Gartner Research

Solution Path for Planning and Implementing a Data and Analytics Architecture

Published: 16 June 2017

ID: G00324344

Analyst(s): Jason Lewis

Summary

Data-driven organizations need a flexible, end-to-end architecture for integrating and analyzing diverse data sources at scale. This research provides a step-by-step methodology to help technical professionals envision, architect and implement a comprehensive data management and analytics strategy.

Table Of Contents

Problem Statement

Solution Path Diagram

Solution Path

  • Step 1: Enterprise Information Management
    • 1.1 Information Governance
    • 1.2 Master Data Management
    • 1.3 Enterprise Metadata Management
    • 1.4 Data Quality Assurance
    • 1.5 Information Life Cycle Management
    • 1.6 Privacy and Security
    • What to Consider Before Moving to the Next Step
  • Step 2: Acquire and Organize
    • 2.1 Ingest and Analyze
    • 2.2 Process and Transform
    • 2.3 Integrate and Store
    • What to Consider Before Moving to the Next Step
  • Step 3: Enable Data for Analytics
    • 3.1 Data Warehouse
    • 3.2 Data Lake
    • 3.3 Data Virtualization
    • 3.4 Logical Data Warehouse
    • What to Consider Before Moving to the Next Step
  • Step 4: Enable Business Insights
    • 4.1 Business Intelligence and Visualization
    • 4.2 Self-Service Data Preparation and Analytics
    • 4.3 Advanced Analytics
    • What to Consider Before Moving to the Next Step
  • Step 5: Extend and Automate With Artificial Intelligence and Machine Learning
    • 5.1 Real-Time Analytics
    • 5.2 Machine Learning
    • Architect for Machine Learning
    • 5.3 Operationalize and Scale
  • Iterate for Continuous Improvement
    • Align With Business Strategy
    • Governance
    • People and Skills
    • Account for Citizen Roles
    • Agile Database Development
    • Cloud Versus On-Premises

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