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AI WebRTC (H Company)

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Key Takeaway

Standardized legacy data structures to improve data quality and increase analysis reliability

Redesigned fragmented user and dashboard data into a Base–Middle–Aggregated structure, and significantly enhanced data utilization scope and analysis reliability through Fact table restructuring and user data correction.

AI WebRTC (H Company)

Client :AI WebRTC (H Company)

Industry :Telco / Media / Software / Data & AI

Service Area :Data & AI

Applied Solution :AIR

1. Overview (Project Background)

This project was initiated to improve data quality degradation and management complexity caused by legacy table structures used in the dashboard-based analysis environment.
Previously, different Fact tables and user-related data were fragmented by dashboard, limiting data utilization scope and consistency.

Accordingly, legacy tables were reorganized into a Base → Middle → Aggregated structure from a DWH perspective,
and the goal was to secure both data quality and reusability through integrated user data management, alternative table configuration for user_ft/cohort, and monitoring pipeline establishment.


2. Solution (Resolution Approach)

Improvement work was performed focusing on data structure standardization and expansion of utilization scope.

  • Fact Table Structure Improvement
    Organized Fact tables that were separated by dashboard and redesigned them into a common Middle and Aggregated table structure

  • Expansion of Data Utilization Scope
    Expanded the range of analyzable data through adjustment of population criteria and addition of columns

  • Azar Web Data Integration
    Integrated existing Legacy tables into Base and Middle table structures

  • User Data Correction Work
    Corrected missing and inconsistent data in users and azar_user_dm tables and corrected column meanings


3. Result (Achievements)

Through data structure improvement, consistency and usability of the analysis environment have been greatly enhanced.

  • Fact Structure Integration
    Integrated Fact tables that were separated by each dashboard into common Middle and Aggregated tables

  • azar_du_match_ft Improvement
    Expanded data scope to enable data used only for specific dashboards to be utilized across all dashboards

  • azar_dt_user_ft Expansion
    Added Session, order, match, inventory, and login info data, and enhanced analysis utilization through new columns

  • Web Event Log Integration
    Designed event logs individually used in multiple web dashboards to be queryable from a single Middle table

  • Legacy Logic Reimplementation and Integration
    Reimplemented existing azar_cohort_user_fact_daily logic based on Base tables,
    and integrated calculated metrics into existing Middle tables (azar_dt_user_ft, azar_dt_user_history_ft, azar_user_dm)

  • User Data Consistency Improvement

    • Corrected missing deletion_timestamp data in users table

    • Improved app_type logic that could not distinguish cheero data

    • Separated reg_country_cd column to match its actual meaning and added new registration country code column

Expected Effects

The following effects can be expected from this improvement.

  • Standardization of data structure and naming conventions

  • Strengthened data lineage management system

  • Systematic management and reusability of analysis deliverables

  • Improved change management process and collaboration efficiency

  • Foundation for introducing anomalous data pre-processing process

Through this, we have secured a foundation to more stably expand the efficiency and results of future data quality improvement work.

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