Services/Data Strategy & Readiness

Data Strategy & Readiness

AI is only as good as the data it learns from. Most enterprises are building world-class AI on third-world data.

Gartner research (2024) finds that only 25% of enterprise data is in a state suitable for AI/ML use without significant preparation. Yet most enterprises invest in AI models before investing in the data foundations those models depend on.

The consequence: AI that produces unreliable outputs, fails regulatory scrutiny, and fails to deliver the ROI your board approved. Data strategy is not a technical overhead — it is the unsung enabler of every AI initiative on your portfolio.

25%

of enterprise data is AI-ready without significant preparation

Gartner, 2024

$3.1T

annual cost of poor data quality to US businesses alone

MIT/IBM

$12.9M

average annual cost of poor data quality per organisation

Gartner, 2024

4×

higher AI ROI when data foundations are built first

McKinsey, 2024

The six dimensions of AI data readiness

A weakness in any single dimension can make an AI use case undeliverable — or worse, deliverable but unreliable. We assess all six before any use case begins development

Availability

Does the data exist and is it accessible by the systems that need it?

If absent: Use case cannot be built

Quality

Is it accurate, complete, timely, and consistent enough for the intended use?

If absent: Model produces unreliable outputs

Governance

Is there clear ownership, lineage documentation, and access control?

If absent: Regulatory non-compliance, audit failure

Integration

Can data from disparate systems be unified without loss of integrity?

If absent: Model trained on incomplete or inconsistent data

Scale

Is there sufficient volume and variety to train reliable, generalisable models?

If absent: Model overfits — fails on real-world data

Compliance

Does data handling comply with GDPR, AI Act, and sector-specific regulation?

If absent: Regulatory penalty and reputational exposure

How we design your data strategy

Five stages from readiness assessment to a compliance-aligned data architecture. Built on DAMA-DMBOK and Data Mesh principles — the industry standards for enterprise-grade data management

Stage 01

Data Readiness Assessment

We assess your data infrastructure across six dimensions: availability, quality, governance, integration, scale, and compliance. Gartner research finds that only 25% of enterprise data is in a state suitable for AI use without significant preparation — our assessment tells you exactly where you stand across each dimension and what each gap costs you

6-dimension data readiness scorecardGap analysis by business domainAI readiness heatmap

Stage 02

Data Governance Framework Design

We design a data governance framework aligned to DAMA-DMBOK — the industry standard reference for enterprise data management — and to your specific regulatory obligations under GDPR and sector-specific requirements. Clear data ownership, lineage tracking, access control, and quality standards are the prerequisites for AI that regulators can audit and boards can trust

Data governance frameworkData ownership matrixQuality standards policy

Stage 03

Data Architecture Design

We design the data architecture your AI systems require — aligned to the Data Mesh paradigm (domain-oriented, federated ownership with centralised governance) where appropriate for large enterprises. This includes the data pipelines, feature stores, data contracts, and metadata management infrastructure that allows AI models to operate on reliable, auditable data

Target state data architectureData mesh domain designFeature store strategy

Stage 04

Data Quality Remediation Programme

We design a prioritised remediation programme addressing the specific data quality issues that will prevent your priority AI use cases from delivering value. IBM research establishes that 80% of data science project time is currently spent on data preparation. Our remediation programme is scoped to the minimum viable data quality required for your highest-priority use cases — not gold-plating everything simultaneously

Prioritised remediation planUse-case-specific data requirementsQuality measurement framework

Stage 05

Compliance & Regulatory Alignment

We ensure your data strategy is aligned to every applicable regulatory obligation — GDPR data minimisation and purpose limitation, sector-specific data retention and handling rules, EU AI Act data governance requirements for high-risk AI systems, and cross-border data transfer restrictions. Data compliance is not a constraint we work around — it is a design parameter

Regulatory compliance mappingData handling policyCross-border transfer assessment

Build AI on foundations that hold

Enterprises that invest in data foundations before scaling AI realise 4× higher returns than those that attempt to clean data as they go. We design the data strategy your AI programme requires to deliver at scale.