Shulou(Shulou.com)06/01 Report--

Big data analysis of the common misunderstandings, 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.

Beginners should be on guard against the following five misunderstandings when making data analysis:

Misunderstanding 1: despise and deviate from business

Data analysis originates from the business requirements and serves the business needs at the same time. Belittling and breaking away from the business will inevitably make the data analysis meaningless. Many data analysts are more focused on the technical level and do not know much about the knowledge and skills in the areas of marketing and management, resulting in a disconnection between their work and the business link. Some analysis reports look very beautiful, professional and complex, but they make the boss look very laborious, lack of business logic, and it is difficult to guide business practice.

Therefore, a qualified data analyst should not only understand analytical technology, but also understand business and management. Only by taking the business requirements as the core, focusing on the analysis ideas, and combining with the application of analysis technology, can the analysis results have practical significance.

Misunderstanding 2: methodology is supreme

Data analysis methodology is a train of thought model which plays a guiding role in the overall work of a data analysis project. With the development of commercial activities in modern society, many new business models and new marketing ideas are constantly emerging.

It is true that the mature analytical methodology in the past is practical, but every business, every analysis, should be based on the original method, requires data analysts to combine the new business model, analyze specific problems, and treat each analysis. It should be a posture in the face of new challenges, bold assumptions, careful verification, open thinking, and can not completely rely on similar cases in the past.

Myth 3: the purpose of the analysis is not clear.

In the face of huge amounts of data, we often feel as if we are in the sea, blind and at a loss. We often struggle with what analytical methods to use, what charts to make, what data we need, and what form to write reports.

For a project, first of all, according to the needs of the business side, make clear why to do data analysis, to solve what problems, that is, the purpose of the analysis. Then, aiming at the purpose of the analysis, build the analysis framework, select the analysis methods and specific analysis indicators, and clearly extract which data, which charts are used, and other analysis ideas, only have a clear understanding of the purpose of the analysis, will avoid the misunderstanding of analysis for the analysis, the more valuable the results and process of the analysis.

Myth 4: pursuing perfect algorithm

Some people hold a stubborn concept in data analysis, pursuing the so-called cutting-edge, advanced analytical technology that shows their technical level, thinking that the more advanced the analytical technology, the better, and the more sophisticated the more powerful it is. Obviously, there are ready-made, simple and very suitable solutions that are not adopted, but spend their time in the pursuit of data algorithms.

There is nothing wrong with pursuing technological progress and development, but we cannot blindly emphasize advanced methods. Save time, save resources, come up with cost-effective solutions is the work attitude that enterprises need, so whether it is high-level methods or low-level methods, as long as it can solve the problem, it is a good way.

Myth 5: data analysis is out of touch with business applications

"Analysis and application syllogism" is a common problem in the application of enterprise data analysis at present, that is, data analysts give the analysis report or scheme to the business side, and the business side implements the application, and the problems or bottlenecks in the business side application are not tracked and solved by data analysts, resulting in difficulties in business landing application. The reasons are not only the problems in the management's understanding and positioning of data analysis, but also the lack of business experience and negative work attitude of data analysts.

In the application scenario of enterprise data operation, the landing of business application is the key link of value embodiment, and data analysis should serve the business application. Therefore, marketing activities from planning to implementation, and then to landing application, each stage should have a close combination of data analysis, timely communication with all departments of the enterprise, sharing the results of data analysis, so as to reflect the real value of data analysis.

With the development and change of social business activities and the rapid expansion of data volume, the business needs of enterprises will continue to change, and the environment of data analysis will become more complex, so as data analysts, we should combine business needs, maintain the mentality of independent thinking, make bold assumptions, carefully verify, and guard against and avoid entering the misunderstanding of data analysis.

After reading the above, have you mastered the methods of big data's analysis of common misunderstandings? 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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