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    Data Quality

    Data Quality

    Quality Improvement & Certification Guidance

    Support systematic quality improvement and recognized certification
    to ensure data accuracy and reliability.

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    Data Quality Improvement

    Data Quality Improvement

    Data quality management defines and establishes systematic processes
    to maintain, improve, and manage data quality.

    • Continuous Quality Management

      Governance-aligned processes maintain quality
      throughout the data lifecycle.

      Ongoing monitoring and automated feedback
      prevent quality decline and protect data integrity.

    • PurposeBased Quality Measurement

      Set criteria by data purpose and importance,
      then use profiling and diagnostics.

      Validate business rules and provide quality reports
      for accurate, data-driven decisions.

    • Delivery & Operations Collaboration

      Enable technical handover and collaboration
      for stable operations.

      Clear R&R and real-time communication
      support agile operations after go-live.

    Systematic Quality Improvement &
    Purpose-Based Measurement

    Ongoing monitoring and automated feedback protect data integrity, while business-rule profiling improves trust.
    Collaboration between delivery and operations teams enables agile response to changing needs.

    Data Quality Improvement Process
    Key to Success
    Data quality improvement depends on ongoing management, not one-off projects.
    Close collaboration between delivery and IT operations teams is essential.

    Data Quality Improvement Cases

    Data Quality Improvement Example

    Continuous quality checks minimize data errors and ensure reliable data for information systems and AI.
    An operating model enables real-time monitoring and immediate action.

    • Enterprise Governance
      Based Quality Management

      Embed quality management from the governance framework stage
      to establish operations aligned with enterprise standards.
      Integrate lifecycle criteria into governance to close policy-practice gaps
      and enable transparent, consistent control of data assets.

    • Pre-Deployment Quality Checks
      to Minimize Data Error Risks

      Make quality checks mandatory before application (AP) deployment
      to prevent potential data errors.
      Validate business logic and data structures before release
      to protect production stability and data integrity.

    • Collaborative Quality Culture
      for Continuous Data Improvement

      Create a cycle where quality checks and continuous data improvement
      strengthen organizational competitiveness.
      Shared KPIs and measurable results encourage participation
      and make data quality a core organizational value.

    Systematic Quality Improvement
    & Purpose-Based Measurement

    Continuous monitoring and automated improvement protect integrity, while precise profiling improves data trust.
    Integrated delivery and operations enable agile governance for changing needs.

    Continuous Data Quality Operations & Performance Measurement Example
    Validation
    Cycle    		 BR
    Type · B    		 usiness
    Area    		 Target
    BR    		 Results
    Valid    		 Results
    Mismatch    		 Action
    Requested    		 In Progress Completed Data
    Cleansing    		 Data
    Model    		 Code
    Registration
    /Update    		 API
    Deployment    		 BR
    Update
    /Delete    		 Other
    Monthly Technical Customer 172 142 30 30 - - - - - - -
    CS 232 218 14 3 8 3 - - 2 - - 1
    Subscription 1,432 1,122 310 303 - 7 - - - 1 6 -
    Total 1,836 1,482 354 336 8 10 - - 2 1 6 1
    Best Practice at Another Company

    “Using a quality management system, we distribute automated diagnostic reports every morning
    and hold quality meetings every Monday.
    Our goal of zero unresolved issues helps prevent application errors in advance.”

    Data Quality Improvement Insight

    Data Quality Improvement Reporting

    Ongoing quality-check results are reported regularly to assess quality management and support data remediation decisions.
    Cumulative results by area help evaluate the operations team’s management effectiveness.

    • Scheduled Quality Management
      for Ongoing Data Trust

      Establish daily, monthly, and quarterly checks
      to continuously monitor data integrity.
      Use optimal check cycles to identify errors early, perform consistent remediation,
      preserve data asset value, and minimize operational risk.

    • Precise Checks & Visual Reporting
      for Data-Driven Decisions

      Provide leaders and practitioners with visual reports
      of key indicators and diagnostic results.
      Translate complex metrics into business insights that clarify severity and priorities,
      enabling faster decisions and real business value.

    • Decision-Making Collaboration
      for Fast Data Remediation

      Establish close communication between quality reporters and decision makers
      for timely, accurate remediation decisions.
      Share error causes and solutions in real time
      so corrective actions are implemented quickly and the organization responds as one.

    Scheduled Checks & Visible Quality Reporting

    Periodic checks maintain integrity, while visual reports support sound quality decisions.
    Cross-functional communication enables fast remediation and continuous response.

    Examples of Quality Check Reports

    • Highest Risk Area

      Common (36.0%)

      Enterprise-wide rule errors make this the top priority,
      as they reduce trust across systems.

    • Major Concern Areas

      B2B & B2C (Avg 29.8%)

      This area directly affects transactions and customer services
      and shows an above-average mismatch rate.

    • Volume Impact

      Subscription (361건)

      The mismatch rate (19.9%) is moderate, but this area has the most cases,
      requiring a review of revenue data consistency.

    Data Quality Certification Guide

    Data Quality Certification Guide

    Data quality certification is a government-recognized program that verifies data quality levels.
    Based on the Data Industry Act, encore streamlines data management standards
    and processes to support clients throughout the certification process

    • Government-Recognized Certification
      under the Data Industry Act

      Support systematic preparation for nationally recognized quality certification
      under the Framework Act on Data Industry Promotion and Utilization.

    • Comprehensive Certification Readiness
      Across Data Structure, Content & Governance

      Review model alignment, data integrity, and governance processes
      against certification criteria.

      Analyze requirements by review area to establish sustainable management
      and strengthen enterprise-wide data trust.

    • Tailored Quality Consulting
      for Certification Success

      Assess data maturity and define a practical roadmap
      for target certification levels.
      Support every stage—from gap remediation and evidence preparation
      to on-site inspections—for efficient certification.

    Comprehensive Assessment & Tailored Consulting
    for Data Quality Certification

    Review data structures and management systems to ensure certification readiness.
    Maturity-based roadmaps and hands-on support strengthen data trust and brand value.

    Data Quality Certification Support Framework