CAPABILITY
Data Foundation
The foundation for trusted decisions.
Clean data, clear ownership, systems you can rely on.
Whether you're preparing for AI, tackling sustainability reporting,
or just trying to get consistent numbers across the business –
it starts with data you can trust.
In 30 Seconds
Data governance and management are established disciplines – they existed long before AI hype. Good data foundations enable better decisions, cleaner reporting, smoother compliance, and yes – effective AI.
We help with the foundational work:
- GovernanceWho owns the data? Who can access it? What are the rules?
- QualityIs it accurate, complete, timely? Can you trust what it tells you?
- ArchitectureHow does data flow? What does it mean? Where did it come from?
The principle: Garbage in, garbage out – whether that's a board report, a compliance submission, or an AI system. Clean data foundations mean you can trust what your systems tell you.
The Challenge
The Data Mess
Data is siloed across systems. No one knows what exists where. Different departments have different definitions of the same thing.
- • “Customer” means one thing in finance, another in ops
- • Dashboards disagree with each other
- • No one trusts the numbers in reports
Governance Misunderstood
People think governance means writing rules and restricting access. Endless red tape. Slowing down projects. Saying “no”.
- • Policies exist but nothing changes
- • Compliance seen as a burden, not enabler
- • Governance treated as admin, not strategy
AI Wants to Work
You've bought AI tools. They could help. But they can't find the right data, or they find conflicting data, or the data is just wrong.
- • AI outputs are inconsistent or wrong
- • Can't trust AI answers for decisions
- • Pilots don't scale because data doesn't scale
The Real Competitive Advantage
Everyone has access to the same AI models now – Claude, GPT, Gemini. The technology is commoditised.
The differentiator isn't the model. It's your data.
If you have clean, well-organised proprietary data, that's your competitive moat – competitors can't replicate it. But if your data is a mess, you're just using AI the same way everyone else is.
This is why organisations often come to us for AI help and discover they need data help first. The two are inseparable.
The reframe: Governance isn't about restriction – it's about making “yes” possible safely, consistently, and with confidence. It's about making data accessible and usable, not buried in systems only one person understands.
The Data Landscape
We organise data capability into six categories – the building blocks of a trusted data foundation.
Trust & Control
Data Governance
Policies, standards, accountability, compliance. The rules that make data trustworthy and accessible.
Who owns this dataset? Who can access it? Are we compliant with privacy and retention requirements?
Data Quality
Measuring, monitoring, and improving data fitness for its intended use.
Accuracy, completeness, consistency, timeliness. If you can't measure it, you can't improve it.
Structure & Consistency
Data Architecture & Metadata
How data is structured, flows, and what it means. The "semantic glue" that enables AI to understand your data.
Data lineage, cataloguing, technical/business/operational metadata. Where did it come from? What does it mean?
Master & Reference Data
Core business entities (customers, products, suppliers) that need consistent definitions across the organisation.
The "golden record". When data conflicts between systems, which source wins?
Execution & Adoption
Data Engineering
Building and maintaining data infrastructure – pipelines, storage, integration. The plumbing that moves data.
ETL/ELT, data lakes and warehouses, real-time vs batch processing. How do we move data reliably?
Data Culture & Literacy
The human dimension – skills, behaviours, leadership that enable data-driven decision making.
Can people interpret data? Do leaders model data-driven behaviour? Is there psychological safety to question data?
Reference framework: DAMA DMBOK (Data Management Body of Knowledge) – the global standard for data management with 11 knowledge areas. DMBOK 3.0 (2025) is being updated for the AI era.
How We Help
Building data foundations that enable AI and trusted decision-making
Data Readiness Assessment
Where are you now? What's blocking progress?
- •Current State: What data exists, where, who owns it
- •Quality Baseline: How good is your data really?
- •Gap Analysis: What's missing for your AI/reporting goals?
Understand your starting point before investing in solutions.
Governance Framework Design
Governance that enables, not restricts
- •Ownership Model: Clear accountability without bureaucracy
- •Policy Framework: Practical policies that people actually follow
- •Access & Security: Right data to right people, safely
Balancing compliance with delivery – rules AND results.
AI Data Preparation
Get your data ready for AI systems
- •RAG Readiness: Structure data for retrieval-augmented generation
- •Metadata for AI: Context that helps AI understand what data means
- •Quality for AI: Identify and fix issues that break AI outputs
Your proprietary data is your AI moat – make it usable.
Sustainability Data Systems
Data flows for reporting and compliance
- •CSRD/CDP Readiness: Data architecture for mandatory disclosure
- •MRV Systems: Measurement, reporting, verification infrastructure
- •Traceability: Supply chain data for EUDR and other requirements
Connecting data governance to sustainability outcomes.
Who We Help
Organisations Starting AI Journeys
You want AI but your data isn't ready. We help you build the foundation first.
How we help:
- • Data readiness assessment
- • Governance quick wins
- • Metadata for AI
- • Quality baseline
Sustainability Teams
Drowning in CSRD, CDP, GRI data requirements. Need systems, not spreadsheets.
How we help:
- • Reporting data architecture
- • MRV system design
- • Traceability solutions
- • Audit-ready data
Growing Organisations
Data that worked for 10 people doesn't work for 100. Need structure before it breaks.
How we help:
- • Governance frameworks
- • Master data management
- • Quality monitoring
- • Team capability
Data & AI Leaders
You know what's needed but lack bandwidth. Need trusted support to move faster.
How we help:
- • Implementation support
- • Change management
- • Team upskilling
- • Strategic advisory
The Bridge to AI
Data Foundation and AI Capability aren't separate – they're layers of the same stack.
RAG systems (how AI looks up your data) only work if that data is clean, organised, and meaningful. Context engineering (giving AI the right information) requires metadata that explains what data means. AI governance (trusting AI outputs) builds on data governance foundations.
We work across both layers because that's how real implementations work.
Ready to Build Your Data Foundation?
Whether you're preparing for AI, tackling sustainability reporting, or just trying to get your data house in order – let's have a conversation.
No commitment, no pitch. Just a conversation about what might help.
Start with the basics:
- • What's driving the need? (AI, reporting, growth, compliance)
- • What's the current state of your data?
- • What would “good” look like for you?