A model is developed for a purpose. This presentation explores the critical role of data modeling and data architecture as foundational to AI, analytics, or any subsequent use of data. Clear, documented data models act as the “digital blueprints,” enabling a common understanding among business users, technical personnel, and systems. Suboptimal data modeling practices accumulate "data debt" and lead to complex, brittle systems. Data models are a stable, reusable component of any system and are the most reliable means of conveying the enormous amount of information required for effective data management. Understanding the strengths of each of the three data modeling types will prepare you with a more robust analysis toolkit. Using reverse engineering analysis, delegates will follow the lifecycle of a set of data as it is prepared for subsequent use. 

Program learning objectives include: 

• How to incorporate AI in your modeling efforts  
• Understanding the role played by the  
  various model types 
• Differentiate appropriate use among  
  conceptual, logical, and physical data models 
• Understand the rigor of the round-trip  
  data re-engineering analyses 
• Apply appropriate use of various data modeling types 

Peter Aiken 

Professor of Information Systems, VCU and Founder, Anything Awesome 

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.