Filed under data-classification
Every brief tagged "data-classification".
- § 01 · IMPORTANT-DATA
'Important Data' Is a Category, Not a Tier
Hong Yanqing argues the mainstream reading of Article 21 of the Data Security Law confuses enterprise asset-inventory language with state-level legal-interest protection — with real consequences for cross-border transfers, enforcement, and how PIPL and DSL stack.
- § 02 · IMPORTANT-DATA
How to Identify 'Important Data' — A Plain-Language Method from Wang Qinglan
Wang Qinglan, head of compliance at a Chinese data exchange, walks through China's unique 'important data' concept in plain language: where it came from, why no other major jurisdiction has anything quite like it, how the U.S., EU, Japan and Korea solve the same problem differently, and — most useful for compliance teams — three methods to identify whether a dataset is 'important' in practice. Her own 'unorthodox' shortcut: ask whether a hostile foreign actor could use this data to cause trouble. If yes, treat it as important data.
- § 03 · CROSS-BORDER
FTZ Data Export Negative Lists — How 17 Sectors Across Seven Provinces Now Identify Important Data
Article 6 of the 2024 CBDF Provisions authorized Free Trade Zones to publish data-export negative lists. Since then, Tianjin, Beijing, Hainan, Shanghai, Zhejiang and others have published negative lists covering 17 sectors — automotive, pharmaceuticals, retail, civil aviation, reinsurance, deep-sea industry, seed industry, and more. Compliance Talker's analysis walks through the structural convergence of the negative lists, the important-data identification refinements each FTZ has produced, and the operational impact on enterprises both inside and outside the FTZs.
- § 04 · DATA-ECONOMY
What Is Actually Traded on China's Data Exchanges — A Bakery Metaphor
Per the Shenzhen Provisional Measures for Data Trading Administration, four categories of object can be traded on a Chinese data exchange: data products, data services, data tools, and other regulator-approved objects. Wang Qinglan walks through what each means in plain language with a bakery metaphor — wheat (raw data) becomes flour (data resources) becomes cakes (data products); a baker is a data service; the oven is a data tool. The piece is useful precisely because it answers a question overseas teams rarely think to ask: what are the data exchanges actually selling?