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Big data analysis of the common ideas, I believe that many inexperienced people do not know what to do, so this paper summarizes the causes of the problem and solutions, through this article I hope you can solve this problem.

The common idea of data analysis is an instructive methodology that can guide readers to analyze data in which direction.

But not limited to data analysis, these ideas are often helpful to problem analysis, allowing readers to find the entry point of analysis more quickly, or to analyze the problem more comprehensively.

The following introduces several common data analysis methods to bring some thinking directions for readers when thinking about problems.

(1) PEST analysis

Readers may think that some people can look at problems in a very macro way, with breadth and depth. To have such a view, in addition to the accumulation of knowledge, there are some ways to enable the reader to have such a state, such as mastering PEST analysis.

PEST is to analyze the problem from the four angles of politics, economy, society and technology, so as to find out what problems enterprises or individuals will encounter in the general environment, and what kind of solutions they have.

Obviously, this is a macroscopic way of thinking.

In addition to reminding readers to think macroscopically, this approach also requires readers to have a certain understanding of the macro environment (economic, political, social and technological), otherwise the analysis may be superficial.

(2) 5W2H analysis

This method can be used not only for data analysis, but also for problem analysis, task monitoring and other scenarios.

5W2H provides a good mnemonic function that allows readers to think more comprehensively or creatively.

5W refers to who, when, where, what and why.

2H refers to how and how much.

Readers should note that 5W2H is a teleprompter that only helps readers think in certain directions, and what each word means should be adjusted according to the scenario used.

For example, for "who" (who), readers can have different thinking points: who will do it? Who is in charge? Who needs cooperation? Who is the user base and what is the age structure? Is the number of people sufficient? Are people's abilities in place? Wait. For example, for how much, readers can have different thinking points: how much does it cost? What is the quality and quantity? To what extent can it be called ok? What is the color and size? Wait.

5W2H is just a mnemonic function, and the meaning of each word should be adjusted according to the application scenario. In essence, it is only a comprehensive analysis of the idea, used to guide specific operations.

(3) logical tree analysis

Also known as problem tree analysis, Xiao Cheng first gives an example:

The operation is easy to implement, and the reader can use the xmind tool introduced earlier by Xiao Cheng to complete the operation.

The key point is to subdivide the new problem by the problem, and eventually become able to solve the problem and give a solution. In addition, separate sub-problems, try not to be heavy and do not leak.

Xiao Cheng's view is that asking questions agrees with thinking, and if there is no problem, there is no thinking. Therefore, problem tree analysis is an effective way to promote thinking, and of course, it is also an effective way to analyze data.

(4) user behavior analysis

User behavior tracks can be defined as:

Cognition, familiarity, trial, use, loyalty

We can think about it from several links, define indicators to collect data, and finally do data analysis from these links.

(5) 4p marketing theory

4p refers to products, prices, channels and promotions.

When analyzing an enterprise, we can start from these four dimensions. 4p thinking, re-sample can be applied to the starting point of data analysis.

After reading the above, have you mastered the common ideas of big data's analysis? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

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