Nitori's AI Data Analysis Overcomes In-House Jargon

Nitori's journey highlights a common challenge: even after centralizing data in a modern platform, the knowledge gap between IT specialists and business users can limit the value. The company addressed this by enabling natural language queries, which lowers the barrier for store staff and others who are not data experts. This move aligns with Nitori's goal to understand not just what happened, but why, which requires deeper analysis of purchasing and sales processes.

The company's shift to BigQuery in late 2022 has already led to an increase in important tables for analysis to over 2,600, indicating a richer data foundation. However, the initial cost of running queries on BigQuery was a concern, leading to careful management. By letting users ask questions in everyday language, Nitori hopes to expand usage beyond a handful of experts, potentially reducing costs over time as more staff become self-sufficient in retrieving data.

The presentation at Google Cloud Next Tokyo suggests that Nitori's approach is seen as a model for other businesses facing similar challenges. The focus on separating BI tools into reference and search functions could help other companies design more accessible data systems. While the full impact on Nitori's operations is not yet detailed, the emphasis on enabling non-engineers suggests a broader trend toward democratizing data access in retail.