Industry Pain Points
Data Redundancy
Difficulties in data collection, numerous business systems, inconsistent interface standards, disorganized data management, low data quality, poor application effects, and a lack of unified indicator accumulation.
Inaccurate Decision-Making
Lack of overall market planning, insufficient information technology investment, emphasis on business process-driven approaches over data-driven decision-making.
Inefficiency
Small management scope, low work efficiency, fragmentation between IT and business departments, information asymmetry, and difficulties in internal collaboration.
Lack of Analytical Models
Primarily descriptive analysis, scenario analysis reliant on experience, scarcity of decision-making analytical models, and limited forward-looking capabilities.
Business Objectives
Solution Architecture
Customer Value
Big Data Infrastructure
Complete the establishment of basic big data infrastructure, define data standardization models, and set up data development, real-time computing, and data asset directories.
Data Asset Inventory
Unify the paths for data storage, management, computation, query, and utilization, and establish a framework, policies, and procedures for data asset management.
Data Security and Sharing
Implement data asset sharing and exchange, and establish mechanisms for data quality and data security management.
Data Empowerment for Business
Enable data assets to empower business operations, improve staff efficiency, achieve full lifecycle management of data, and realize closed-loop management of comprehensive data assets.
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