1, determine the report audience and analysis purpose.
No matter what kind of data analysis report you write, you must first find out who the report is for. Different audiences have different expectations for data analysis reports.
For example, an analysis report on the reasons for the decline of life insurance premium data of insurance companies, group leaders prefer to see the conclusions and suggestions of data analysis, while various business departments pay more attention to the specific business reasons that lead to the decline, so the focus of our report is different for different audiences. In fact, it is necessary to clearly understand the analysis purpose of the report, what problems to solve and what expectations to achieve.
2. Clear framework and ideas are the most important part of the output of data analysis conclusions. An excellent data analysis report should accurately reflect your analysis ideas and let readers fully receive your information. Therefore, when making a report, the framework and ideas should be clear.
3. Make sure the data is accurate.
It often takes more than 60% of the time to write reports, obtain and sort out data. It is necessary to plan data, coordinate relevant departments to organize data collection, export and process data, and then write a report. If the data is inaccurate, the analysis results will be meaningless and the report will lose its value. Therefore, when collecting and integrating data, it is necessary to pay attention to whether the data is reliable and verify the data caliber and data range.
Matters needing attention in writing analysis report
1. Data is just a means to quantify things, it represents an objective situation, there is no good or bad right or wrong, and there is no emotion.
Data has natural objectivity, whether we touch it or not, it has happened and will not change, so it is more like a "knowledge seeker" in the face of massive data. All we have to do is read it, analyze it and interpret it.
2. Define core analysis indicators according to business and understanding of product functions. The process of putting forward data requirements is often the source of a data analysis report, and all the analysis comes from the indicators you originally defined. Putting forward data requirements is a process of "defining product objectives, making assumptions according to the objectives and predicting product effects", which requires clear prediction and mastery.
3. Be bold and cautious in analyzing data.
We should be sensitive enough to the data, keenly discover the hidden information in the data, further put forward bold assumptions and questions through logical reasoning, and finally verify them through further tracking.
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