Webcast : ER/Studio Enterprise Team Edition

Data modelers should avoid faulty design patterns because they can lead to a range of issues that reduce the efficiency, accuracy, and usability of the data model. By avoiding faulty design patterns, data modelers can create more efficient, accurate, and user-friendly data models that better support the goals and requirements of an organization. Some reasons to avoid faulty design patterns include:

• Poor performance: Faulty design patterns can cause inefficient data retrieval and storage, leading to slow query performance and increased resource consumption.
• Data inconsistency: Inaccurate or incomplete data models can cause data inconsistencies, making it difficult to maintain data integrity and ensure accurate reporting.
• Difficult maintenance: A poorly designed data model can be challenging to maintain and update, requiring more time and effort to address issues and implement changes.
• Reduced usability: Faulty design patterns can make it difficult for users to understand and interact with the data model, leading to decreased productivity and user satisfaction.
• Increased complexity: A poorly designed data model can introduce unnecessary complexity, making it harder to understand and work with.

To improve your data model design skills, you can learn to apply many best practices for model structures and patterns. But how do you determine which are the best patterns to use for specific designs? In this Geek Sync webinar, Steve Hoberman will explore a series of faulty design patterns and share lots of examples of data models that exhibit these patterns. You will learn how to fix each model, with the goal of making you a better data modeler by the end of this session!

Speaker: Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conferences, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award.

A good data modeler possesses a combination of technical and soft skills. By possessing these qualities and continuously honing their skills, a good data modeler can create effective data models that accurately represent the business requirements and help drive decision-making. Here are some of the main qualities of a good data modeler:

• Technical expertise: A good data modeler has a strong foundation in database design principles, data modeling techniques, and database management systems.
• Attention to detail: Data modeling requires a high level of precision and attention to detail. A good data modeler is meticulous in their work and can identify errors or inconsistencies in the data.
• Problem-solving skills: Data modeling involves complex problem solving, and a good data modeler possesses the ability to analyze complex data elements and relationships to identify solutions.
• Communication skills: A good data modeler has excellent communication skills to translate technical jargon into simple language that stakeholders can understand. They can collaborate with stakeholders and subject matter experts to understand business requirements and translate them into data models.
• Flexibility and Adaptability: A good data modeler is flexible and adaptable to changing business requirements. They can adjust data models to meet the ever-changing needs of the business.
• Domain knowledge: A good data modeler has domain knowledge in the area they are modeling. They understand the business processes and can accurately represent them in the data model.
• Continuous Learning: A good data modeler is always learning and keeping up-to-date with the latest trends and best practices in data modeling.