Is dbt lowering the bar for data modelling having a negative effect on data quality?

Data modeling is the cornerstone of data-driven decision-making. It's the art of translating a business's concepts, definitions, and activities into data structures. When done right, it empowers you to answer the crucial "why" questions by capturing the "who, when, how, and what" of a business. Moreover, it paves the way for future efficiency, reusability, and data consistency.

So, why do so many organizations still overlook the importance of a robust data modelling strategy?

This week, on The Stacked Data Podcast, I have the pleasure of hosting Rob, the Head of Data Product at Miro. Rob delves into the critical significance of data modelling, the common pitfalls to avoid, and shares invaluable insights on how to approach data modelling and effectively lead teams of data modellers.

🚀 Key Takeaways:

1. Know      Your Critical Concepts and Attributes: Define and design them upfront,      ensuring alignment across your organization. Regularly revisit and expand      your list of conceptual definitions.
2. Invest      in Ongoing Education: Constantly enhance the skills of your data      contributors. While not everyone needs to be a data expert, analysts      should grasp architectural principles, master their tools, and engage in      continuous learning. Rob, for instance, dedicates 5-7% of team time to      Learning and Development (L&D) activities.
3. Maintain      Your Models Like a Garden: Regularly dedicate time to review, refine,      refactor, clean, upgrade, and promote your data models. This should be a      shared responsibility and part of your sprint routine.