Data Governance: Core Questions, Practices, and Implementation in Meituan Delivery
This article examines the three fundamental questions of data governance—what to govern, where to govern, and how to govern—defines data assets, outlines stakeholder roles, presents a ten‑step implementation framework, and showcases Meituan Delivery's practical standards, architecture, metadata, security, and tooling for effective enterprise data governance.
The article explores three core questions of data governance: what is governed, where it is governed, and how it is governed.
It defines data assets, distinguishing them from raw data, and sets three essential criteria: assets must stem from business transactions, be owned or controlled by the enterprise, and be expected to generate economic benefit.
Stakeholders are identified as data producers, owners/controllers, and beneficiaries of data value.
Key coordination areas include data standardization (model, master data, key metrics), data rights (ownership and control), and process optimization to improve data quality and security.
Governance location combines small‑data manual standardization (mid‑platform) with large‑scale predictive analysis (backend), forming a hybrid "human + AI" model.
The implementation roadmap consists of ten steps: (1) identify symptoms and set goals, (2) analyze current state, (3) assess maturity, (4) perform root‑cause analysis of data quality issues, (5) evaluate business impact and prioritize, (6) create a data‑governance roadmap, (7) develop detailed action plans, (8) control project execution, (9) monitor and evaluate outcomes, and (10) pursue continuous improvement.
The Meituan Delivery case study demonstrates practical application: establishing business, technical, security, and resource standards; architecture governance using bridge tables and time‑bucketed dimensions; comprehensive metadata collection, modeling, and unified services; and security measures for sensitive data and data sharing.
Tooling includes Wherehows (a data catalog for discovery, lineage, and metadata) and QuickSight (a visualization platform enabling self‑service analytics).
In conclusion, the initiative achieved stronger data standards, more flexible architecture, enhanced security, and a complete metadata ecosystem, with future plans to adopt advanced databases, improve data products, and further lower data‑usage costs.
Signed-in readers can open the original source through BestHub's protected redirect.
This article has been distilled and summarized from source material, then republished for learning and reference. If you believe it infringes your rights, please contactand we will review it promptly.
Big Data Technology & Architecture
Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
How this landed with the community
Was this worth your time?
0 Comments
Thoughtful readers leave field notes, pushback, and hard-won operational detail here.