Data Modeling For Data Products
- Data Modeling, Data Products, & Everything in Between!
- Adapted Data Modeling Practices in a Data Product Era.
- Embracing The Right Tools & Frameworks
- Designing Scalable and Reusable Data Products
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Data Modeling For Data Products
Meet Your Data Guru
What You'll Learn In This Masterclass
Understand the concept of Data Modeling and what are data products.
Data Modeling: The process of defining the structure, relationships, and constraints of data. Aims to optimize how data is stored and accessed. Can be performed at different levels (conceptual, logical, physical) and using various methods (dimensional data modelling, data vault, etc.)
Data Product: A reusable, active, and standardized data asset designed to deliver measurable value by applying product thinking principles. It includes one or more artifacts enriched with metadata like governance policies and data contracts.Usually aligned to a specific domain or use case.
Understand how to build data models that are tightly aligned with business reality. From defining meaningful metrics to structuring flexible, domain-driven models teams want to use.
- Start with the business: Focus on real business needs, not just shifting data around. Anchor your models to actual use cases by collecting input across the org.
- Structure by domain: Organize data into domains that mirror how the business runs, even if the lines get blurry.
- Metrics come first: Defining key metrics early such as inputs, outputs, and drivers, should come before any technical modeling.
- Model in layers: Begin the modeling process with a broad conceptual model everyone can understand, then drill into domain-specific logical models.
- Own it together: Distribute ownership, but keep it coordinated to avoid a rigid, one-size-fits-all approach.
- Stay adaptable: Data models should evolve as the business does. Don’t default to popular frameworks. Pressure-test them and pivot when needed.
Understand the practical side of data governance - tools and frameworks that help data teams scale with clarity and control.
- Align with metric trees: Start with metric trees, a method to break down North Star metrics into smaller, influenceable components, aligning stakeholders across the business.
- Standardize with the semantic layer: A semantic layer connects those North Star business metrics directly to the data platform - standardizing definitions, supporting governance, and optimizing queries.
- Modernise dimensional modeling: On the modeling front, it highlights adapting classical dimensional modeling to modern needs, especially with entity-centric models where wide dimension tables become the core.
- Document everything: stress the importance of design docs living documents that capture ownership, inputs, outputs, quality expectations, and reasoning behind key data decisions.
- Build for scale and clarity: Together, these tools & Frameworks create a foundation for scalable, transparent, and well-governed data products.
Understand why building data products isn't just about pipelines. They are about designing for scale from day one with transparent processes, shared definitions, and governance that doesn't slow you down.
- Data products need structure: Data products are here to stay. And they require more than just good intentions. You need robust, iterative processes that support evolution, cross-team alignment, and business-value focus.
- Bridge business and data: Defining the business through metrics (like metric trees) and operationalizing them via semantic layers is key to bridging business and data. There's no one-size-fits-all modeling approach. Tools and techniques should fit your use case and tech stack.
- Governance is non-negotiable: None of these scales are without governance.
- Make documentation and automation core: Documentation and automated controls aren't afterthoughts; they're foundational. Build for change, align early, and govern always.
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