What It's Like to Work as a Data Analyst at Kurly

Kurly's Data Farm Team shares the process of converting internal customers' ambiguous requests into clear analysis requirements. In the requirement organization stage, analysts communicate like a game of twenty questions to clarify the purpose of use, frequency, and detailed conditions. In the following data exploration stage, they collaborate with planning and development teams to verify technical details such as status values and load timing for tables like SHPDH and SPHDI.

The core is the separation of the production system and the analytics system. Since production data exists for system operation, using it directly for analysis makes it vulnerable to changes. As in the case of the inventory system change, Kurly built a structure that creates core tables to maintain the usability of analytics data even when the production system changes.

Data quality is achieved through rigorous QA and securing timing consistency. Outliers caused by device failures or worker mistakes are filtered out during the preprocessing stage, and data inconsistencies arising from batch time gaps—such as between order completion (11 PM) and shipment completion (X time)—are resolved by setting buffers. Using the finally secured data, Kurly conducts inventory operation ratio simulations for cases like splitting refrigerated centers, optimizing logistics efficiency and contributing to actual business decision-making.