Data Governance Practice and Logical Closed‑Loop at KuaiKan: A Case Study

KuaiKan, founded in 2014 and now serving over 340 million users, faced rapid data growth across multiple business lines, leading to fragmented, low‑quality data and inefficient development processes.

The governance background highlighted three main pain points: exploding data volume, limited manpower for comprehensive governance, and the complexity of end‑to‑end data pipelines across business domains.

The proposed three‑step planning path includes: Step 1 – focus on a single core business to achieve quick wins; Step 2 – distill governance strategies from the pilot and apply them across other lines; Step 3 – replicate the MVP solution to reduce effort on new domains.

The logical closed‑loop consists of three layers: business scope management (tracking product changes, metrics, and priorities), data asset governance (standardized processes, tools, and metadata management), and application feedback (continuous improvement based on user experience).

Business scope management involves building a business‑process model, maintaining a metric hierarchy, and prioritizing data assets, all documented in internal knowledge bases and warehouse management systems.

Data governance standards cover the entire data lifecycle: source synchronization, ETL cleaning, warehouse modeling, development testing, task scheduling, quality monitoring, and platform tools such as metric management, data collection, metadata, lineage, and resource governance.

Collaboration techniques emphasize aligning data product analysts, developers, and business owners, starting with high‑impact core services, establishing MVP processes, and creating a unified feedback loop to evaluate and quantify governance effectiveness.

Effectiveness is measured by reducing metric duplication, increasing warehouse data reuse, and shortening development cycles, all of which have shown measurable improvements.

Future work includes scaling platform tools, extending cross‑domain governance, and strengthening data‑source (especially event‑tracking) quality management.

The presentation concludes with a Q&A about lineage storage (currently MySQL) and a call for audience interaction.