Big data offers a competitive advantage for institutions that have been able to devise practical solutions to break down their complexity, analyze their content in order to achieve added value and rewarding returns; a good data analysis process leads to a sound, informed, clear and fast decision by decision-makers.

It also leads to faster identification of the appropriate str ategy, so data are used to make decisions and monitor progress towards the institution’s goals. The value of data in terms of decision-making is crucial and has a significant impact on the survival and development, or the development of institutions or not, and monitor the variables and future trends in decision-making [12].

Big data is important because it enables institutions to collect, store, manage, and handle large amounts of data; thus, obtaining the desired results, large diversity is required, and the size and speed of obtaining such data are very important in light of the development of scientific research methods.

Big data technology also has the ability to analyze sensor data, web sites, and social and behavioral social networking data.

The analysis of this data allows correlations between the independent data set to detect several aspects, such as forecasting business trends for companies, linking legal citations, combating crime, identifying traffic flow conditions, etc. These predictions also provide groundbreaking instruments for decision-makers to better understand clients and markets alike [13].

This new field of data science seeks to extract applied knowledge from data, especially big data that can be analyzed to reveal patterns, trends, correlations, and get insights from them, and ultimately to the scientific implementation of what has been learned. In addition, this field overlaps with all human activities, economics, finance, and business without exception.

Big data contributes to the decision-making process as a result of its special advantages for the business sector, including the following subsection.


The rapid growth in the application of data science in business is not surprising due to the strength of economic considerations in this science. In a competitive market, all buyers pay the same price, and the seller’s revenue is equal to that price multiplied by the quantity sold.

However, there are many buyers who are willing to pay more than the equilibrium price, and these buyers maintain a consumer surplus that can be extracted using big data to define the features of consumers.

In addition, charging consumers different prices based on their analyzed features allows companies to obtain the highest price that the consumer is willing to pay for a particular product. Determining optimal price discrimination or market segmentation using big data is highly profitable. This practice has been the norm in some industries, such as the aviation industry, but it currently extends across a wide range of products.

The gains from price targeting also enabled companies to offer discounts to consumers who could not afford the equilibrium price, thereby increasing revenue and expanding then consumer base.

Defining consumers with big data is an important reason for the high ratings of companies such as Facebook, Google, and Amazon, which offer products and sendees that rely primarily on customer data.


Big data science has been successful in systemic financial risk analysis. The world has become more interconnected than ever, and measuring these links promises a new vision in economic decision-making so that systemic risk is seen through the lens of networks as a powerful approach.

Data specialists are now using abundant data to build images of interactions among banks, insurance companies, brokers, and others. It is clear, for example, that the knowledge of the most interconnected banks will be useful, and the same applies to the information on the most influential banks in the market, which is measured using an intrinsic value-based approach.

Once these networks are built, data specialists can measure the degree of risk in the financial system, as well as the contribution of financial institutions to overall risk, providing regulators with a new way to analyze and ultimately manage systemic risk [14].


Artificial Intelligence is a modem computer science that searches for sophisticated methods of doing works and conclusions similar to those attributed to human intelligence. It is a science that first investigates the definition of human intelligence and determines its dimensions, and then simulates some of its properties by translating mental processes into the equivalent. From calculations, increase the ability of the computer to solve complex problems and make decisions in a logical and orderly maimer.

Artificial intelligence is one of the most fields at the present time, as it has proven its efficiency in multiple fields and has been applied in many business applications hr companies and economic institutions [15]. For example, the latter can make predictions and determine the relationships between economic variables in better and more accurate ways of statistical standard.

It can also be argued that the main reasons for the tremendous success of artificial intelligence are naturally due to two main reasons: the availability of large amounts of data to leant machines, the steady growth in the power of computing, and the development of special-purpose computer chips [16].

The most important applications of artificial intelligence are simulation models, learning algorithms, and artificial neural networks. They can be applied in the business sector, to predict financial market developments, the actions of economic variables influencing the institution's economic climate, and more [17].

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