1.Fully consider the growth of user scale and data scale, and prepare for the accumulation of data assets. 2.Can be adapted to a variety of data sources, a variety of methods to collect the full amount, throughout the user's product life cycle. 3.Collect enough comprehensive attributes, dimensions, and metrics to make the accumulated data assets more high-quality. 4.Improve the timeliness of data collection, thus providing timeliness of subsequent data applications.
1. It can access, convert, write or cache multiple sources of data inside and outside the enterprise, breaking the island and realizing the physical convergence of data. 2. Ability to combine subject domains/business functions to enable cross-functional insights from big data, including integrated decision-making capabilities across multiple subject domains. 3. Realize unified storage after internal and external data cleaning and integration.
1. The data asset catalog forms a complete map of enterprise assets, and based on the data asset catalog you can identify data management responsibilities and resolve data issues disputes. 2. Clarifies data ownership attribution to data subjects. 3. Realize the security and sharing of data assets through comprehensive governance of multiple areas such as metadata management, data standards management, data quality management, and data security management.
1. Provide project management/requirement management capabilities. 2. Provide offline data development, real-time data development and ETL development capabilities. 3. Provide unified management capabilities for data models.
1. Provide task scheduling function during data development. 2. Provide data monitoring and resource monitoring functions in the data operation and maintenance phase.
1. Provide diverse service approaches for data users, and develop different data service strategies according to the roles and needs of users. 2. The main modes of service provided include: online data services, data service subscription, dataset and data API services, etc.