A cloud data platform that fits, not just fits the vendor’s reference architecture.
Cloud migration projects tend to go wrong for a predictable reason: the technology decision gets made before anyone has properly mapped what the business actually needs the data to do. You end up with infrastructure that is technically sound and genuinely mismatched to how the organisation works, expensive to unwind, and slower to deliver value than the business case promised.
AWSMicrosoft AzureData Architecture
Strategy before platform
The real question most organisations face now isn’t whether to be in the cloud, most already are. It’s whether what they’ve built is a genuine foundation or a collection of warehouses, lakes, and point solutions that have accumulated over several years of separate decisions. That fragmentation is what makes AI initiatives stall: a model or an assistant can only be as reliable as the data it’s actually able to reach, and a platform nobody has consolidated is a platform AI can’t be trusted against.
This shows up most often in organisations partway through a cloud migration, where the harder problem isn’t moving the data, it’s designing a strategy the organisation’s own team can actually run once the migration is done. In regulated sectors like banking and healthcare, where data sensitivity and system complexity are both high, that gap between a technically complete migration and a genuinely usable one is where the real risk sits. The right answer depends on what already exists and what a team can realistically maintain, not on a reference architecture that looked good in a vendor pitch.
Then the platform itself
Once the strategy is right, we architect and deliver a governed data foundation built to serve reporting, analytics, and AI from the same trusted source, rather than a warehouse for dashboards and a separate, ungoverned copy for everything else. That includes data pipelines and transformation work, operational data source development, and the observability and quality checks that let a team trust what the platform tells them, not just what it was built to tell them.
We work across both Amazon Web Services and Microsoft Azure, and we will tell you honestly if the right move is a smaller step than the one you came in asking about.
Good platform architecture is what makes governance, analytics, and AI initiatives further down the line actually achievable instead of aspirational. That’s deliberate: this work is designed to connect directly to what we do in AI Governance and Business Analytics, not sit next to it as an unrelated project.
What this covers:
- Data pipelines and transformation work to move and reshape information reliably between systems
- Operational data source development
- A governed data foundation built to serve both analytics and AI workloads from one trusted source, not separate copies
- Data observability and quality checks, so the platform’s own reliability is monitored, not assumed
- OLAP and tabular data models built for real reporting and analytics needs, not a generic best practice layout
We are technology agnostic in the sense that matters most: we are not paid more for recommending a bigger build, so our advice is not shaped by which platform pays the best margin. Our interest is in a data platform your team can actually run and trust once we are gone.
Contact us to talk through where you currently sit on the cloud journey, and we will help you work out the right next step before we talk about which platform to build it on.
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