For the majority of data information customers, the most important thing is not the data analysis and processing process, but the interpretation and display of the big data analysis results (i.e. data presentation). Therefore, in a complete data analysis process, the interpretation step of data results is crucial. If the results of data analysis are correct but no appropriate interpretation method is used, the results obtained are likely to be difficult for customers to understand, and in extreme cases, they may even mislead customers.
There are many methods for data interpretation. The more traditional method is to output the results in text form or directly display the results on a computer terminal. This method is a good choice when facing a small amount of data: However, the data analysis results in the big data era are often massive, and the correlation between the results is extremely complex. It is basically not feasible to use traditional interpretation methods. At this time, you can consider improving data interpretation capabilities from the following two aspects.
1) Introducing visualization technology
Visualization, as one of the most effective means to interpret large amounts of data, was first adopted in the field of scientific and engineering computing. By comparing the visualization of analysis results, the results are presented to customers in an image-based way. This graphical method is easier to understand and accept than text.
Common visualization technologies include Tag Cloud, History Flow, Spatial Information, etc. Among data visualization tools, report tools include JReport, Excel, Shuipin Yibiao, FineReport, etc. BI analysis tools include Power BI.Style Intelligence, BO, BIEE, Xiangxing Technology ETHINK, Yonghong Z-Suite, etc. Domestic data visualization tools include BDP business data platform, big data magic mirror, data view, FineBI business intelligence software, etc. You can choose the appropriate visualization technology and tools according to specific application needs.
2) Customer participation in the analysis process
Allow customers to understand and participate in the specific analysis process to a certain extent. Here, human-computer interaction technology can be used to use the interactive data analysis process to guide customers to conduct analysis step by step, so that customers can better understand the origin of the analysis results while getting the results: data origin technology can also be used to help customers trace the entire data analysis process through this technology, which helps them understand the results of data analysis.
Report Writing
After the data analysis is completed, it is generally required to write a data analysis report, which is a summary of the entire data analysis process and a reference for corporate decision makers. It can provide decision makers with a scientific and rigorous basis for decision-making. An excellent data analysis report requires a clear theme and a clear framework to illustrate the data with pictures and texts and to show the results in a clear and orderly manner, so that decision makers can see the core content of the report at a glance. Finally, conclusions and suggestions need to be added, and solutions and ideas for solving problems need to be provided so that decision makers can use them as a reference when making decisions.