Industry Pain Points

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  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.

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  Inaccurate Decision-Making

  Lack of overall market planning, insufficient information technology investment, emphasis on business process-driven approaches over data-driven decision-making.

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  Inefficiency

  Small management scope, low work efficiency, fragmentation between IT and business departments, information asymmetry, and difficulties in internal collaboration.

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  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

To establish a unified and efficient enterprise-level data mid-platform, create a data management architecture that turns data into assets and assets into services, realize business data empowerment and an effective data management control mechanism, build data mining and analysis capabilities, forge a powerful data value-driven engine, enhance the efficiency of business refinement operations, accelerate the intelligent development of business, and promote the digital transformation process of the enterprise.

Solution Architecture

Customer Value

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  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.

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  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.

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  Data Security and Sharing

  Implement data asset sharing and exchange, and establish mechanisms for data quality and data security management.

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  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.

Customer Cases