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This article mainly introduces the relevant knowledge of "what problems have been solved by big data's technology". The editor shows you the operation process through an actual case, and the operation method is simple, fast and practical. I hope this article "what problems have been solved by big data's technology" can help you solve the problem.

Big data needs to update the processing model in order to have stronger decision-making power, insight and process optimization ability to adapt to massive, high growth rate and diversified information assets. These information assets derive more valuable information based on objective data.

1. Recommend more suitable products according to the sales fee habit and demand, so the related service enterprises can make use of big data for accurate marketing, so as to achieve win-win and mutual benefit.

2. When enterprises encounter bottlenecks or industry difficulties, small and medium-sized enterprises can make use of big data's rapid response to do a good job in service transformation.

3. The links of enterprise strategic layout and resource allocation can find a sentence closer to the facts through big data, and at the same time provide an opportunity for traditional enterprises that must be transformed under the pressure of the Internet to keep pace with the times.

Therefore, we can conclude that business organizations using relevant data and analysis can help them achieve their goals of reducing costs, improving efficiency, developing new products, making more informed business decisions, and so on. Here are some problems that can be solved by big data's application:

1. Timely analysis of the root causes of failures, problems and defects may save enterprises billions of dollars every year.

2. Plan real-time traffic routes for thousands of express vehicles to avoid congestion

3. Analyze all SKU, price and clear inventory with the goal of profit maximization.

4. Push the discount information that he may be interested in according to the customer's purchasing habits.

5. Quickly identify gold customers from a large number of customers

6. Use clickstream analysis and data mining to avoid fraud.

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