At present, the continuous development of the Internet of Things, artificial intelligence, big data, and data analysis has allowed various industries to accumulate a lot of data, especially in the current era of the Internet, the data has been gradually increasing. However, whether certain data is useful and important to their respective companies requires judgment. This will gradually form the data analysis industry. However, novices who are new to the data industry estimate that data analysis and data mining are still not clear. Smartbi knows that it will Many people confuse the two, so now I will tell you the difference between the two.
Data analysis: refers to the process of analyzing a large amount of collected data with appropriate statistical analysis methods, extracting useful information and forming conclusions, and then the data is studied in detail and summarized. This process is also a supporting process of the quality management system. In practice, data analysis can help people make judgments in order to take appropriate actions. The mathematical foundation of data analysis was established in the early 20th century, but it was not until the advent of computers that practical operations became possible and data analysis was popularized. Data analysis is the product of the combination of mathematics and computer science.
Data mining: also translated as data exploration and data mining. It is a step in database knowledge discovery. Data mining generally refers to the process of searching for information hidden in a large amount of data through algorithms. Data mining is usually related to computer science and achieves the above goals through many methods such as statistics, online analysis and processing, information retrieval, machine learning, expert systems (relying on past rules of thumb), and pattern recognition.
The specific differences between the two are:
(In fact, the scope of data analysis is wide, including data mining. The difference here mainly refers to statistical analysis)
Data volume: The data volume of data analysis may not be large, but the data volume of data mining is extremely large.
Constraints: Data analysis starts from a hypothesis. It is necessary to establish an equation or model to fit the hypothesis, while data mining does not require hypothesis and can automatically establish an equation.
Object: Data analysis is often for digitized data, while data mining can use different types of data, such as sound, text, etc.
The result: Data analysis interprets the results and presents effective information. The results of data mining are not easy to interpret. The value of information is evaluated, focusing on predicting the future, and making decision-making recommendations.
Data analysis is a tool that turns data into information, and data mining is a tool that turns information into cognition. If we want to extract certain laws (ie, cognition) from data, we often need to combine data analysis and data mining. For example, Smartbi uses these two to process user data. Take an example: you go to the vegetable market with 50 yuan to buy vegetables. For a dazzling array of chicken, duck, fish, pork, and various vegetables, you want to match meat and vegetables. You ask the price one by one, and continue to perform statistical analysis to find out how much meat you can buy. How many dishes and probably how long you can eat, you get a set of information in your heart, this is data analysis. When you make a choice, you need to evaluate the value of this information. According to your own preferences, nutritional value, scientific collocation, meal time plan, the most cost-effective combination, etc., the value of this information is analyzed, Finally determine a purchase plan, this is data mining.
Smartbi uses the combination of data analysis and data mining to maximize the usefulness of data. If there is anything you don’t know, please go to the official website of Smartbi to consult, Wheat will serve you!