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    Data Analysis & Utilization

    Data Analytics & Utilization

    Portals, BI/DW, Data Lakes, Machine Learning & Graph DBs

    Build tailored analytics platforms and services that uncover enterprise data value
    and generate business insights.

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    Data Portal

    Data Portal

    Build a standardized, high-quality data portal based on governance to maximize enterprise data use.
    Provide a user-friendly environment where both IT and business users can search, request, and use data.

    • Intuitive, User-Centered
      Search & Access

      Provide advanced search and personalized data catalogs
      so anyone can quickly find the information they need.

      Give administrators data asset visibility and business users easy access through business terms,
      improving data literacy and speeding data use.

    • Governance-Based
      Standard Data Delivery

      Deliver cleansed and validated standard data under governance principles
      to ensure consistent, trusted use.

      Use enterprise standard terms and quality data to prevent interpretation errors
      and enable immediate analysis and decision-making.

    • Efficient Management
      Through Connected Tools

      Connect modeling tools and core solutions with IT infrastructure
      for integrated management from design to operations.

      Automate metadata collection and synchronization to shorten implementation,
      reduce manual effort, and create scalable data operations.

    Intelligent Data Portals & Governance-Based Data Supply

    Connect intuitive search with standardized catalogs to improve enterprise data literacy.
    Automated integrated management increases data availability and improves decision-making.

    Data Portal Architecture & Use

    Data Analysis & Utilization

    BI / DW

    BI/DW services extract, transform, and integrate enterprise data into centralized, large-scale repositories.
    They meet analytics needs by user group and deliver trusted data for informed decisions.

    • Centralized Governance for
      Enterprise Data Assets

      Centralize distributed data to improve management efficiency
      and control data asset value.

      Remove data silos, reduce duplicate management costs,
      and strengthen security and integrity for safe, efficient data use.

    • Unified Source Data
      Through Integration & Standardization

      Clean diverse source data to enterprise standards
      and integrate it into a single flow.

      Automate standardized transformations to ensure alignment
      and create a unified data pipeline across systems.

    • Tailored Analytics
      for Faster Data-Driven Decisions

      Provide intuitive, flexible analytics to turn integrated data
      into business insights.

      Self-service analytics and visual reports improve accessibility
      and accelerate decisions with real-time data.

    Centralized Governance for Enterprise Data Availability

    Unify source data to eliminate silos and provide tailored analytics,
    strengthening data security and integrity for real-time, insight-driven decisions.

    BI / DW Implementation Framework

    Data Lake

    Data Lake

    A data lake stores structured and unstructured data in raw form, giving analysts and scientists
    a flexible, scalable environment for exploration and advanced models.

    • Unified Repository for Structured
      & Unstructured Data

      Build an enterprise data lake for numeric data, documents, images, logs,
      and more.

      Flexible storage removes information silos and combines fragmented data
      for multi-dimensional business analysis.

    • Optimized Analytics
      & AI Development

      Provide curated data and environments for analysts and data scientists
      to train and deploy machine learning models.

      Standardized data processing shortens AI service development
      and turns data into intelligent services faster.

    • Data Orchestration
      for Efficient Operations

      Design and automate pipelines from collection to transformation
      and loading.

      Orchestration tools improve workflow visibility, reduce waste,
      and support stable processing under changing data volumes and loads.

    Integrated Data Lakes & Intelligent Orchestration

    Build standardized pipelines and optimized analytics on enterprise repositories
    to broaden business analysis, shorten AI service lead times, and improve operations.

    Data Lake Architecture

    Machine Learning

    Machine Learning Analytics Models

    Train organizational data to generate and operationalize insights through prediction, classification, and clustering.
    The process spans PoC validation, production rollout, and operations handover.

    • Customer Segmentation
      & Demand Forecasting

      Use machine learning to analyze behavior and purchase history
      for precise customer targeting.

      Combine time-series analysis and external variables to improve inventory efficiency,
      respond to market changes, and optimize marketing and revenue.

    • Anomaly Detection
      & Strategic Insights

      Analyze real-time data streams to detect and manage anomalies,
      helping prevent risks such as fraud and system failures.

      Reveal hidden correlations and new business opportunities
      to support objective decisions, operational stability, and competitiveness.

    • Sustainable Intelligent Enterprises
      Built on High-Quality Training Data

      Use encore’s data-cleansing expertise to secure quality training data
      and build reliable AI and intelligent operations systems.

      MLOps enables continuous feedback and retraining,
      creating business intelligence that evolves with changing market needs.

    Intelligent Analytics for Revenue Growth & Risk Reduction

    Combine machine learning and real-time streaming analytics for precise targeting and anomaly detection,
    delivering strategic insights and stronger business competitiveness.

    machine learning project example

    Graph Database

    Graph Database

    Graph databases model data through entities (nodes), relationships (edges), and properties,
    enabling intuitive visualization and rapid analysis of complex connections.

    • Relationship-Centric Data Storage
      with Nodes & Edges

      Model complex relationships through intuitive node-and-edge structures to manage multidimensional connections beyond conventional tables.
      A graph architecture centered on relationships accelerates context-based exploration and reveals hidden data value.

    • Intuitive Visualization
      of Complex Data Relationships

      Visual dashboards make complex networks
      and connections easy to understand at a glance.

      They reveal key patterns and nodes that are difficult to identify
      through text and numbers alone,
      lowering barriers to data analysis and strengthening enterprise-wide data use.

    • Intelligent Relationship Analysis
      for Business Context & Risk Detection

      Connect fragmented data into an integrated network
      to analyze causal relationships and interactions in depth.

      Predict the impact of data changes, trace hidden clusters and relationship paths,
      and enable anomaly detection and precise targeting—supporting more informed strategic decisions.

    Relationship-Centric Data Integration
    & Intelligent Correlation Analysis with Graph Architecture

    Connect business entities through nodes and edges to manage multidimensional relationships and enable intuitive visual analysis.
    Reveal hidden data value and strengthen strategic decision-making.

    Graph Database Modeling & Implementation Process