2023-09-29-华为-2023云数据使能白皮书_56页_15mb

Data-Driven Enterprise Transformation Whitepaper Summary

Vision and Goals

• Core Vision: Leverage "Data + AI" capabilities to create a data-driven modern enterprise, enabling real-time decision-making and automation.
• Key Objectives: Achieve "terrace-like" management through data-driven insights, transition from point-to-point connections to万物互联 (universal connectivity), and enhance business intelligence stages.
• Enterprise Development Stages: Categorized into five levels (Initiation, Basic, Growth, Continuous Development, Industry Leadership) based on data application depth, determining business wisdom.

Challenges in Enterprise Data Capabilities

• Primary Issues:
  • Lack of deep understanding of data value, leading to focus on short-term gains rather than long-term benefits.
  • Inadequate enterprise governance and culture alignment with digital strategies.
  • Severe data silos, hindering effective data sharing and utilization.
  • Weak data security frameworks due to evolving regulations like GDPR and Data Security Law.
  • Insufficient technical platforms to handle large-scale, diverse data for advanced applications.
  • Difficulty in sustaining long-term investments and continuous transformation due to organizational and cost barriers.

Methodology and Solutions

• Standardized Approach: A methodology includes phases like project initiation, status surveying, blueprint planning, solution design, development, and operation promotion.
• Core Principle: Adhere to "Bi-model IT, Stand-alone but Not Broken" to integrate new and legacy systems seamlessly.
• Data-Driven Transformation: Focus on three pillars:管理体系 (management system), 技术平台 (technical platform), and 应用场景 (application scenarios), providing a holistic enterprise-level data enablement method.

Data Management System

• Governance Strategy: Establish clear data strategies, policies, organizations, and detailed management norms to ensure data cleanliness and usability.
• Data Asset Development: Build unified data lakes, standards, and sharing mechanisms to transform raw data into valuable assets.
• Operation Mechanism: Conduct ongoing data quality monitoring and operational improvements to sustain data value release and alignment with business goals.

Technical Platform and Products

• Platform Services: Huawei Cloud offers services like GaussDB (distributed and cloud-native databases) and GaussDB(DWS) data warehouse, supporting high-performance, elastic data handling.
• Product Portfolio: Includes DataArts Studio for data lifecycle management, MRS for big data processing, and ModelArts for AI development, enhancing intelligent decision-making and automation.

Success Cases

• Industry-Specific Transformations:
  • Glass manufacturing: Achieved energy savings and real-time process control through data integration.
  • Cement industry: Improved operational efficiency via data-driven production optimizations.
  • Beer industry: Enhanced supply chain with automated data processing.
  • Airport management: Enabled airport operations with real-time decision support.
  • Automotive: Streamlined supply chain and reduced inventory.
  • Steel industry: Data governance led to standardized and efficient operations.

Conclusion

This whitepaper outlines a comprehensive approach to advanced data and AI implementation, advocating for a phased, governed strategy to overcome barriers and achieve significant business value through modernization and digital transformation.