Create Atomic Metrics
Concept
Atomic metrics are indivisible metrics in business definitions and are the most basic metrics. Subsequent composite and derived metrics can be created based on atomic metrics. Atomic metrics are usually measures under a specific business event behavior, used to quantify and statistics the performance of products or businesses. The method is usually aggregation calculation, for example, Net Profit = sum(Order Net Profit), Total Transactions = count(distinct Order No).
Create an Atomic Metric
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Go to Metric Center > Metric Management, select a topic, click Add Metric, and select Atomic metric.
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Configure Basic info, Business attributes, Calculate attributes, and Management config.
Configure Basic Info
| Parameter | Description |
|---|---|
| Dataset | Select a dataset you have permission to use. |
| Name | Set the metric name. It must be unique within the topic. |
| English name | Set the English name. It must be unique within the topic. |
Configure Business Attributes
Business attributes describe the business meaning of a metric. Enter custom attributes or link an existing business metric from the business dictionary.
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Enable Customize business attributes to enter Business caliber and other attributes configured by the administrator, such as Department and Affiliation. The business caliber explains what the metric means and how it should be used.
For custom attribute configuration, see Metric Attribute Management.
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Disable Customize business attributes, then search for and select an existing entry in Link business metric. Its configured business attributes are synchronized to the metric.
For creation and management instructions, see Business Dictionary.
Configure Calculation Attributes
| Parameter | Description |
|---|---|
| Calculation method | With Use expression disabled, select a source field and its aggregation method, such as sum, minimum, or maximum. Enable Use expression to define a custom aggregation expression. The expression can calculate detail values from fields in the selected dataset, but its outermost function must be an aggregation function.  |
| Data Format | Set the display format of the result. |
| Applicable dimensions | Select dataset fields for dimensional analysis. Included time fields can be selected as the time dimension for time-based derived metrics. |
| Time dimension | Select a time field from the applicable dimensions for time-based derivations. |
| Filter | Add conditions to restrict the data included in the calculation. |
Expressions support global parameters. Dashboard filters can control parameters used by metric cards:
- You can set parameter defaults on a metric. If no metric default is set, the global parameter default applies.
- Priority: filter value > metric default > global parameter default.
- If multiple metrics on a card use the same global parameter, the filter value overrides the parameter defaults of all those metrics.
Configure Management Properties
| Parameter | Description |
|---|---|
| Save To | Select the metric's save location. |
| Owner | Select the person responsible for explaining the business caliber and ensuring its accuracy. |
Save the Metric
Choose whether to save a draft or save and publish the metric:
- Saving a draft preserves the configuration without publishing it.
- Saving and publishing makes the metric available to other features.
If a new metric has the same calculation definition as an existing metric, the system checks whether their applicable dimensions match and displays a duplicate-definition notice.
For the resulting status and draft indicators, see Metric Management: List Mode.
Configure Query Acceleration
After creating an atomic metric, open its detail page to configure query acceleration. For instructions, see Query Acceleration.
Examples
(1) Inventory Market Value
Calculation logic: Within the statistical date, the inventory market value of each store = Product Tag Price * Inventory Quantity
Table structure: Date, Store, Category, SKC, Price, Inventory Quantity
- Calculation caliber:
sum(Price * Inventory Quantity)
- Applicable dimensions: Store, Category, Date
(2) SKCs Available for Sale
Calculation logic: Count the distinct store/SKC combinations with inventory available for sale.
Table structure: Date, Store, SKC, Inventory Available for Sale
- Calculation caliber:
count(distinct concat(`Store`, '_', `SKC`))
- Applicable dimensions: Date, Store
- Filter: Inventory Available for Sale > 0
(3) R&D Person-Days Statistics
Calculation logic: R&D person-days = Total Work Hours / Standard Work Hours. Before 2025, the standard work hours per day is 7 hours; after 2025, it is 8 hours per day. Average person-days within the statistical date.
Table structure: Date, Total Work Hours
- Calculation caliber:
SUM(CASE
WHEN ([Date] < '2025-01-01') THEN ([Total Work Hours])/7
ELSE [Total Work Hours]/8
END
)
- Applicable dimensions: Date