Inside Ant Group’s Big Data Governance: Key Practices and Insights

Ant Group’s Big Data Governance Experience

This article shares Ant Group’s practical experience in large-scale data governance, organized into four sections: an overview of data governance, data quality governance, data storage-processing governance, and future considerations.

Key Focus Areas

Ant Group concentrates on five critical dimensions when governing data: architecture, security, compliance, quality, and value.

Architecture, security, and compliance : Ensure data flows safely across business processes while meeting privacy, anti‑money‑laundering, and other regulatory requirements.

Data quality : Deliver accurate and timely data to enable business confidence.

Data architecture and governance : Establish reusable, well-designed data models, standards, and master data to support collaborative development.

Data value : Distinguish between negative value (resource costs of storage, computation, and asset maintenance) and positive business value generated when data is consumed; aim to transform data from a raw resource into a future‑oriented product.

The presentation focuses on the two topics of data quality governance and storage-processing governance.

Guest speaker: Peng Huan, senior data R&D expert at Ant Group. Edited by Wang Wei, proofread by Li Yao, produced by the DataFun community.

Excerpt from the e‑book “A Plain‑Spoken Big Data Book”, Part 1. Scan the QR code to download the full electronic book.