Transforming the way people manage their taxes
Jobis & Villains, which runs the SamjumSam AI accounting and tax-filing platform used by over 19 million registered users in Korea, unified previously siloed data pipelines on the Databricks Data + AI Platform with Unity Catalog and Databricks SQL. The company built a real-time streaming pipeline that triggers personalized prompts based on customer behavior, generating 2 billion KRW in additional sales, while cutting data engineering resource utilization by 20%.
Overview
Jobis & Villains, which runs the SamjumSam AI accounting and tax-filing platform used by over 19 million registered users in Korea, unified previously siloed data pipelines on the Databricks Data + AI Platform with Unity Catalog and Databricks SQL. The company built a real-time streaming pipeline that triggers personalized prompts based on customer behavior, generating 2 billion KRW in additional sales, while cutting data engineering resource utilization by 20%.
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Inspect the highlighted sourceThe challenge
Jobis & Villains' siloed data systems made it difficult to manage data in a unified way, creating data engineering overhead that slowed time to insight. Metrics were extracted differently by different analysts, the same metrics were created with different names, some data could not be processed in the existing AWS Glue catalog, and downstream analysis performance was unstable due to inconsistent data extraction criteria.
The solution
Jobis & Villains adopted the Databricks Data + AI Platform to unify previously scattered data into unified pipelines. Unity Catalog implemented unified data governance and controls, Databricks SQL let teams standardize and extract metrics from common data, and a real-time streaming pipeline on the platform enabled analysis of streaming customer behavior data, powering real-time prompts sent to users when they left a certain area within the platform.
Reported business value
The real-time streaming pipeline and targeted interactions generated 2 billion KRW in additional sales, and Databricks reduced data engineering resources needed to manage pipelines by 20%, freeing resources for new data platform tasks and future initiatives.
Sources
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