Business Diagnostic Data Products: Thinking Model, Architecture, and Growth Challenges

The speaker, a senior data product manager at JD Retail, introduces the concept of business diagnostic data products, which are data solutions closely aligned with business operation analysis and decision‑making.

Key topics include:

What a business diagnostic data product looks like, illustrated by a value‑realization chain that moves from efficiency improvement to effect achievement.

The architectural form in retail scenarios, consisting of a foundational data‑asset layer (topic‑based data, governance, metric system) and an upper data‑application layer (consumer asset operations, full‑link business analysis).

A thinking model for building such products, split into internal (business cognition) and external (environment) perspectives, covering business awareness, product abstraction, product growth, industry insight, and competitor awareness.

Challenges when progressing from 0→1→100, focusing on data quality (consistency, latency, anomalies) and product planning (what to do and what not to do, moving from efficiency to effect, satisfaction to commercialization).

A Q&A section that distinguishes early‑stage challenges (establishing core metrics and visibility) from later‑stage challenges (leveraging rich data for new product launches and scaling impact).

The talk concludes with a roadmap for data product professionals, emphasizing stages of efficiency satisfaction, effect achievement, and business realization, and encourages continuous iteration and alignment with business goals.