Common Challenges with Data Vault Data Modeling

Common Challenges with Data Vault Modelling

In a recent webinar hosted by the Data Vault User Group, Neil Strange, Chairman of the group, shared his extensive experience and insights on the common challenges faced in Data Vault data modelling. With a career spanning decades and training under industry pioneers like Richard Barker, Neil brought a wealth of knowledge to the session.

The Art and Skill of Data Modelling

Neil emphasized that data modelling is both an art and a social skill. It requires a blend of technical knowledge, business understanding, and language skills. Effective data modelling involves working at an abstract level while maintaining attention to detail, and it necessitates collaboration with non-technical stakeholders to ensure the model aligns with business needs.

Adapting to Agile Methodologies

One of the significant challenges Neil discussed is adapting traditional data modelling techniques to agile methodologies. Historically, data modelling is a lengthy process, but with the advent of agile, there is now a need for more flexible and iterative approaches. Neil highlighted how Data Vault's techniques facilitate this agility, allowing for incremental development and refactoring.

Key Challenges in Data Vault Modelling

Neil outlined several key challenges in Data Vault data modelling:

• Getting Started: Starting with a blank slate can be daunting. Neil recommended focusing on business problems and using a top-down approach to identify key concepts and business events before diving into the source data.

• Modelling to Extremes: Balancing between overly abstract and overly physical models is crucial. Neil advised finding a "Goldilocks zone" that is neither too abstract nor too detailed, ensuring the model is practical and useful.

• Units of Work: Understanding and defining units of work is essential in Data Vault modelling. Neil explained how these units help manage the scope and complexity of the data model.

• Breaking Standards: While standards are important, there are times when it may be necessary to deviate. Neil discussed the importance of understanding when and how to break standards without compromising the integrity of the model.

• Hub vs. Link Dilemma: Deciding whether a concept should be a hub or a link can be challenging. Neil provided examples and guidance on how to make these decisions based on the business context and data relationships.

Practical Examples and Solutions

Neil shared practical examples to illustrate these challenges and their solutions. For instance, he discussed the issue of upstream breaking schema changes and how Data Contracts can help manage these changes effectively. He also highlighted the importance of performing root cause analysis to address data quality issues at their source.

Conclusion

Neil concluded by emphasizing the importance of flexibility and common sense in data modelling. He encouraged data practitioners to apply judgment and adapt their models to the specific needs of their projects. By focusing on prevention, performing root cause analysis, and implementing Data Contracts, organizations can overcome common challenges and improve their data quality practices.

For a more detailed exploration of these topics, you can watch the full webinar above