During data analysis, you may often need to use some basic analytical approaches such as time trends, drill-down queries, and comparisons.
This document introduces some common basic approaches to data analysis.
Prior to data analysis, you need to think about the analysis purpose and the information you want to convey to viewers.
During data analysis, the analysis result can only be intuitively displayed after you clarify the analysis purpose.
Creating a mind map can also be an effective solution.
The following table describes seven types of basic approaches to data analysis that you can use and provides an example for each approach.
Changing with time
Purpose: You can use time periods to describe a trend.
Drill-down query
Purpose: You can set the context to enable viewers to better understand the data information of specific fine-grained categories.
Zooming out
Purpose: You can describe the relationships between viewers' focuses and the holistic contexts, and the impact of specific content on the overall situation.
Comparison
Purpose: You can show the differences between two subjects or among multiple subjects.
Crossroads
Purpose: You can highlight the essential shift when the data of one type exceeds that of another type.
Factor
Purpose: You can elaborate on subjects by dividing subjects into different types or categories.
Outlier
Purpose: You can display the significant anomalies of anomalies or events.
Data display requires conciseness. You need to ensure that each component is useful. Besides, you can remove labels, titles, legends, or grid lines if you do not need them.
Components are common parts of data analysis in FineBI. You can set Adaptation Display for these data components. Adaptation Display allows you to adjust the component sizes, enabling the components to fit the dashboards to be created, as shown in the following figure.
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