A data platform your organisation can rely on.

A modern data platform is a governed environment that ingests data from operational systems, transforms it into consistent, well modelled datasets, and serves those datasets to reporting, analytics and AI workloads. In practice it combines cloud storage, orchestration, transformation code held in version control, automated testing, monitoring, security controls and documented lineage, so the data it publishes can be trusted and traced.

Choose the platform that fits your existing licensing, team skills and workloads: Microsoft Fabric where Power BI is already established and you want one governance and licensing model, AWS where your estate already runs there, and Snowflake for a platform-independent warehouse with separate storage and compute. Assembling individual services such as Data Factory, Databricks or Azure SQL gives more control and can suit high-volume estates. We assess the options against your position rather than defaulting to one.

Start with what the business depends on rather than with the technology. Identify the reports and processes that must not break, document the logic buried in existing jobs, then migrate in slices with reconciliation against current outputs at each step. Rewriting the logic as tested, version controlled code is usually better value than lifting and shifting old jobs, because it removes the maintenance cost that made the legacy platform difficult in the first place.

A first usable increment, meaning real data flowing through the platform into a business report, is typically achievable within the first few months. Full migration of an established estate takes longer and depends on the number of sources, the complexity of the logic and the pace at which the business can absorb change. We sequence work so value arrives before the programme completes.

Choosing the technology before understanding the decisions the platform must support. Migrating legacy logic without questioning it. Leaving testing, monitoring and documentation until the end. Building without the business owners of the data involved. Underestimating the cost of running the platform once it is live. Each of these is avoidable with an honest design phase.

Cost is a design decision. We size compute to workload patterns, separate development and production environments, schedule refreshes to business need rather than habit, and set up cost reporting so consumption is visible to the people who can act on it. Cost reviews form part of the platform handover rather than a later surprise.

No, and waiting for clean data before building rarely works. The platform is where quality becomes measurable: testing, validation and clear ownership expose issues that were previously hidden in spreadsheets. We design quality checks into the pipelines and agree with the business which issues are fixed at source and which are handled in transformation.

We standardise the pattern rather than treating every source as a one off. Ingestion is templated and incremental where the source supports it, and separated from transformation so a change in a source system does not ripple through business logic. Managed ELT tooling such as Fivetran or Matillion handles well supported sources quickly, transformation is modelled and tested as code with dbt, and bespoke engineering is reserved for systems that only offer files or reports. The result is data from many systems arriving in one governed platform, with monitoring and alerting that keeps those pipelines reliable once they are live.

Yes, and that is how most of this work runs. We contribute engineers into your delivery process, follow your standards where they exist and propose them where they do not, and review code together. Building capability inside your team is part of the engagement rather than an optional extra.

A reader should be able to take any figure in a report and trace it back to the source system, the transformation applied and the owner of the definition. In practice that means transformation logic in version control, tests that describe expected behaviour, a documented model, and lineage that is generated from the platform rather than maintained by hand.

Getting a model to work is only part of the challenge. Machine Learning Operations provide the processes and infrastructure needed to deploy data science models into production, monitor how they perform, manage changes and retrain them when needed. We build these requirements into the platform so models can move from experimentation into production and continue to perform reliably over time.

AI workloads are demanding consumers of data. They need history, consistent definitions, reliable refresh, access control and traceability. Most AI initiatives that stall do so because those conditions are missing rather than because the model was wrong. A well engineered platform is the practical foundation for AI, and we design it with those workloads in mind.

Yes. We are based in Dundee and work with ambitious organisations across Scotland and the UK. Engineering work is largely remote with onsite time for discovery, design sessions and key delivery milestones, and we agree that balance with you at the outset.