Data Architecture and Data Modeling Differences — Achieving a Common Understanding

DATE: May 11, 2021

TIME: 2 PM Eastern / 11 AM Pacific

PRICE: Free to all attendees

About the Webinar

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 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 a 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 about architecture, it is important to include the complementary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner, can organizations take 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

About the Speaker

Peter Aiken, PhD

Professor of Information Systems, VCU and Founder, Anything Awesome

Peter Aiken, an acknowledged Data Management (DM) authority, is an Associate Professor at Virginia Commonwealth University, past President of DAMA International, and Associate Director of the MIT International Society of Chief Data Officers. For more than 35 years, Peter has learned from working with hundreds of Data Management practices in 30 countries. Among his 10 books are the first on CDOs (the case for data leadership), the first describing the use of monetization data for profit/good, and the first on modern strategic data thinking. International recognition has resulted in an intensive schedule of events worldwide. Peter also hosts the longest-running DM webinar series (hosted by dataversity.net). From 1999 (before Google, before data was big, and before Data Science), he founded Data Blueprint, a consulting firm that helped more than 150 organizations leverage data for profit, improvement, competitive advantage, and operational efficiencies. His latest venture is Anything Awesome.

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