Data Governance Practices and Lessons from Beike Zhaofang
This article outlines Beike Zhaofang's data governance framework, covering its purpose, three core dimensions—sharing, accuracy, and usability—how the scope is focused based on company characteristics, the necessity of data convergence and efficiency building, product architecture, target management, operational experiences, and challenges with corresponding measures.
Data governance aims to increase the value of data by ensuring it is shared, accurate, and usable; the article explains these three dimensions in detail, highlighting the need for transparent data access, clear business semantics, and reliable data formats.
The scope of governance is tailored to Beike Zhaofang's business characteristics, focusing on high‑frequency, real‑time data from the middle‑platform that supports front‑end applications, operational rules, and risk control.
Data convergence is necessary because fragmented data sources lead to high communication costs and low efficiency; consolidating data onto a unified platform improves accessibility, reduces manual effort, and supports AI, quality control, and fine‑grained operations.
Efficiency construction covers the entire lifecycle—from data production, testing, publishing, to query and permission management—providing self‑service testing, subscription, and quality monitoring within the platform.
The product framework categorizes data assets (interfaces, events, metrics, tables) into domain modules, enabling configurable development, standardized publishing, unified query entry, and self‑service subscription.
Governance project target management evaluates data discoverability, completeness of core field information, and data quality metrics such as bad case reduction, linking these to business satisfaction rates.
Product and operational rollout experiences emphasize deep user engagement, iterative feedback, and promoting data format standardization to drive data producers to adopt the platform.
Challenges include organizational alignment, strategic support, and technical dependencies; measures involve cross‑team collaboration, standard setting, and continuous innovation to enhance data value.
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