Enterprise Data at Huawei: Methods and Practices of Enterprise Data Governance

Foreword by Tao Jingwen
Why Does Huawei Promote Digital Transformation?
What Is the Digital World Like in Huawei’s Blueprint?
How Does Huawei Implement Digital Transformation?
Foreword by Xiong Kang
Foreword by Su Liqing
Preface
Introduction
Target Audience
Acknowledgements
Contents
1 Data-Driven Digital Transformation of Enterprises
1.1 Digital Transformation Challenges for Non-DNEs
1.1.1 Business Characteristics: Long and Extensive Supply Chains
1.1.2 Operation Environment: High Risks in Data Exchange and Sharing
1.1.3 IT Construction: Complex Data and Historical Problems
1.1.4 Data Quality: High Requirements for Data Trustworthiness and Consistency
1.2 Huawei’s Digital Transformation and Data Governance
1.2.1 Huawei’s Goals for Digital Transformation
1.2.2 Huawei’s Digital Transformation Blueprint and Data Governance Requirements
1.3 Huawei’s Data Governance Practices
1.3.1 Huawei’s Data Governance History
1.3.2 Huawei’s Vision and Goals for Data Work
1.3.3 Overall Approach and Framework of Huawei’s Data Work
1.4 Summary
2 Establishing an Enterprise-Level Integrated Data Governance System
2.1 Development of an Enterprise-Level Data Governance Policy
2.1.1 Data Management Guidelines of Huawei
2.1.2 IA Management Policy
2.1.3 Data Source Management Policy
2.1.4 Data Quality Management Policy
2.2 Data Governance Incorporating Transformation, Operations, and IT
2.2.1 Establishment of a Data Management Process
2.2.2 Relationship of the Data Management Process with Transformation Project Management and Quality and Operations Management
