Data Architecture and Data Modeling Differences: 
Achieving a common understanding

Many can be confused when it comes to data topics. Architecture, models, data–it can seem a bit overwhelming. This program offers a clear explanation of data modeling and of data architecture with a focus on the power of their interdependence. Both data architecture and data models are made more useful by each other. Data models are a primary means to achieve shared understanding of specific data challenges. They are literally the pages that intersect data assets and the organizational response. Data models, as documentation, are the currency of data coordination, used to verify integration, and are mandated input to any data systems evolution. Ideally, data architecture is the sum of the organizational data models, however, coverage is rarely complete. Anytime you are talking architecture, it is important to include the complimentary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner can organizations takes steps to better understand what they have and what they need to accomplish by employing data modeling and data architecture. This program's learning objectives include:
–  Understanding the role played by models
–  Incorporating the interrelated concepts of
    architecture/engineering
–  What is taught: forward engineering with a goal of
    building
–  What is also needed: reverse engineering with a goal of
    understanding
–  How increasing coordination requirements increase
    design simplicity