A team case with a clear reporting date
A China News Service story published by Chinanews Hunan on August 31 described Zhang Zhi, founder of Aidian Zhiying (Changsha) Data Technology, and a five-person team providing restaurant-delivery data analysis at Dazehu OPC Entrepreneurship Center. We retain the headcount as reported then, not as today's staffing or a basis for estimating revenue or profit.
The case suggests how people with different capabilities can collaborate continuously around a business need. AI-assisted development and analysis do not eliminate customer discussions, data checks or maintenance. The following analysis concerns multi-outlet data services, rather than undisclosed internal processes at this company.
Define what is being compared before comparing outlets
When a customer supplies daily reports from several outlets, first check date ranges, outlet identifiers and field meanings. A field called order value may include or exclude refunds, delivery fees or discounts. Asking AI to explain differences before checking definitions can mistake data differences for business changes.
Create a field guide recording sources, update frequency, owners and scope. Retain original values for unmatched fields and raise questions for confirmation. Decide how to compare them after the customer confirms the interpretation; do not invent missing records to make charts tidy.
Return anomalies to people who can judge them
A sales fluctuation is a reason to investigate, not proof of a cause. Outlet and operations staff may need to explain promotions, closures, stockouts or delayed records. Present the anomaly, relevant dates and original records together so the customer can establish what happened.
A team serving several businesses can maintain general checking rules separately from customer-specific explanations. General rules concern gaps and changing definitions; individual records explain a customer's outlet arrangements and practices. This prevents unchecked transfer of one customer's experience to another.
Sustainability depends on the update process
Further questions include how outlet lists are synchronized, corrections trigger recalculation, recommendations are approved and how much discussion remains after report generation. These tasks determine upkeep and whether customers can confidently reuse results. Related reading: Wu Jun and Guanhaodian's division between rules and AI.