A single, reliable place for all your organisation's data
Core capabilities
Five functions — what the platform does with your data
Click any capability to see what it does and the tools we use at that layer.
Architecture options
Choose your data platform pattern
Select an architecture pattern that matches your workload requirements. Each pattern represents a proven approach to building a cloud data platform.
On-premise / hybrid
On-premise and hybrid architectures keep data within your controlled environment, addressing strict regulatory requirements and data sovereignty rules. This pattern provides maximum control over data residency and security policies.
Architecture pipeline
Cloud ecosystems
Three platforms — we work across all of them
We implement data platforms on Amazon Web Services, Microsoft Azure, and Google Cloud Platform. We work with the data-specific services on each provider — not just general cloud infrastructure.
Amazon Web Services
Amazon Web Services is our most deployed platform — well-suited for data-heavy workloads with mature managed services across ingestion, storage, and querying. The S3-based lakehouse pattern with Redshift and Glue is core to most AWS data platform builds.
Core services
- WarehouseAmazon Redshift
- Data lakeAmazon S3
- IngestionAWS Glue
- StreamingAmazon Kinesis
- OrchestrationAmazon MWAA (Airflow)
- GovernanceAWS Lake Formation
- Query layerAmazon Athena
Microsoft Azure
Microsoft Azure is the natural fit for organisations already invested in the Microsoft ecosystem. Synapse Analytics unifies warehousing and data lake operations, while seamless Power BI integration makes it the strongest platform for enterprise self-service analytics.
Core services
- WarehouseAzure Synapse Analytics
- Data lakeAzure Data Lake Storage
- IngestionAzure Data Factory
- StreamingAzure Event Hubs
- OrchestrationAzure Synapse Pipelines
- GovernanceMicrosoft Purview
- BI layerPower BI Premium
Google Cloud Platform
Google Cloud Platform leads on analytics query performance and ML integration. BigQuery's serverless architecture removes cluster management entirely, and Vertex AI makes GCP the strongest choice for organisations combining data platforms with machine learning workloads.
Core services
- WarehouseBigQuery
- Data lakeGoogle Cloud Storage
- IngestionCloud Dataflow
- StreamingPub/Sub
- OrchestrationCloud Composer (Airflow)
- GovernanceDataplex
- ML layerVertex AI
Use cases
Three examples — in plain terms
These represent common scenarios, not fixed templates. Every implementation is designed around the specific data sources, systems, and reporting needs of the organisation.
A manufacturing group with operations across four states has data in seven different systems — an on-premise ERP, two plant-level databases, a logistics platform, a finance system, and two SaaS tools.
Finance consolidates monthly reports by manually exporting from each system, reconciling in Excel, and emailing the result. The process takes four days and produces different totals depending on who runs it.
Fragmented, manual, unreliable
- ReportingFour-day manual reconciliation process every month. Numbers differ depending on who runs the export.
- AccuracyRevenue figures differ between finance, sales, and operations by up to 8% — every review meeting starts with a 20-minute debate.
- Trust"I don't trust this number" is said in every data-related meeting. Decisions are made on gut feel.
- New sourcesTwo recent acquisitions added three more systems. The manual process is close to breaking point.
Unified, automated, trusted
- ReportingMonthly report generated automatically. Delivered to the CFO on the first working day — zero manual work.
- AccuracySingle, consistent definition for revenue across all seven source systems. One number, everywhere.
- TrustData trust score moves from 40% to 87% within six months of platform launch.
- New sourcesNew source systems onboarded in days using the established ingestion framework.
Business outcomes
What changes after a platform is in place
These outcomes are based on typical production deployments. Actual results vary by organisation size, data volume, and starting state. We establish baseline measurements during the assessment phase so you have a before/after comparison.
Finance, operations, and sales see the same numbers because they are all reading from the same transformation layer. Metric definitions are written once, tested automatically, and used everywhere.
Analysts typically spend 60–80% of their time finding, cleaning, and reconciling data before they can analyse it. A functioning platform moves that burden to the pipeline layer — automated, documented, and reliable.
Reports that require manual extraction, consolidation, and reconciliation take days. When the platform handles those steps automatically, the same report is available on a defined schedule without any manual work.
Cloud warehouses add compute and storage on demand. A platform handling 100GB of data today can handle 10TB in two years without architectural change — only cost increases proportionally with volume.
With lineage tracking, any number in any report can be traced back through every transformation to the source system that produced it. This is what internal audit, compliance teams, and regulators ask for.
AI and ML models require clean, consistent, versioned training data. A governed cloud data platform provides this — which is why most AI projects stall without one. The platform is not AI itself; it is the prerequisite for AI that works.
How to engage
Three engagement types
Every engagement starts with a free platform assessment — we review your current data environment, identify the highest-impact gaps, and recommend a scope before any commitment is made.
- Data source inventory and architecture design
- Cloud warehouse provisioning and security setup
- 3–5 source systems connected with ingestion pipelines
- Transformation layer and first reporting layer
- Team training and handover documentation
- Current platform audit — what works and what does not
- Migration plan with zero-downtime strategy
- Modern stack implementation (dbt, Airflow, cloud warehouse)
- Data quality and governance layer
- Cost optimisation built in from design
- Team capability uplift alongside delivery
- Pipeline monitoring and incident response
- Monthly cost review and optimisation
- New data source onboarding as needed
- Analyst support — query help, model changes, new metrics
Common questions
What we hear before most platform engagements
Understand your current data infrastructure gaps — before committing to any platform
We review your existing data sources, current reporting setup, and team capability — and return a written assessment covering your gaps, a recommended platform approach, and an indicative scope. This is not a sales presentation. It is a structured technical opinion on your situation.
