How we approach it
Reporting forgives a lot. A dashboard with a stale join and an undocumented filter still gets published, and someone eyeballs the number. AI does not forgive any of it: a model trained on data with unstated assumptions produces confident, plausible, wrong output at scale.
Our data practice exists to get organisations from the first standard to the second — modernising platforms, rebuilding pipelines with quality gates, establishing lineage and ownership, and delivering the analytics that pay for the work along the way.
What sits under Data & Analytics
Data Strategy & Consulting
Target-state architecture, operating model and a sequenced modernisation plan — including the systems and reports to retire.
ExploreData Engineering & Pipelines
Ingestion, transformation and orchestration built with tests, lineage and alerting — so a broken feed is an alert, not a discovery three weeks later.
ExploreData Platform Modernisation
Lakehouse and warehouse builds designed for parallel running, so the business keeps its numbers while the foundation is replaced underneath.
ExploreData Migration & Integration
Moving data between platforms with the testing that lets you sign off — row counts, control totals, business-rule validation and a documented rollback.
ExploreData Governance & Quality
Catalogues that are current, quality rules that run automatically, and ownership that maps to how your organisation is genuinely structured.
ExploreDatabase Consulting
Performance engineering, high availability, upgrades and licensing review across the relational and document databases your operations depend on.
ExploreBusiness Intelligence Consulting
BI programmes that rationalise what exists, agree what the measures mean, and give analysts self-service without abandoning governance.
ExploreData Visualisation
Visualisation designed around decisions and built to accessibility standards — including the public-facing reporting that has to work for everyone.
ExploreAdvanced Analytics & Machine Learning
Forecasting, segmentation, risk scoring and optimisation — built, validated and deployed where the decision is actually made.
ExploreThe sequence, in the order it actually happens
Understand the estate
Source systems, integration patterns, existing warehouses and marts, and the shadow spreadsheets that are actually load-bearing.
Fix the foundations
Ingestion, modelling, quality gates, lineage and access control — done once, properly, rather than per project.
Deliver visible value early
A real analytics deliverable in the first quarter so the programme has something to show while the deeper work continues.
Open the platform up
Self-service access with governance attached, so analysts move without waiting on a central queue.
Run it
Managed operation of pipelines and platforms, with SLAs on freshness and quality rather than just uptime.
Data & Analytics in practice
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Consolidating fragmented departmental reporting
Nine departments maintained overlapping data marts with conflicting definitions of the same measures. We produced a consolidation plan with a shared semantic layer and a phased decommissioning schedule.
Outcome — One agreed definition per measure, and a plan to retire the duplicates.
Rebuilding student data pipelines for AI workloads
Nightly extracts from the student information system had no tests and no lineage. We rebuilt them with schema contracts, quality gates and column-level lineage so downstream AI work had a trustworthy base.
Outcome — Silent schema breaks surfaced as alerts before they reached reporting.
From an unsupported on-premises warehouse to a governed lakehouse
An ageing warehouse could not support new analytics or AI. We built a lakehouse with in-region storage, migrated the reporting layer, and ran both in parallel with automated reconciliation until sign-off.
Outcome — Cutover completed with reconciled numbers rather than a leap of faith.
Migrating a records system with statutory retention
A case management database had statutory retention and audit obligations. We migrated with full lineage, preserved audit history and produced an evidence pack for the records authority.
Outcome — Migration completed with retention and audit obligations demonstrably intact.
Classifying personal health information across the estate
The organisation could not state with confidence where PHI existed. Automated scanning and classification across databases and file stores produced an inventory, then drove access and retention rules.
Outcome — A defensible inventory of where regulated data actually lives.
Tuning a PeopleSoft database through peak registration
Registration-period load caused timeouts every term. We profiled the workload, corrected indexing and statistics, and reworked the worst batch processes.
Outcome — Peak registration handled without emergency intervention.
Start with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.