Abstract: The Data Big Bang that the development of the ICTs has raised is providing us with a stream of fresh and digitized data related to how people, companies, and other organizations interact. To turn these data into knowledge about the underlying behavior of social and economic agents, organizations, and researchers must deal with unstructured and heterogeneous data [1]. Technologies like the Internet, Smartphones, and Smart sensors are generating tons of digitized and fresh data about people and firms' activities that, if properly analyzed, could help reveal trends and monitor economic, industrial, and social behaviors. This new data paradigm is called Big Data, which refers to Volume, Velocity, Variety, and Value in the context of data analysis.
Identifying which data sources are available, what type of data they provide, and how to treat these data is basic to generate as much value as possible for organizations [2]. A Big Data architecture adapted to the specific domain and purpose of the organization contributes to systematizing the process of generating value. The Big Data paradigm also offers many advantages and benefits for companies, governments, and society. The purpose of this paper is to review some sources of Big Data to analyze social and economic behaviors and trends. A classification into three types of sources (article content, audiovisual/social content, and registration content) is made, together with a description of some databases and types of analyses that can be drawn from them. The aim is also to analyze how these sources can be used to analyze social and economic behaviors and trends, with examples that show the potential knowledge that could be achieved. Finally, the limitations and challenges posed by Big Data for social and economic analyses are discussed.

Keywords: Predictive analytics, IT infrastructure, government financial management, fraud detection, data integration, advanced analytics, machine learning, public sector technology, financial oversight, anomaly detection, real-time monitoring, risk assessment, decision support systems, data-driven governance, digital transformation, cybersecurity, automated auditing, cloud computing, artificial intelligence, financial data analysis.

| DOI: 10.17148/IJIREEICE.2021.91216