Business categories, data domains, and data marts form a business-driven management framework. This framework establishes a closed-loop value chain from data production to consumption by classifying data ownership into business categories, defining core business activities as data domains, and organizing scenario-based data services as data marts. This topic describes the relationships between Business Categories, Data Domains, Business Processes, Data Marts, and Subject Areas, and explains how to use them.
Relationships between core concepts
Business Category: A business category is the highest-level division of a business. For example, in the retail industry, you can divide business into physical retail and E-commerce based on sales channels.
Data Domain: A data domain is a subject-based aggregation across different business lines. It typically divides a company's business data based on multiple dimensions, such as business type, data source, and data use. A data domain can belong to multiple business categories. For example, a transaction domain can serve both online and offline transaction scenarios.
Business Process: A business process is a specific business activity within a data domain. For example, a transaction domain can include business processes such as placing an order and making a payment. A data domain can contain multiple business processes.
Data Mart: A data mart is a collection of data tailored for specific business scenarios, such as a data mart for an operations platform.
Subject Area: A subject area divides a data mart into subjects based on analytical perspectives, such as product analysis and user behavior. A data mart can contain multiple subject areas.
Business categories
If your group or company has complex and large-scale business operations, you can create business categories to differentiate data. This helps you manage business data more easily.
For example, in the retail industry, common dimensions for classification include sales channels, product management lines, and core functions. You can use one of these dimensions to classify your business based on the principles of data ownership and business independence.
The following table provides examples of business classifications based on these common dimensions.
Division dimension | Scenarios | Business category example | Covered data scope |
Sales channel | Omnichannel retailer | 1. Physical retail business 2. E-commerce business 3. Cross-border business | POS transactions/App orders/Overseas warehouse inventory |
Product management line | Multi-category group | 1. Fast-moving consumer goods (FMCG) business 2. Home appliance business 3. Fresh food business | Basic SKU information/Product categories/Shelf life monitoring |
Core function | Single-channel, multi-department collaboration | 1. Procurement and supply chain business 2. Marketing business 3. Membership operations business | Supplier profiles/Promotional activity tables/Membership level tables |
Define a business category
In the navigation pane on the left of the Data Warehouse Planning page, click Business Category to navigate to the Business Category page. Then, you can define a business category.
Create a business category
On the Business Category page, hover over the
icon and click Create Level-1 Business Category.In the Create Level-1 Business Category dialog box, configure the parameters and click OK to create the category.
Create a sub-business category
If a level-1 business category has sub-business categories, you can create them. The method is the same as creating a level-1 business category.
Associate data domains
After creating a business category, you can associate target data domains in the Associate Data Domains area of the category page. This defines the data scope that the current line-of-business can access. After you complete the configuration, all associated data domains can be used for data modeling within this business category.
For more information about data domains, see Data domains.
Manage data marts
After creating the business category, navigate to the Data Mart Management area on the category page. You can view the list of data marts associated with the current business category. You can also edit or delete target data marts as needed.
For more information about data marts, see Data marts.
Deleting a data mart removes its association with the business category and deletes the data mart itself. Use caution when performing this operation.
Use business categories
After creating business categories, you can associate them when you create Dimension Tables, Fact Tables, Aggregate Tables, and Application Tables in Dimensional Modeling. You can also click the 
icon above the directory tree on the left to navigate to the model list page and view table classification details from a business data perspective.
You can also navigate to Data Metric and associate business categories when you create Atomic Metrics, Derived Metrics, and Compound Indicators in the common layer.
Data domains
A data domain is a high-level data classification standard created by abstracting, refining, and combining business processes. It serves as the primary grouping category for business users, helping them quickly locate their business data from a vast amount of information.
The following figure shows the relationship between business categories and data domains in the retail industry.
Define a data domain
Create a data domain
In the navigation pane on the left of the Data Warehouse Planning page, click Common Layer > Data Domain to navigate to the Data Domain page.
Click Create Domain. In the Create Data Domain dialog box, configure the parameters and click Confirm to create the domain.
System-default data domains cannot be deleted. Before you delete a data domain, you must delete all business processes and logical models within it.
Add a business process
After you create a data domain, you can view its details and create business data activities (business processes) for analysis based on this domain.
On the Data Domain page, click a created data domain to navigate to its details page.
After a data domain is created, the system adds a business process with the name suffix
_defaultto the domain by default.Click Create Business Process. In the Create Business Process dialog box, configure the parameters and click OK to create the process.
