Guangdong Mobile’s Data‑Weaving Practice: Building a Virtual Data Center for Big Data Governance

Introduction – Starting in 2017, Guangdong Mobile entered a digital‑intelligence phase, focusing on data‑weaving technology and its integration with AI large‑model capabilities to enhance data governance and optimize the big‑data service ecosystem.

Outline – The presentation covers four parts: (1) Guangdong Mobile’s big‑data development history, (2) background challenges, (3) a data‑weaving‑based data‑management solution, and (4) future outlook.

1. Big‑Data Development History – Over two decades, Guangdong Mobile built a data‑warehouse, migrated to a big‑data platform in 2014, and entered the digital‑intelligence stage in 2017, introducing data‑weaving, DataOps, large‑model AI, and platform‑engineering concepts.

2. Background – Rapid business growth exposed two bottlenecks: a centralized data‑supply model that cannot meet the storage, compute, and management demands of massive, heterogeneous data, and a traditional data‑center that struggles with unstructured data and multi‑scenario applications.

3. Data‑Weaving‑Based Management Scheme

3.1 Overall Framework – Guangdong Mobile built a “physically distributed, logically unified” virtual data‑center, forming a “central hub + peripheral wings” mechanism that drives business requirements and unlocks data value.

3.2 Physical Data Layer – Implements a “centralized collection, distributed storage” strategy, integrating BOMS‑domain data, applying “central + distributed” storage, “East‑data‑West‑storage” for cost‑effective tiering, and cloud‑edge collaboration to support new workloads such as video‑cloud and large‑model training.

3.3 Data Virtualization Layer – Deploys a unified collaborative compute engine across clusters; SQL statements are automatically split into operators that are dispatched to the appropriate clusters, achieving up to 90% reduction in data transfer and 8‑10× performance gains.

3.4 Data Management Layer – Establishes a unified global data‑asset view through standardized metadata, automated asset inventory, and AI‑driven quality monitoring (completeness, validity, accuracy, uniqueness, consistency). It also provides end‑to‑end timeliness monitoring via unified scheduling and large‑model analytics.

3.5 Data Operations (Peripheral Wings) – Offers a “digital‑intelligence assistant” built on large‑model, knowledge‑graph, and data‑weaving technologies, delivering intelligent recommendations for analysts, developers, and operators, and implements a unified tenant‑management system to ensure orderly multi‑tenant usage.

4. Future Outlook – Anticipates smarter, more automated data‑weaving with large‑model integration, multimodal data fusion, enhanced data‑sovereignty and governance, and open, standardized APIs and protocols to foster broader adoption.

Conclusion – Data‑weaving drives the evolution of Guangdong Mobile’s big‑data platform toward a virtualized data‑center, markedly improving data development, operation, and resource efficiency while enabling the monetization of data assets.