The first step in big data mining in marketing research and forecasting is model building.

Data mining is the process of searching algorithmically for information hidden in large amounts of data. Data mining is usually associated with computer science and is accomplished through a number of methods such as statistics, online analytical processing, intelligence retrieval, machine learning, expert systems (relying on past rules of thumb), and pattern recognition.

Need is the mother of invention. In recent years, data mining has attracted a great deal of attention in the information industry, mainly because of the existence of a large amount of data, which can be widely used, and the urgent need to convert this data into useful information and knowledge. The information and knowledge obtained can be used in a wide range of applications, including business management, production control, market analysis, engineering design and scientific exploration.

Data mining is a hot research issue in the field of artificial intelligence and databases, and the so-called data mining refers to the non-trivial process of revealing implicit, previously unknown and potentially valuable information from a large amount of data in a database.

Data mining is a decision support process, which is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, databases, visualization techniques, etc. It is highly automated to analyze the enterprise's data, make inductive reasoning, and mine the potential patterns from it to help decision makers adjust the market strategy, reduce the risk, and make the right decision. The knowledge discovery process consists of the following three phases: ① data preparation; ② data mining; ③ result expression and interpretation. Data mining can interact with users or knowledge bases.