The term "alternative data" sometimes known as alternative data or non-traditional data, is most often used to describe regionally specific, valuable information that is distinct from typical financial data. Alternative data does not require a specialized data network or administration and may be collected and processed in real time. As a result, "alternative data" has quickly evolved in the financial sector in recent years. "Alternative data" is a well-known illustration of "big" data since, to start, it's enormously large in quantity as shown by its scope and transmission. The second element is the real-time or very close-to-real-time nature of the data collection and transmission. Data types and data structures come in a huge diversity, which is the third component. This paper studies the different classifications and acquisition methods of alternative data, focusing on the different applications of alternative data in the economic and financial fields, respectively, the application of alternative data in forecasting, especially in macro-economic forecasting and user income forecasting, and the application of alternative data in capital markets, business analysis and decision-making.