historical data. The main goal is to determine the reasons and patterns of success or failure in a particular area to use this data for the most effective models. For descriptive analytics, basic mathematical functions are used. Predictive analytics- It helps predict the most likely development of events based on the available data. To do this, use ready-made templates based on any objects or phenomena with a similar set of characteristics. With the help of predictive (or predictive, predictive) analytics, you can, for example, calculate a collapse or price change in the stock market. Or assess the potential borrower’s ability to repay a loan. Prescriptive analytics- It is the next level up from predictive. With the help of Big Data and modern technologies, it is possible to identify problem points in a business or any other activity and calculate under what scenario they can be avoided in the future. Diagnostic analytics- It uses data to analyze the causes of what happened. This helps to detect anomalies and random connections between events and activities.

• Descriptive analytics- It is the most common. It answers the question “What happened?” analyzes real-time and historical data. The main goal is to determine the reasons and patterns of success or failure in a particular area to use this data for the most effective models. For descriptive analytics, basic mathematical functions are used. 
• Predictive analytics- It helps predict the most likely development of events based on the available data. To do this, use ready-made templates based on any objects or phenomena with a similar set of characteristics. With the help of predictive (or predictive, predictive) analytics, you can, for example, calculate a collapse or price change in the stock market. Or assess the potential borrower’s ability to repay a loan.
• Prescriptive analytics- It is the next level up from predictive. With the help of Big Data and modern technologies, it is possible to identify problem points in a business or any other activity and calculate under what scenario they can be avoided in the future.
• Diagnostic analytics- It uses data to analyze the causes of what happened. This helps to detect anomalies and random connections between events and activities.