Webcast : ER/Studio Enterprise Team Edition

Creating a robust data model involves designing a model that is resilient, adaptable, and maintainable. Here are some guidelines to help you build a robust data model:

• Understand the business requirements: Begin by analyzing the business needs and requirements. This will ensure that your data model represents the domain and supports the desired functionality.
• Use a consistent naming convention: Adopt a consistent naming convention for tables, columns, and other database objects. This will make your data model easier to understand and maintain.
• Normalize the data: Apply normalization principles to reduce data redundancy and improve data integrity. This involves organizing the data into tables with well-defined relationships.
• Use appropriate data types: Choose the correct data types for each attribute, considering the data and the required storage space.
• Define primary and foreign keys: Define primary keys to identify each record in a table, and use foreign keys to establish relationships between tables.
• Implement constraints: Use constraints (e.g., unique, check, and not null constraints) to enforce data integrity rules and maintain data quality.
• Create indexes: Use indexes to improve query performance for large data sets. Be mindful of the trade-offs between indexing and write performance.
• Plan for scalability and performance: Consider the expected data volume and query patterns and design the data model to support efficient querying and data manipulation.
• Design for flexibility: Create a data model can adapt to changing business requirements. This may involve using more generic table structures, incorporating extensibility mechanisms, or using a modular design approach.
• Document the data model: Provide clear documentation, including entity-relationship diagrams and data dictionaries, to help users and developers understand the data model.
• Test and validate: Test your data model with realistic data and use cases to ensure it meets the performance and integrity requirements. Validate your model with stakeholders to ensure it aligns with their expectations.

Do you know what makes a great data model? What does it mean to be Third Normal Form or a Star Schema? When would you use one over the other and why? How can you identify bad designs? Join Leslie Andrews for a discussion on good and bad data models, and learn what you should do in order to create a perfect ten model of your own!

Speaker: Leslie Andrews is an IT professional with a passion for making improvements that help people to do their jobs more efficiently. She obtained her BBA with an MIS concentration from the Anderson School of Management at the University of New Mexico and has worked in the public sector for 15 years developing applications and databases. She enjoys spending time with her family, traveling, climbing, kettlebells, and reading epic fantasy; she is active in the SQL community, is a 2019 IDERA ACE, and on the Governing Board of a Charter School.