Data Modelling: The Lost Art of Turning Inputs into Insights

In today’s data-driven world, business pour vast resources into data engineering pipelines, platforms, and processing often overlooking the true value driver in the solution, data modelling. Without well-defined models, data remains a raw asset rather than a business intelligence enabler. In this session, we will remind ourselves of the foundational principles of dimensional modelling and agile data warehouse development, drawing on the timeless work of Ralph Kimball, Lawrence Corr, and Bill Inmon.

From a data architect’s perspective, we’ll explore why modelling is not just a technical step but a business-critical discipline that transforms inputs into actionable outputs. You’ll learn how robust data entities bridge the gap between engineering and analytics, enabling clarity, consistency, and scalability in delivering valuable insights.

We’ll also discuss practical approaches for embedding modelling into modern data strategies whether you’re building a data warehouse, a Lakehouse, or a semantic layer.

• Understand the value of data modelling. Explaining why modelling is critical for transforming raw data into actionable insights and how it complements engineering investments. • Explore Proven Methodologies. Introduce key principles from Kimball, Corr, and Inmon to design dimensional and agile models that deliver business value. • Apply Modelling in Data Architectures. Demonstrate practical approaches for embedding modelling into data warehouses, Lakehouses, and semantic layers.