Data Product Design
Data Product Design shapes the meaning, logic and relationships behind your data. It creates the conceptual models and semantic structures that help your team understand information clearly and use it confidently across analytics, AI and operations.
Data Product Design helps shape data in a way that is logical, meaningful, future-proof, and usable.
- Tamara de Heij
Data Product Lead
& Founder of i-spark
Data Product Design sits between business language and technical reality.
It examines how your describe work, how you interpret the same concept in different contexts and how decisions flow through processes, products and data.
From this understanding, it shapes the structure behind your information. It also determines which logic belongs where.
This expertise anticipates how definitions might evolve, how domains might intersect and how choices made now will influence future products, systems and analytical work.
With strong Data Product Design, your data becomes easier to understand, maintain and extend.
A foundation that gives your data clarity, consistency and long-term structure.
Clear definitions for entities, relationships and boundaries that reflect how your team actually works.
Products built on meaning and logic, aligned with the business questions you need to answer.
Consistent definitions that reduce misinterpretation and keep metrics aligned across reporting, AI and operations.
Well-reasoned decisions on whether transformations live in source systems, models, semantic layers or product logic.
Data products that evolve easily, reduce duplication of logic and create efficiency across development cycles.
Clear semantics and structured meanings that support advanced use cases without rework.
You will work directly with our founder, who leads our Data Product Design expertise with a deep understanding of business logic and data. We explores how your team describe their work, how concepts are interpreted and which questions your data needs to support.
Through this process, we shape the meaning behind your data. We design conceptual models, determine where logic belongs in the pipeline and evaluate the long-term impact of decisions.
Your team gains a coherent structure that brings clarity, consistency and confidence to your data products.
A Data Product Lead shapes the logic and semantics behind your data products. They translate business questions into conceptual models, determine the meaning of data and design structures that support dashboards, analyses, AI agents and operational processes.
They consider where logic belongs in the pipeline, the impact of different design choices and how to maintain coherence across departments. Their work requires data experience and an understanding of long-term consequences, guiding your company toward data products that make sense and stay consistent.
i-spark guides you through understanding the entities and relationships that define your work. You also uncover which questions your data must support and which metrics need stronger semantic clarity.
This gives you a clear view of the structure that will help your organisation grow with confidence.
We work through the meaning behind each concept and the logic it needs to carry. This includes choosing where transformations should sit, how definitions hold up and which modelling decisions will remain stable.
What you receive is a set of data products created with by clarity around meaning, behaviour and long-term impact.
Questions we often hear about Data Product Design
Data Product Design creates the structure and meaning behind your data. It turns business questions into well-defined concepts, relationships and semantics, and shapes the logic that sits inside your data products. This gives you a clear and consistent foundation for dashboards, analyses, AI agents and operational processes.
A Data Product Lead focuses on meaning and semantics. They determine what your data represents, how definitions stay consistent and how logic should behave across domains.
A Data Architect focuses on technical layers, storage patterns and performance.
A Product Owner manages process and prioritisation.
An Analytics Engineer builds the models technically.
The Data Product Lead defines the structure and logic that all these roles depend on.
This expertise becomes essential when definitions drift, metrics no longer align, logic is duplicated across tools or when new dashboards or AI use cases create inconsistencies. It is especially valuable before scaling a data platform, introducing new data products or building semantic layers.
That’s completely normal. Most companiesstart with fragmented data. A good data strategy takes that into account, it identifies what’s missing, what’s usable, and what needs improvement to support better decisions.
Conceptual models ensure that analytics and AI are built on a clear understanding of the business. They define entities, relationships and boundaries in a way that supports accurate insights and predictable model behaviour. Without this foundation, downstream work becomes fragile and hard to maintain.
This decision depends on meaning, long-term stability and how the logic will be used. Some logic belongs upstream, some belongs in a transformation layer and some belongs inside the product itself. The goal is to place logic where it remains clear, consistent and adaptable over time.
No. The work focuses on meaning, logic and behavioural expectations. Your teams contribute their understanding of the business, and i-spark translates that insight into structured semantics and models.
Explore how stronger semantics and well-designed data products can support you.