This topic describes the typical scenarios in which Dataphin is used.
Intelligent data warehousing on the cloud helps improve the efficiency of strategic decision-making
Scenario: A group that runs supermarket chains across China has many online and offline retail channels and various retail forms.
Pain points: The group has a large number of business systems and data sources. To improve business operations, the group needs to frequently retrieve various business data for further data analysis. However, the data system is complex and no unified rules are used to standardize the data. This makes it difficult to guarantee the speed, accuracy, and consistency of data analysis. Strategic decision-making and data-driven operations are adversely affected.
Solution:
- Data integration: Dataphin adopts data ingestion to integrate data from different business systems and unify the information to form a solid foundation of fundamental data.
- Data modeling: Dataphin allows you to design standard data models that constitute common data by using a top-down approach based on your business development requirements.
- Data production: After models are built, Dataphin automatically converts the models into code, generates data production tasks, and periodically schedules the production tasks to quickly respond to business requirements.
Value:
- Unified data construction: Data is standardized.
- Efficient data development: Code is automatically generated.
- Efficient strategic decision-making: The process of data analysis is more accurate. The data requirements can be responded to in a timely manner.
Recommended service combination: Dataphin + MaxCompute
For more information about MaxCompute, see What is MaxCompute?.
Theme-based data service exports membership data to drive efficient operations
Scenario: A large cross-province direct-to-consumer food and beverage company has online and offline customer contact channels and promotes the brand through the targeted marketing of popular products.
Pain points: The company rapidly expands its business and has a large collection of user data. The company wants to improve the efficiency of customer acquisition and retention, and receive more advantages from marketing and conversion. However, the user data in each customer acquisition channel is heavily dispersed, and the membership management system is too simplistic. Therefore, the recommendation accuracy is low, and membership marketing methods are limited.
Solution:
- Data integration: Dataphin can integrate data from different channels to your data warehouse through data ingestion. This plays an important role in enriching the solid foundation of fundamental data.
- Data modeling: Through data modeling and automatic coding, you can build a membership data model that integrates data such as member attributes and statistical metrics.
- Theme-based service: You can use ad hoc queries to automatically generate the aggregate data model of the member theme. Then, you can use the model to analyze daily member reports and build a robust member portal.
Value:
- Unified data construction: Data is standardized.
- Efficient data development: Code is automatically generated.
- Convenient asset management: Data is rich and integrated. The theme-based service is more intelligent.
Recommended service combination: Dataphin + Quick BI + MaxCompute
- For more information about Quick BI, see What is Quick BI?.
- For more information about MaxCompute, see What is MaxCompute?.