Launch Data Pipeline for ED&A

Launch data pipeline for delivery to global ED&A

Project Overview

Built a pipeline to prepare and deliver Korea launch and marketing analysis data in the format required by the Global ED&A (Enterprise Data & Analytics) team.

Challenge

The work required preparing launch and marketing analysis data in a format aligned with the global analytics model, while also linking migrated overseas accounts with a shared user identifier before delivery.

Solution

Launch data extraction and transformation pipeline

Designed an extraction and transformation pipeline so launch-related data could be prepared in a form aligned with the global analytics model.

Extracted launch data including DRAW records

Prepared data to fit the global analytics model

Transformed data for launch and marketing analysis use cases

Completed extraction within the committed schedule

Identity linkage and ED&A delivery flow

Connected migrated overseas accounts with a shared user identifier and structured the delivery flow so the data could be used in the global analytics system.

Linked migrated overseas accounts with a shared user identifier

Operated the delivery pipeline for Global ED&A

Connected data for use in the global analytics system

Composed the datasets delivered to ED&A

Tech Stack

Launch Data

Launch-related data extraction

Marketing Data

Data preparation for marketing analysis

Identity Linkage

Identifier linkage for migrated overseas accounts

ED&A

Delivery flow for the global analytics organization

Data Transformation

Data transformation for the global analytics model

Data Pipeline

Pipeline structure from extraction to delivery

Key Results

Extracted about 100 million launch-related records within the committed schedule

Delivered 65 million records to ED&A with shared user identifier integration

Built a preparation and delivery flow aligned with the global analytics model

Connected migrated overseas accounts with the identifiers used for global analysis

Learnings

Global analytics work depends on clear data preparation rules and identifier linkage

Delivery quality depends more on alignment with the target model than on extraction alone

A shared user identifier increases downstream analytical usability

Analytics pipelines still require strong business-domain understanding to design correctly

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