The built-in plugin can be used without installation.
You created a chart of sales changes and found that the sales volume in February dropped significantly when viewing the sales data for 2019. You want to analyze what caused the abnormal fluctuations in this month. Data Explanation can help you identify key influencing factors, and later adjust sales strategies based on these factors.
Data Explanation can automatically perform complex analysis, improving analysis efficiency.
You can use intelligently recommended dimensions to interpret the data, or customize settings according to your own needs.
Data explanation preview can be performed as long as there is a component preview interface.
All FineBI users can view data explanation, but only design users can configure data explanation.
Data Explanation cannot be used for the data points in the detail table and box chart.
Open Intelligent Data Explanation in Manage > System > General to make configuration take effect.
1. Click Save to make configuration effective after disabling Intelligent Data Explanation or reopening it after disabling.
2. Disable Intelligent Data Explanation to trigger manually configured data explanation to avoid the abuse of data explanation. For manual configuration, see Customizing Data Explanation in this article.
After creating a component, you can click a data point to display the Data Explanation option.
Click Data Explanation to open the data explanation panel on the right side.
Note: The dimensions used for data explanation are set by default. For the table used in this component, the top five items of all dimensions sorted by default are used as the explanation basis.
Display the situation, value, percentile rank and the ranking label among the overall values of the current data point.
The data explanation panel displays the top five key influencing factors for the current data point. You can click the drop-down button to view the contribution value and contribution rate of each factor.
For example, Respiratory as the Product category contributes the most to the current sales volume, accounting for 57.9% of the total.
Data Explanation allows for analysis at the current time point and comparison between two time points. The selectable comparison periods include year-on-year and month-on-month.
For example, select the data point for February 10 and choose MonthYear-on-year for the comparison period, you can see that the value for February 10 increased by 92,120,000 compared to the previous month, with a Change Rate of + 136.1%.
Click the dropdown button to view the contribution value and contribution rate of the key contributors that caused the change.
The month-on-month growth rate of Respiratory is 177.8%, and the contribution rate to sales growth is 64.3%, which shows the growth of sales in Respiratory is the most critical factor contributing to the growth of sales in February.
To perform Time sequence comparison, a time field and a comparable time type in the current component analysis area are necessary.
The time fields for grouping and the comparable time options are shown below:
Group by Time Field
Year-on-year (YoY), Quarter-on-quarter (QoQ), Month-on-month (MoM), Week-on-week (WoW), Day-on-day (DoD)
Other time grouping is unavailable for Time Sequence Comparison. For example, if the time grouping is set to Month Day, it will prompt: There is no time field that satisfies the condition.
The default data explanation is based on the top five items of all dimensions sorted by default in the table used in the component. However, some dimensions may not be needed for explanation, or certain dimensions may need to be combined as the explanation basis.
For example, to see which salesperson sells the most of a certain product, you only need two dimensions for explanation, and customize the dimension explanation basis.
1. Go to the component editing page, click Data Explanation to display the data explanation function setting panel.
2. In the setting panel, you can customize Dimension explanation basis by adding up to five dimension basis conditions. Each condition area can be dragged in no more than three dimension fields.
Note: If Intelligent Data Explanation is disabled, Default for dimension explanation basis in the setting panel will be grayed out, and you need to select Custom to set dimension basis conditions.
For example, to see which salesperson sells the most in which category, the dimension basis conditions can be set as follows:
In the dashboard, on February 10, salesperson e in Respiratory product contributed 12.2% to sales volume, and salesperson d in Respiratory product contributed 11.3% to sales volume. Both are key influencers.
Compared to the sales volume on January 10, you can find the sales growth of Respiratory product with shipping as the sales type contributed most to the overall sales. You can adjust the sales strategies based on the analysis results.