Data analysis is to convert a large amount of data into valuable information, in order to maximize the use of data function, play the role of data. The types of data analysis can be divided into status analysis, cause analysis and prediction analysis, which are divided into the following 6 steps according to the process:
(1) Clarify the purpose and thinking of data analysis
Clarify the thinking of data analysis and build a framework to decompose the purpose of data analysis into different analysis points, that is, how to carry out data analysis, from which perspectives to analyze, and which analysis indicators to use (all kinds of analysis indicators should be reasonably used). At the same time, ensure that the analysis framework is systematic and logical.
(2) Collect the data required for data analysis
There are four general data sources: databases, third-party data statistical tools, statistical yearbooks or reports of professional research institutions, and market surveys.
The data collection should be carefully checked and tested before release, because once the data collection is officially launched and there are problems, the required data cannot be obtained or the data is inaccurate, which will affect the subsequent data analysis.
(3) Data for data analysis
Data processing mainly includes data cleaning, data transformation, data extraction, data calculation and other processing methods, processing all kinds of original data into intuitive and viewable data required by users.
(4) How to realize data analysis
Data analysis is a process in which some analytical methods and tools are used to analyze the processed data and obtain valuable information to support decision-making.
Common data analysis tools, Excel PivotTable, can solve most of the problems.
Data mining is a more advanced data analysis method that reveals unknown relationships in data and can be used to predict the future, focusing on finding patterns and laws.
(5) Data presentation
Data is visually presented through tables and graphs. Commonly used data charts include pie charts, bar charts, bar charts, broken line charts, bubble charts, scatter charts, radar charts, etc. We can further process and arrange into the graphics we need according to the actual situation.
(6) Report writing
A good data analysis report, first of all, needs a good analysis framework, and illustrated, clear hierarchy, can let the reader at a glance. Clear hierarchy can make readers understand the report content correctly; The combination of pictures and pictures can make the data more lively, enhance the visual effect, and help readers to have a more vivid and intuitive insight into the problem, so as to make scientific decisions.
Is there a BI tool that supports all types of analytics and the entire data analysis process? Smartbi, a well-known BI tool in China, can do this. Smartbi provides data connection, data preparation, data analysis, and data application functions to meet users’ requirements for complex reports, data visualization, self-service exploration and analysis, machine learning modeling, predictive analysis, and natural language analysis.