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    Data Diagnosis

    Data Diagnosis

    Governance, Standards, Quality, Models, Performance, and Migration Comprehensive Data Assessment

    Identify data management issues and define optimal improvements through a comprehensive assessment.

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    Data Governance Diagnosis

    Data Governance Assessment

    Measure data management status and identify governance issues
    to define improvements for stronger competitiveness.

    • Business Process Diagnosis for Greater Competitiveness

      Analyze core business flows, remove redundant steps, and redesign processes.
      Reorganize data-linked operations for greater agility in changing markets.

    • Cost Savings Through Process Optimization

      Eliminate time and physical waste to reduce operating costs.
      Focus freed resources on key priorities for higher productivity.

    • Seamless Cross-Department Communication

      Use standardized guides and shared processes to remove collaboration bottlenecks.
      Reduce communication costs and accelerate execution toward shared goals.

    Optimize Processes Through Data Assessment

    Remove data-flow bottlenecks to maximize resources and cut costs.
    Standardized governance removes communication barriers, enabling faster decisions and agile execution.

    Data Governance Assessment Framework

    Data Standard Diagnosis

    Data Standard Diagnosis

    Data standards define and manage names, definitions, formats, and rules for data across systems.
    Assess standards management and identify improvements.

    • Anyone
    • Understands same meaning
    • Uses same way
    • Enterprise Data Standards for
      Consistent Data Assets

      Establish enterprise guidelines for consistent data quality.
      Apply them across the data lifecycle to improve transparency and governance.

    • Unified Enterprise Terms and Naming Standards

      Define standard words and terms to prevent inconsistent interpretation.
      A common language improves collaboration and enables faster decisions.

    • Consistent Data Use Through Easier Identification

      Standardize definitions and structures so everyone can identify and interpret data consistently.
      Lower data-use barriers and strengthen enterprise data literacy.

    Establish an enterprise-wide standards management framework to ensure data
    consistency and enable rapid decision-making based on a common business language.

    Unify data naming conventions and formats across disparate systems to strengthen enterprise-wide integration.
    Establish a standardized business language to eliminate communication barriers and improve data literacy,
    laying the foundation for data to become a practical asset that everyone can understand and use consistently.

    Data Standards Assessment and Management Framework

    Data Quality Diagnosis

    Data Quality Diagnosis

    Define data quality processes, assess current quality levels, and propose improvements.

    • Quality Management for
      Trusted Data Assets

      Assess accuracy and consistency to secure reliable data for business decisions.
      Maximize practical data use and foster a data-driven culture.

    • Continuous Quality Management

      Establish lifecycle-wide processes for sustained data quality.
      Set metrics and continuous monitoring to keep data valuable and responsive.

    • Root-Cause Analysis for Data Integrity

      Analyze data lifecycle vulnerabilities, not just visible errors.
      Address technical and management causes to protect integrity
      and reduce costs and risks.

    Enterprise Quality Governance for Data Value and Trusted Decisions

    Go beyond cleansing with lifecycle analysis and continuous monitoring to prevent errors at the source.
    Build a virtuous cycle of high-quality, practical data.

    Data Integration Modeling Process
    • Policy

      Enterprise-wide principles and guidelines

    • Organization

      Quality management roles and responsibilities

    • Process

      Quality measurement and change management

    • Elements

      Key quality management items

    • System

      Supporting tools and systems

    Key Data Quality Metrics and Criteria

    • DQI Data Quality Index

      A metric for measuring data quality
      (completeness, validity, accuracy, etc.)

    • CTQ Critical To Quality

      Key business data that directly impacts
      customers, processes, and markets

    • BR Business Rule

      Business-specific rules for accurate data management

    Data Model Diagnosis

    Data Model Diagnosis

    Verify that data requirements, business rules,
    and logical structures are fully and accurately defined for system development.

    • Data Model Integrity and Architecture Assessment

      Analyze model fitness and consistency to identify management limitations and technical debt.
      Find operational bottlenecks and establish a foundation for stable data asset management.

    • Root-Cause Analysis and Improvement Priorities

      Analyze causes of key defects, including data duplication and complexity.
      Set improvement priorities to manage future upgrade risks.

    • Practical Improvements for Next-Generation Systems

      Propose a roadmap for a scalable, flexible To-Be system.
      Design high-quality data structures for evolving business needs.

    Next-Generation Roadmap Through Data Model Assessment

    Resolve technical debt and bottlenecks with scalable data structures for agile change and asset value.

    Data Model Assessment Criteria

    Data Performance Diagnosis

    Data Performance Assessment

    Data performance reflects hardware, model design, and SQL access paths.
    encore begins with access path diagnostics to identify system-wide issues and recommend solutions.

    • Hardware Resource Assessment

      Analyze server utilization to identify availability, waste, and hardware bottlenecks.
      Provide a basis for model and SQL access path optimization.

    • Data Model and SQL Access Path Optimization

      Optimize hardware, models, and SQL access paths to resolve performance issues.
      Accelerate core business logic with a high-performance data architecture.

    • Performance Verification

      Monitor server usage after optimization to verify performance gains.
      Improve efficiency and response times without infrastructure expansion.

    System Performance Through H/W and SQL Optimization

    Analyze resource use and access paths to remove bottlenecks, optimize models, and improve IT availability and agility.

    3-Step Data Performance Assessment

    Data Migration Diagnosis

    Data Migration Assessment

    Data migration is critical to a successful system launch.
    encore identifies root causes and provides a validated recovery roadmap for error-free migration on schedule.

    • A precise approach to data continuity
    • encore Data Migration Assessment
    • Early Decision: Fix or Rebuild

      Analyze scripts and mapping logic to identify limitations.
      Choose between logic fixes and framework redesign to reduce sunk costs.

    • Fast-Tracking and Schedule Impact

      Identify dependencies, early migration, and parallel processing opportunities.
      Remove delays and optimize sequencing for timely, complete migration.

    • Guidelines for Repair or Re-Architecture

      Analyze scripts and mapping logic to clarify technical limitations.
      Use objective criteria to select repair or re-architecture for a successful launch.

    Data Model Assessment for Next-Generation Systems

    Resolve technical debt and bottlenecks with scalable data structures for change and asset value.

    7-Step Data Migration Assessment Process

    Key Outputs

    • Target Compliance

      Migration schedule and effort estimates

    • Direction

      Project Reboot vs. Restart decision

    • Recovery Plan

      Best practice implementation plan