Use data domains
You can then reference the Data Domain in the following modules:
You can go to Dimensional Modeling to create Source Tables, Dimension Tables, and Aggregate Tables within a specific data domain.
The English abbreviation of the data domain can be used as an optional property for rule definitions when you add a new model rule in the Data Warehousing Level checker.
Business processes
A business process describes the flow of a business activity. For example, in E-commerce, adding an item to the shopping cart, placing an order, and making a payment can all be business processes. Business processes are typically used for business performance analysis. For example, in funnel analysis, the activity of purchasing a product is broken down into business processes such as browsing products, adding an item to the shopping cart, placing an order, making a payment, and confirming receipt. By counting the number of orders for each business process, you can perform a funnel analysis on the "number of orders" metric.
The following figure shows the relationship between business categories, data domains, and business processes in the retail industry.
Define a business process
In the navigation pane on the left of the Data Warehouse Planning page, click to navigate to the Business Process page.
Click Create Business Process. In the Create Business Process dialog box, configure the parameters and click OK to create the process.
To delete a business process, you can delete it directly from the business process list or from the Business Process list within its Data Domain.
ImportantBefore you delete a business process, you must first delete the associated logical models and metrics.
Use business processes
You can then reference business processes in the following modules:
You can associate a specific business process when you create a Fact Table in Dimensional Modeling.
In Data Metric, you can create Atomic Metrics, Derived Metrics, and Compound Indicators to measure business properties for each business process.
Data marts
A data mart provides personalized data for statistical analysis in specific application scenarios or products based on business categories. Data marts are usually located in the application layer.
For example, in the E-commerce business of the retail industry, you can build data marts such as an E-commerce mart and a retail customer portrait mart to serve the analytical needs of operations personnel.
Define a data mart
Create a data mart
In the navigation pane on the left of the Data Warehouse Planning page, click to navigate to the Data Mart page.
On the Data Mart page, hover over the
icon and click Create Level-1 Data Mart. In the Create Level-1 Data Mart dialog box, enter the parameters and click OK to create the data mart. The following table describes the key parameters.
Mart Type.
Business Mart: A data mart for business requirements.
Data Application Mart: A data mart for data product requirements.
Public Mart: Select this type if you want to create common application layer models for all data marts.
Business Category.
The Business Category to which the data mart belongs. For more information, see Business categories.
To delete a Data Mart, right-click the target mart in the data mart directory on the left and click the Delete button in the drop-down list that appears.
Create a sub-data mart
If a level-1 data mart has sub-data marts, you can create them. The method is the same as creating a level-1 data mart.
Manage subject areas
After creating the data mart, navigate to the Subject Area Management area on the data mart page. You can view the list of subject areas associated with the current data mart. You can also edit or delete target subject areas as needed.
Deleting a subject area removes its association with the data mart and deletes the subject area itself. Use caution when performing this operation.
Use data marts
After creating a data mart, you can reference it in the following modules:
You can associate the data mart when you create an application table for specific business data analysis in Dimensional Modeling.
In Data Metric, you can create Derived Metrics and Compound Indicators to measure business properties for each data mart.
Subject areas
A subject area is used to classify data within a data mart based on analytical perspectives. It is typically a collection of closely related data subjects and is ultimately used for statistical analysis in business applications.
For example, consider an E-commerce mart. It primarily serves the analytical needs of industry operations personnel. Its business category is retail. The data in the E-commerce mart is then divided into subject areas such as "product," "category," and "region" based on different perspectives. Later, when you create application layer derived metrics and application layer models, you need to associate them with the target data mart and subject area.
Define a subject area
Create a subject area
In the navigation pane on the left of the Data Warehouse Planning page, click to navigate to the Subject Area page.
On the Subject Area page, hover over the
icon and click Create Level-1 Subject Area. In the Create Level-1 Subject Area dialog box, configure the parameters and click OK to create the subject area.To delete a subject area, right-click it in the subject area directory on the left and click the Delete button in the drop-down list that appears.
Create a sub-subject area
If a level-1 subject area has sub-subject areas, you can create them. The method is the same as creating a level-1 subject area.
Use subject areas
After creating a subject area, you can reference it in the following modules:
You can associate the subject area when you create an application table in Dimensional Modeling.
In Data Metric, you can create Derived Metrics and Compound Indicators to measure business properties for each subject area.
Next steps
After you complete the preceding configurations, you need to define the data warehousing structure, plan the data warehousing levels, and set up checkers for each level. This establishes standards for subsequent Dimensional Modeling and Data Metric.
For more information, see Data warehousing levels.