The Closed‑Loop Logic of Data Governance at Kuaikan Manhua

In a Q&A format, Kuaikan Manhua explains how its data governance forms a closed‑loop, emphasizing that governance is a long‑term process sustained by a cycle of governance, feedback, and re‑governance, with dedicated personnel monitoring each stage.

Using the analogy of emptying a reservoir, the company illustrates that the outflow (governance) must be faster than the inflow (data pollution) to gradually reduce contamination, mirroring the need for faster data cleaning than data generation.

The first part of the loop is business‑scope management, which tracks iteration changes, added or removed features, and manages business metrics and their priority levels.

Managing business scope is crucial because commercial experiments (e.g., distribution, IP expansion, monetization) often lag behind data construction; early tracking allows data teams to prepare for upcoming metric and source changes.

The second part is data‑asset management, which establishes governance standards, builds tooling, and applies these to efficiently govern core business data.

A feedback management mechanism is added to collect issues from data users—developers, analysts, data product owners, and business product teams—ensuring that problems are reported back to the governance team, thus completing the closed‑loop.

Closed‑loop summary: By tracing data sources, managing business processes and metric priorities, and leveraging tools and standards, Kuaikan Manhua conducts orderly data governance; continuous user feedback then drives ongoing improvements.

In business, track the source, especially the process and metric priorities; with tools and standards, conduct orderly governance, then use continuous feedback to iterate and improve.