Gartner Research

Data Modeling to Support End-to-End Data Architectures


The complexity of data architecture demands deft data design. Technical professionals must model each data artifact, but different ones require different modeling techniques. Modelers must coordinate these techniques and retain one — conceptual data modeling — for gathering requirements from users.

Published: 08 February 2019

ID: G00377645

Analyst(s): Joe Maguire

Table Of Contents


  • Use Cases for Data Modeling
    • Conceptual Data Models
    • Business
    • Data Persistence
    • Runtime
    • Data Integration
    • End-User Representations
  • Strengths
  • Weaknesses


  • Create Appropriately Detailed Conceptual Models
    • Do Not Equate “Conceptual” With “Enterprise-Scale”
    • Do Not Misuse Generalization
    • Include Attributes
    • Deliver Something Useful as Soon as Possible
  • Consult Multiple Subject Matter Experts
    • Manage the Provincial View of Data
    • Where Possible, Cede Control to Users
  • Separate Solution Design From Problem Description
    • Within Limits, Relational Can Approximate Conceptual
    • NoSQL Design Is Not Conceptual

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