Ready to lead with data you actually trust? Have a frank conversation with our team about where your data stands today, and what a governed, AI-ready platform could unlock for your business strategy and operational efficiency.
Finance says one thing, operations says another. Leadership debates the data instead of acting on it and every quarter the cost of a wrong call grows.
While you wait weeks for a reliable report, others act on insight in near real time. Slow data compounds into a disadvantage.
Without clean, governed and documented data, automation can’t be trusted at scale
Without reliable operational data, improvement remains anecdotal instead of structural.
Adoption stays low, ownership is unclear, and business value never fully materialises.
Every data initiative starts with a business problem, not a technical one. We define value, effort and priority before anything is built, aligned with leadership.
Key deliverables: business request, value case, effort estimate, MT/Steerco prioritisation, documentation.
We sit with business teams to sharpen the question, define KPIs, and document end-to-end process flows. We help organizations assign data owners and report owners so accountability is clear from day one. Then we go into the source systems: map data availability, profile quality, and identify the minimal dataset required to answer the KPI.
Key deliverables: business glossary, KPI feasibility, source system discovery, data profiling, data ownership assigned.
We design the data model in a fit-for-purpose data architecture based on the real business process and relationships uncovered earlier. At the same time, we put data quality governance in place: capture rules, set thresholds, and create a foundation for an organisation that actually knows the quality of its data.
Key deliverables: Data model, data quality rules, DQ thresholds.
We build the bronze and silver layers with full ELT, unit tests and lineage metadata. Before building the final report, we surface a “raw truth” exploration report and review it with the business. This is where business rules are naturally discovered and confirmed,so the final output reflects how your organisation actually works, not how anyone assumed it worked.
Key deliverables: Enterprise data model, business rule discovery, ETL development, unit tests, lineage governance.
With confirmed business rules, we build the business vault layer, information marts and the final BI report. UAT with real users. Production release with security, access governance and data observability dashboards from day one. We train report owners, data owners and stewards, so your team can own what we built together.
Key deliverables: information mart, UAT & production release, BI report, data observability, training & handover.
A data product isn’t “done” at go-live. We monitor usage in the first month, fix what needs fixing, and keep a regular cadence with data owners, stewards and report owners. Continuous improvement loops back into Phase 0, so the platform evolves with your business, not behind it.
Key deliverables: usage monitoring, continuous improvement, DQ monitoring, regular check-ins, process quality.
We bring a working path to the first conversation, not a slide deck. You see results from sprint one.
We start with strategic priorities and business questions. Tools follow outcomes, not the other way around.
Our “minimal necessary” approach matches infrastructure to your maturity. You don’t pay for complexity you can’t use or maintain.
Auditability, data quality rules, lineage and ownership are built in from day one, so reporting becomes defensible.
We train your people and hand over ownership. The goal is autonomy, not an ever-growing dependency on external consultants.
The metadata, glossary and lineage layer we build now is the foundation you need for AI agents later, without starting from scratch.
Microsoft Fabric, Databricks, Azure OpenAI, Data Vault 2.0, Star Schema, dbt, Python, CI/CD, Power BI.
Advise → Build → Mature.
Raw data scattered across disconnected source systems with no unified view. Leadership wants to optimise business oversight.
Applied the full vertical slice methodology, from business question through Data Vault 2.0 modelling to governed analytics, with business teams involved at every stage.
A governed, high-integrity data platform that turns raw data into a scalable engine for growth, and a business team that owns it.
Uncertainty around data strategy and sovereignty due to changing regulations. Leadership was stuck between conflicting options and geopolitical risk.
Avoided all-or-nothing thinking. Built scenarios with explicit risk and agility tradeoffs so decisions could move forward.
Leadership makes confident, informed decisions. Clear direction with realistic options, not rigid positions or vendor lockin.
Uncontrolled tool proliferation created risk and duplication, while the deeper goal, accelerating innovation, was being blocked.
Reframed a technical governance problem into a portfolio and ownership conversation. Advisory anchored to the technical proposition to align direction.
Faster flow from ideas to value. Clear ownership, better governance, and real alignment with innovation goals.
It’s our structured, eight-phase process for turning a business question into a governed, production-ready data product. Unlike traditional big-bang data projects, each vertical slice delivers something usable and validated at every stage, from value case through to live report, with your team involved throughout. This keeps risk low, alignment high, and value visible from the first sprint.
Data Vault 2.0 is a modelling methodology designed for enterprise data platforms that need to be auditable, flexible and scalable over time. It structures data into hubs (business entities), links (relationships) and satellites (contextual attributes). We use it because it makes the platform resilient to change, when your source systems evolve, the vault doesn’t break. It also provides the auditability and lineage that governance and AI use cases require.
It depends on scope and data complexity, but our methodology is specifically designed for incremental delivery. By Phase 6, the raw truth review, you already have a working data model and a report to explore with your business team. A first production-ready report, from kick-off to UAT, typically takes between six and ten weeks for a well-scoped slice. We define SLAs during Phase 1 so expectations are explicit from the start.
No. Our approach is source-system agnostic. We connect to what you already have ERP, CRM, legacy databases, SaaS applications, and build a unified layer on top. You don’t need a big-bang migration. We work with your current infrastructure and evolve it incrementally.
Deliberately not. Phase 7 and Phase 8 are built around knowledge transfer: we train your report owners, data owners and data stewards so they can maintain and extend the platform. We prefer a “minimal necessary” approach, building enough for your team to own and grow it, rather than engineering a dependency on external consultants. Phase 8 aftercare ensures adoption and provides a safety net, but the goal is your autonomy.
Clean, governed, well-documented data is the prerequisite for reliable AI. As part of our methodology, we build a business glossary, metadata layer and lineage documentation that makes your data interpretable, not just by your team, but by AI agents. This foundation allows future agentic workflows to navigate and analyse your data landscape without manual instruction. We build it in from day one, so you’re not starting from scratch when you’re ready.
We have deep expertise in Microsoft Fabric, Databricks and Azure. For transformation, we use Python, pySpark, SQL and dbt. All platforms are built with full CI/CD integration for production stability. We select the right tool for your current maturity and existing investments, we’re technology-pragmatic, not vendor-loyal.
