Customer Stories
K Telecom
Next-Generation System with Distributed Architecture
Integrated wireless and wired services with distributed architecture.
- ClientK Telecom
- ProjectNext-Generation Wired/Wireless System
- IndustryTelecommunications
- SolutionsDA#, META#, ETT#, Encore Distributed Methodology
- HighlightsIntegrated mobile/growth services and distributed architecture
Data End-to-End Expertise
Experts reduced project risks through precise data modeling and migrated 2+ systems and 40+ DTRs within 8 hours.
- Purpose & Background
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- Integration of wired and mobile BSS systems using a distributed architecture to ensure high availability and fault tolerance.
- Key Features
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- Design: Classify wired services into growth and maintenance categories, integrating growth services with mobile services.
- Development: Support the development of core SQL queries.
- Data Validation: Identify and cleanse erroneous data by developing Business Rule validation SQL for core services.
- Environment Support: Establish distributed execution and operating environments, and build a data hub.
- Zero-Migration: Minimize cutover downtime by shortening migration time.
Next-Generation Project Data Migration Process
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STEP 01
Current Data Analysis
- Identify terms, existing physical models, and relationships
- Leverage automated data modeling tools
- Reduce analysis costs and time
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STEP 02
Problem Identifica
- Simplify complex existing data structures
- Refine and enhance logical data structures
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STEP 03
Data Standardization and Governance
- Define standardized terms and codes
- Establish data architecture management processes
- Validate compliance with standards
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STEP 04
TO-BE Data Architecture Design
- Build an innovative TO-BE Data Architecture based on best practices and requirements.
- Define data modeling direction
- Establish data subject areas
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STEP 05
Enterprise Data Management and Control
- Reflect current operational service requests in the TO-BE Data Architecture
- Support data cleansing
- Manage model configurations and versions
- Manage data quality
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STEP 06
Data Governance Support
- Utilize metadata systems
- Register data modeling results
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STEP 07
Database Management and Consulting
- Optimize indexes
- Optimize complex SQL query performance
- Diagnose issues and recommend solutions
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STEP 08
Data Migration
- Establish migration strategies, develop migration processes, and validate data cleansing
- Identify potential risks in advance
- Apply specialized technologies for large-scale data processing
- Migrate a total of 3,070 tables