Explore the distinctions between canonical data models and information assets in data modeling, and learn how logical data shapes application interfaces in this insightful analysis.
A new blog post of What’s Your Baseline was published on Sep 15, 2021:
Overview:
The article discusses distinctions between canonical data models and information assets, focusing on logical data’s application within models and interfaces. It addresses challenges encountered in data projects and sets the stage for discussions on physical data modeling in forthcoming articles.
Core content:
- The article differentiates between canonical data models and information assets and their applications in modeling.
- It highlights essential views in meta models, including conceptual/logical decomposition and physical data models.
- Challenges in integrating entities and clusters within data modeling are analyzed, especially in BPMN models.
- Strategies for enhancing application collaboration diagrams using clusters are presented, improving clarity in application landscapes.
- The article foreshadows further exploration of physical data modeling in subsequent discussions.
Learnings:
- Readers will gain insights into the differences between canonical models and information asset views within data architecture.
- The article emphasizes the importance of clarity when working with entities and clusters in data modeling.
- Utilizing logical data attributes in interface mapping can prevent future discrepancies caused by technical changes.
- Applying best practices for interface definitions enhances resilience to changes in data requirements.
- The upcoming discussion on physical data modeling promises practical insights for implementing models effectively.
The original content was published in English.