2.2.3 Decision-Making Through the Transformation Management System and Process Operation System
2.2.4 Integrating Data Governance into IT Implementation
2.2.5 The Role of the Internal Control System in Implementing the Data Governance Policy
2.3 Establishment of a Data Management Responsibility System
2.3.1 Appointment of Data Owners and Data Stewards
2.3.2 Establishment of Corporate-Level Data Management Organizations
2.4 Summary
3 Differentiated Data Classification Management Framework
3.1 Data Classification Management Framework Based on Data Features
3.2 Structured Data Management Centered on a Unified Language
3.2.1 Governance of Reference Data
3.2.2 Governance of Master Data
3.2.3 Governance of Transactional Data
3.2.4 Governance of Report Data
3.2.5 Governance of Observational Data
3.2.6 Governance of Conditional Data
3.3 Unstructured Data Management Centered on Feature Extraction
3.4 External Data Management Centered on Compliance
3.5 Metadata Management for Data Value Streams
3.5.1 Metadata Governance Challenges
3.5.2 Metadata Management Architecture and Strategy
3.5.3 Metadata Management
3.6 Summary
4 Business Transaction-Oriented IA Construction
4.1 Four Components of IA
4.1.1 Data Asset Catalog
4.1.2 Data Standards
4.1.3 Data Models
4.1.4 Data Distribution
4.2 Principles of IA Construction: Establishing a Common Code of Conduct at the Enterprise Level
4.3 Core Elements of IA Construction: Business Object-Based Design and Implementation
4.3.1 Business Object-Based Architecture Design
4.3.2 Business Object-Based Architecture Implementation
4.4 Expanding Existing IA to Business Digitalization: Objects, Processes, and Rules
4.5 Summary
5 Construction of a Data Foundation Centered on Connection and Sharing
5.1 Framework of Data Foundation Construction to Enable the Digital Transformation of Non-DNEs
5.1.1 Overall Architecture of the Data Foundation
5.1.2 Construction Strategy for the Data Foundation
5.2 Data Lake: Logical Aggregation of Enterprise Data
5.2.1 Three Characteristics of Huawei’s Data Lake
5.2.2 Six Standards for Data Lake Entry
5.2.3 Data Lake Entry Methods
5.2.4 Incorporating Structured Data into the Data Lake
5.2.5 Incorporating Unstructured Data into the Data Lake
5.3 Themed Data Linkage: Converting Data into Information
5.3.1 Application Scenarios of Five Types of Themed Data Linkage
5.3.2 Dimensional Data Modeling
5.3.3 Graph Modeling
5.3.4 Tag Design
5.3.5 Metric Design
5.3.6 Algorithm Modeling
5.4 Summary
6 Data Service Development Targeting Self-service Consumption
6.1 Data Services: Self-service, Efficient, and Reusable
6.1.1 What Is a Data Service?
6.1.2 Data Service Lifecycle Management
6.1.3 Data Service Classification and Development Standards
6.1.4 “One Day, One Week, and One Month” Requirements for Data Supply
6.2 Building a Data Map Centering on User Experience
6.2.1 The Value of Data Maps
6.2.2 Key Capabilities of DMAP
6.3 Everyone Can Be an Analyst
6.3.1 From “Babysitting” to “Service + Self-service Analysis”
6.3.2 Building Key Capabilities for Self-service Analysis
6.4 A Transformation from Result Management to Process Management: From Observation to Management
6.4.1 Business Operations Enabled by Data
6.4.2 Typical Data Consumption Scenarios
6.4.3 Huawei’s Journey and Experience in Data-Driven Digital Operations
6.5 Summary
7 Building the Full Data Awareness Capability of “Digital Twins”
7.1 A Full and Contactless Data Awareness Capability Framework
7.1.1 Origin of the Requirement for Data Awareness: Digital Twin (DT)
7.1.2 Data Awareness Capability Architecture
7.2 Hardware-Enabled Awareness Capabilities
7.2.1 Classification of Hardware-Enabled Awareness Capabilities
7.2.2 Implementations of Hardware-Enabled Awareness Capabilities at Huawei
7.3 Software-Enabled Awareness Capabilities
7.3.1 Classification of Software-Enabled Awareness Capabilities
7.3.2 Implementations of Software-Enabled Awareness Capabilities at Huawei
7.4 Driving the Digitalization of Enterprise Activities Through Awareness
7.4.1 Awareness Data in Huawei Information Architecture
7.4.2 Building Data Awareness Capabilities at Non-DNEs
7.5 Summary
8 Building Comprehensive Quality Management Capabilities to Ensure “Clean Data”
8.1 PDCA Data Quality Management Framework
8.1.1 What Is “Data Quality”?
8.1.2 Data Quality Management Scope
8.1.3 Overall Data Quality Framework
8.2 Comprehensive Monitoring of Abnormal Enterprise Data
8.2.1 Data Quality Monitoring Rules
8.2.2 Data Monitoring for Quality Control
8.3 Promoting Quality Improvement Based on the Comprehensive Data Quality Level
8.3.1 Operation Mechanism for Data Quality Measurement
8.3.2 Quality Measurement Design
8.3.3 Quality Measurement Execution
8.3.4 Quality Improvement
8.4 Summary
9 Building Secure, Compliant, and Controllable Data Sharing Capabilities
9.1 Internal and External Security Trends Driving Data Security Governance
9.1.1 Data Security: A New Battlefield for Competition
9.1.2 Changes in Data Security in the Digital Era
9.2 Secure Data Sharing in Digital Transformation
9.3 Metadata-Based Security and Privacy Protection Framework
9.3.1 Metadata-Based Security and Privacy Governance
9.3.2 Hierarchical Data Security and Privacy Management Policies
9.3.3 Hierarchical Solution for Managing Data Security and Privacy of the Data Foundation
9.3.4 Classification-Specific Identifiers for Data Security and Privacy
9.4 Data Protection and Authorization Management Based on Static and Dynamic Controls
9.4.1 Static Control: Data Protection Capability Architecture
9.4.2 Dynamic Control: Data Authorization and Permission Management
9.5 Summary
10 Data Is Becoming a Core Competency of Enterprises
10.1 Data: A New Factor of Production
10.1.1 When Data Becomes an Item on the Balance Sheet
10.1.2 Institutional Recognition of Data as a Factor of Production
10.1.3 Value of Data Assets Depending on the Market
10.2 Enterprise Data Ecosystem Involving Large-Scale Interactions
10.2.1 The Underlying Technologies of a Data Ecosystem
10.2.2 Data Sovereignty: The Core of Secure Data Exchanges
10.2.3 Purpose and Principles of IDS
10.2.4 The Role of Multi-party Secure Computing in Data Sovereignty
10.3 Evolution of Data Management Methods
10.3.1 Embracing the Future with Intelligent Data Management
10.3.2 Content Analysis of Data Assets
10.3.3 Intelligent Linkage of Primary and Foreign Keys Inspired by Attribute Features
10.3.4 Pre-discovery of Quality Defects
10.3.5 Algorithms for Data Management
10.3.6 Digital Ethics and Algorithmic Discrimination
10.4 Machine Cognition and the Four Worlds
10.4.1 The Singular Physical World and Manifold World of Human Cognition
10.4.2 The Digital World as a DT of the Physical World
10.4.3 The World of Machine Cognition
10.5 Summary