Shulou(Shulou.com)06/02 Report--

Today, I would like to talk to you about the four major misunderstandings of big data's analysis, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following contents for you. I hope you can gain something according to this article.

There is no doubt that big data's analysis has a great impact on modern society, and it has been widely used in various industries. Indeed, data analysis can help us make some decisions so that we can take scientific and appropriate action. But is the data analysis really 100% reliable? Next, let's talk about the four major misunderstandings of data analysis: wrong sample size, misjudgment of causality, neglect of silent users, and over-reliance on data.

The data analysis process needs to be properly used to improve effectiveness throughout the product life cycle, including all processes from market research to after-sales service and final disposal. Because of the validity and objectivity of the data, the data has become one of the most favorable means to explore the nature of the problem and find the laws of things. However, although the data are objective, they can sometimes be deceptive. In the process of dealing with data, we may often make some mistakes, resulting in a large bias in the conclusions of the analysis.

Myth 1: the selected sample size is incorrect.

At the 2008 Olympic Games, Yao Ming's 3-point shooting percentage was 100%, and Kobe Bryant's 3-point shooting rate was 32%, so does it mean that Yao Ming's 3-point shooting percentage is higher than Kobe Bryant? Obviously you can't say that, because at that Olympic Games, Yao Ming only shot one 3-pointer and Kobe Bryant made 53 shots. Therefore, in the comparative analysis of data, for the selection of samples, it is necessary to formulate the same sampling rules to reduce the deviation of analysis conclusions.

Misunderstanding 2: misjudgment of causality

Data from an e-commerce website show that the number of commodity reviews is proportional to commodity sales. That is, the more comments on a product, the higher the sales of the product. If we think that most reviews are the reason for high sales, the conclusions of data analysis will guide us that we need to create more product reviews to drive product sales. But if you really do so, you will find that the sales of many goods are not the same sensitive to comments, and even many goods sell very high, but it has nothing to do with the number of comments. Here, we need to think, is comment really an inevitable factor affecting sales?

In addition to comments, the factors that affect sales are its quality, price, activities and so on. If we can fully recognize these factors, then if we want to boost product sales, we will first need to consider from other angles, rather than comments. Therefore, in the analysis of data, the correct judgment of the logical relationship of data indicators should find the correlation between several rather than causality.

Myth 3: ignore silent users

Users urgently need the core requirements of ≠ products. Product managers make decisions when they hear feedback from some users and spend a lot of time developing corresponding functions. as a result, these functions may only be the urgent needs of a very small number of users, and most users do not care. Ignore silent users, do not fully consider the core needs of most of the target users of the product, may cause a waste of manpower and material resources, what is more, will miss business opportunities.

Myth 4: over-reliance on data

Over-reliance on data, on the one hand, will make us do a lot of worthless data analysis; on the other hand, it will also limit the inspiration and creativity that product managers should have. For example, by analyzing the data of the carriage, it is likely that we have come to the conclusion that the user needs a faster carriage. If we rely too much on data and limit our thinking, it is very likely that cars will not be born. Many good or even great product decisions are not found through data, but are the embodiment of the comprehensive wisdom of a product manager. Therefore, the data is objective, but the person who interprets the data is subjective. Only when we have a correct understanding of the data, can we use the data correctly. When doing data analysis, we must have a verification mentality when dealing with data, and we need to be on guard against those second-hand data that have been processed by others.

After reading the above, do you have any further understanding of big data's analysis of the four major misunderstandings? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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