Single technical solution is difficult to support multiple data analysis scenarios

It is difficult for a single technology to meet diverse analysis scenarios: enterprises need to face a variety of data types to achieve global data asset convergence; At the same time, it is necessary to support traditional warehouse analysis scenarios, but also to support big data analysis scenarios. Therefore, a single data lake or data warehouse faces capability challenges.

Data assets values are difficult to find and utilize

Enterprises generally face data silos, difficult to discover and understand data assets, accuracy and timeliness cannot be guaranteed, which lead to low data utilization rate, and difficult to empower data value and form the vision of data-driven business decisions.

Low ROI of data construction and operation

Decentralized technical architecture increases additional data movement costs, heterogeneous technical systems cannot effectively arrange and manage the system, system resource utilization is low, heterogeneous development tools are difficult to efficiently support data research and development, and reduce delivery and operation and maintenance efficiency, resulting in uncontrollable data construction and operation and maintenance costs