Huolala's Data Governance Practices: Data Quality, Metadata, and Cost Management Platforms

Huolala emphasizes the growing importance of data governance and data quality in data development and data‑warehouse construction, and shares its practical experience in building a governance system, a data‑quality platform, and a metadata platform.

Data Governance System – The practice is divided into four key steps: organizational assurance (establishing dedicated governance teams),制度建设 (defining standards and processes such as data‑ingestion, development, and modeling specifications), project implementation (carrying out storage and compute governance actions) and platform support (building R&D tools to improve efficiency).

Data Quality Platform – Common quality problems (late table delivery, upstream errors, missing tracking data, report anomalies) are traced to business, technical, infrastructure, and management causes. The platform provides pre‑, in‑, and post‑process quality controls, a zero‑code rule configuration interface, automatic task generation, circuit‑breaker mechanisms that block downstream jobs on strong rule violations, and comprehensive quality reports with customizable scoring.

Metadata Platform – Offers data‑map, lineage analysis, model management, cost control, and asset management. It supports data‑asset discovery, lineage‑based troubleshooting, and security compliance. The platform also drives cost governance through resource budgeting, asset health scoring, and automated cold‑data archiving and lifecycle management, achieving up to 54% storage cost savings.

Future Plans – Enhance self‑driven data quality governance, extend quality checks to OLAP and real‑time scenarios, improve data‑lineage granularity, strengthen cost‑governance capabilities, and promote unified data models and standards across the organization.