Data processing
Data processing refers to the appropriate processing of the collected data, cleaning and denoising. The collected data is extracted, and the relationships and entities are extracted from them. After association and aggregation, the data is stored in a uniformly defined structure. When extracting data, the data needs to be cleaned and sorted to ensure the quality and credibility of the data. Common data processing methods include cleaning, extraction, merging, conversion, calculation, sorting and grouping.
Data analysis
Data analysis is the core part of the entire big data processing process, because in the process of data analysis, the value of data will be discovered. Data analysis refers to the process of converting the collected data into information after processing, sorting and analyzing it.
Common methods for analyzing data include arrangement diagrams, cause-and-effect diagrams, stratification methods, questionnaires, scatter diagrams, histograms, control charts, correlation diagrams, system diagrams, matrix diagrams, KJ methods, program review and evaluation techniques, PDPC methods, matrix data diagrams, etc.
On the basis of data analysis methods, the analysis methods should be further applied to business needs. Analysis based on business themes will involve many areas, from the conversion rate of customers participating in cross-border e-commerce enterprise promotion activities, to the analysis of customer retention time, to the timeliness and accuracy of the connection between internal links, etc. Each type of data has unique indicators and dimension requirements, as well as analysis method requirements. The most important of these is to conduct analysis from the three major perspectives of marketing, operations, and customers.