Data Platforms & Engineering
When data is spread across ageing systems, legacy warehouses and manual pipelines, reporting becomes slow, inconsistent and hard to trust. It also makes new analytics and AI initiatives harder to deliver.
We help CIOs, CTOs and data leaders modernise that environment, whether that means completing a cloud migration, replacing legacy infrastructure or bringing fragmented systems together.
You finish with a governed platform on Microsoft Fabric or AWS, automated and monitored pipelines, clear data lineage, and a team able to run and extend it confidently.
Common challenges
These patterns appear across sectors. The technology is usually the visible symptom, and the harder issues are ownership, definitions and the absence of engineering discipline around how data moves.
Systems were added to solve immediate problems and the integrations between them were never designed. Reporting now depends on a chain of extracts, spreadsheets and manual steps that only a few people understand.
On premise warehouses, ageing ETL tooling and unsupported versions still run business critical reporting. Change is slow and risky, so teams work around the platform rather than through it.
Some workloads moved to the cloud and some did not. The organisation now runs two operating models, pays for both, and has no clear position on what moves next or why.
The same measure arrives with different values depending on the source. Without shared definitions, tested pipelines and clear lineage, leadership spends meetings reconciling figures instead of making decisions.
Failures are discovered by the business rather than by monitoring. There is limited testing, little alerting and no agreed recovery process, so trust in the platform erodes with every incident.
Analytics and AI ambitions assume governed, well modelled, reliably refreshed data. The current estate cannot supply it yet, and the gap between ambition and foundation is rarely costed honestly.
Our approach
Architecture decisions are made against your workloads, licensing and skills rather than a preferred stack, and everything we build is written to be maintained by your team.
Data and AI engineering is the work required to turn data, analytics and AI into reliable production systems. It covers the pipelines and platforms that make data available and trusted, as well as the infrastructure needed to deploy, monitor and manage machine learning models and AI applications. This includes data engineering, MLOps and AI engineering, with the testing, security, governance and monitoring needed to operate them reliably at scale.
Stage 01
We map the current estate: source systems, integrations, pipelines, storage, models, reporting and the manual steps holding it together. We document how data actually moves, where trust breaks down, what the platform costs to run today and which business processes depend on the fragile parts.
Stage 02
We design the target architecture and the route to it. That covers platform choice, typically Microsoft Fabric or Azure, ingestion and integration patterns, data modelling approach, environments, security, monitoring and cost control. Migration is sequenced by business risk and value rather than by technical convenience.
Stage 03
We build in slices that deliver usable data early. Pipelines are engineered with version control, automated testing, documented lineage and alerting. Legacy workloads are migrated with reconciliation against the existing outputs, so the business can see that the new platform produces the same answers before anything is switched off.
Stage 04
We move the platform into steady state: run books, monitoring, release process, cost reporting and ownership. We work alongside your engineers throughout so they can extend and support the platform themselves, and we agree what decommissioning of legacy components looks like.
Business outcomes
We describe outcomes in the terms our clients use about the work.
Analytics and AI draw from a governed, modelled platform rather than competing extracts, so the same question returns the same answer.
Acquisition, transformation and refresh run automatically, with the time previously spent moving files released back to your team.
Pipelines that are tested, alerted and documented, so failures are found and fixed before they reach a leadership report.
Architecture that supports new sources, new products and AI workloads without another rebuild in two years.
Access, environments, lineage and audit designed as part of the platform rather than added after the first review.
Your team works in the codebase with ours, so they own the standards, the tooling and the support model when we step back.
Relevant client work
The case studies set out what the organisation needed, what we built and what it produced.
Data Understood push to learn the nuances of the company, its overall strategy, its people and thus deliver solutions that benefit the company to its core and lay the foundation for a strong future in data.
Data Understood's deep understanding of our problems and ability to communicate with non-technical teams in a very positive manner is impressive.
They provided costed structure and solutions to create an ongoing credible data management capability.
Where to go next
Platform work usually sits between a strategy that set the direction and the analytics that prove the value. The sector context shapes integration, security and migration sequencing.
Buying questions
Written for the people who have to make the technical and financial case internally, covering how platform modernisation is run in practice.
Whether you're developing a data strategy, modernising your data platform, preparing for AI or tackling a specific business challenge, start with a 30-minute conversation with Inez.
