Data Architecture
With the right structure in place, your team builds faster, decisions become easier, and your data platform stays cost-efficient as your needs evolve. Strong foundations lead to less friction and more confidence.
The ability to integrate and utilize data effectively is a key driver of success.
- Jeroen Visser
CTO, i-spark
Every system, report, model, and AI workflow depends on the same thing: a stable backbone that reliably moves data. Data architecture shapes that backbone. It defines how information flows, how systems connect, and how your platform grows without friction.
You might be experiencing slow pipelines, unpredictable performance, inconsistent definitions, or rising platform costs. These symptoms usually point to a foundation that needs a clearer structure.
A strong architecture provides that clarity. With the right blueprint, you gain a data foundation that is easier to build on, manage, and scale.
A foundation that keeps your data landscape stable, scalable, and ready for everything you want to achieve.
A well-designed architecture gives you a complete view of how systems connect and how your data platform supports analytics and operational needs. It becomes easier to build, easier to maintain, and far easier to scale.
Clear modelling principles, storage choices, and historisation logic create dependable foundations. Your teams gain structures they can trust as your organisation grows.
Defined patterns for APIs, ELT, CDC, and streaming make data movement predictable. Your team will spend less time fixing pipelines and more time creating value.
With thoughtful workload design and compute planning, your data platform will respond faster and stay efficient , supporting dashboards, ML, and AI workloads.
Security frameworks and platform guardrails strengthen trust. Access, protection, and compliance become clearer, reducing operational risk.
Practical guidance on technology choices and architectural decisions prepares your team for AI, automation, and new data capabilities.
Architecture becomes stronger when people with deep data platform knowledge support your choices. Our team assesses your technical state, explores how your workloads behave, and shapes a design that fits how you operate.
Across the project, you work with a Data & AI Solution Architect and a Data Architect. They help define boundaries, integrations, structures, and performance expectations.
You gain clarity on how your environment should look, how it should grow, and which decisions will support long-term reliability.
A Solution Architect supports the designs of the overall data and analytics landscape for you. They define how systems interact, how data moves, and how the platform supports both day-to-day operations and long-term ambitions.
Their work shapes integration patterns and performance expectations,.They help your team build solutions that stay reliable and scalable as your business evolves.
A Data Architect focuses on structure. They define modelling patterns, storage formats, historisation methods, data domains, and consistency rules.
They provide clarity on how information is organised and how can different teams use and maintain it. With them , you are able to strengthen coherence across data products and ensure your models remain sustainable and easy to extend.
Together, we identify:
– Where your architecture supports teams well
– Where gaps, inconsistencies, or bottlenecks appear
– How your current design influences cost and performance
– Parts of your ecosystem, ready for scale
– Needs to evolve to support future analytics and AI
The architectural plan includes:
– Coherent data and solution design
– Integration patterns that reduce friction
– Scalability and performance guidelines
– Security and access framework
– Actionable set of architectural
decisions
The result is a foundation your team understands, trusts, and can confidently build on.
Questions we often hear about Data Architecture.
Data architecture is the structural design of how data is collected, stored, organised, transformed, and used across a company. It defines modelling choices, storage patterns, integration methods, governance needs, and platform behaviour. A strong architecture guides your team, reduces rework, and keeps data consistent with tools and departments.
As you adopt more tools and generate more data, everything becomes harder to maintain. A strong architecture provides stability. It sets boundaries, creates predictable patterns, reduces fragmentation, and supports analytics and AI.
Indicators include increasing platform costs, slow dashboards, inconsistent KPIs, pipeline failures, duplicated models, or unclear integrations. Architecture becomes essential when your current setup no longer supports future goals or when multiple teams start depending on the same data assets.
No. Any company that wants plans to grow its digital and analytical capabilities benefits from a defined architecture. Smaller teams often see faster improvements because foundational decisions have a bigger impact early on.
No. These platforms offer powerful tools, but they do not decide how your data should be structured, how integrations should work, or how models should evolve.
Architecture sets the direction that makes these platforms effective and sustainable.
Solution architecture focuses on how systems connect and how data moves between them. Data architecture focuses on how data is organised, structured, and governed. Both roles work together to shape a platform that behaves reliably and consistently.
Clear architectural guidelines reduce unnecessary compute usage, avoid redundant pipelines, and limit expensive storage patterns. Workloads become easier to monitor, scale, and tune. This keeps your data platform efficient and prevents cost escalation.
We’ll help you shape the architecture behind it.