About the Webinar
Architecture, models, and data can seem overwhelming, especially at first. This program cuts through the complexity, offering a clear explanation of data modeling and data architecture and highlighting their powerful, interdependent relationship – a necessary partnership.Both data architecture and data models are made more useful by each other. And now that partnership and your efforts can be augmented using AI to aid the relevant knowledge workers.
This webinar defines and illustrates this interdependence and shows one way of supporting these design efforts. You will learn that data models are not just technical diagrams; they are essentially unlocking data value. Data models are a primary means to achieve a shared understanding of specific data challenges. They are literally the blueprints that document the intersection of data assets and organizational actions. 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. For most organizations, this means documenting as part of maintenance, or worse, trying to learn in a time of crisis. Anytime you talk about architecture, it is important to include the complementary role of engineered data models in the context of tradeoffs. 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
About the Speakers
Peter Aiken, Ph.D. is an acknowledged Data Management authority, an associate professor at Virginia Commonwealth University, president of DAMA International, and associate director of the MIT International Society of Chief Data Officers. For more than 40 years, Peter has learned from working with hundreds of Data Management practices in more than 30 countries. Among his 13 books are the first on making the case for data leadership (CDOs), the first focusing on data monetization and modern strategic data thinking, and the first to objectively specify what it means to be data-literate. International recognition has resulted from these and a (pre-Covid-19) intensive worldwide events schedule. Peter also hosts the longest-running Data Management webinar series on dataversity.net. Before Google, before data was big, and before Data Science, Peter founded several organizations that have helped more than 200 businesses leverage data – specific savings have been measured at more than $1.5 billion. His latest venture is Anything Awesome.
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