Gartner Research

Solution Path for Implementing a Comprehensive Architecture for Data and Analytics Strategies

Published: 28 June 2018

ID: G00351281

Analyst(s): Carlton Sapp

Summary

New analytic requirements need a flexible architecture for interoperating with diverse data sources and complementary analytic solutions. Technical professionals can use this step-by-step methodology to create a comprehensive architecture to support a data 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
    • Leveraging Artificial Intelligence and Machine Learning API Services to Complement Your Data and Analytics Architecture
    • Accelerate Delivery of AI Technologies With Machine Learning as a Service and/or Embedded AI Services
    • Get Started With Accelerating Delivery of AI Technologies by Focusing on These Drivers
    • 5.3 Automate and Scale
  • Step 6: Deploy and Integrate Analytics Into Operations
    • 6.1 Analytic Deployment Architectures
    • 6.2 Points of Integration for Existing Analytic Systems
    • 6.3 Model Deployment for Data Scientists
  • 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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