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AI ServicesAI ReadinessData Quality and Availability
AI Readiness

Is your data ready for AI?

AI is only as good as the data behind it. We evaluate your data sources, quality, completeness, and accessibility to determine which AI applications are viable for your business today and what needs to change to unlock the rest.

Data Source Inventory
Quality Scoring
Governance Review
AI Viability Map
6+
Data quality dimensions assessed
2 weeks
Typical assessment delivery time
80%
Of businesses have usable AI data today
UK-Wide
Data quality assessments delivered
What We Assess

Every dimension of data readiness.

Our data quality assessment covers source inventory, quality profiling, governance, accessibility, and AI viability mapping to give you a complete picture of your data readiness.

Data Source Inventory

We catalogue every data source in your organisation, including databases, spreadsheets, cloud platforms, and third-party feeds, to establish a complete picture of what data you hold and where it lives.

Data CatalogueSource MappingData Inventory

Data Quality Assessment

We assess your data against five quality dimensions: completeness, accuracy, consistency, timeliness, and uniqueness. Each dimension is scored and mapped to specific AI use cases to show what is viable today.

Data QualityQuality ScoringCompleteness

Data Accessibility Review

We evaluate how accessible your data is for AI consumption, including API availability, data format standardisation, access controls, and the effort required to connect data to AI tools.

Data AccessAPI ReadinessFormat Standards

Data Governance Evaluation

We review your data governance framework including ownership, classification, retention policies, and consent records to identify governance gaps that would prevent safe AI adoption.

Data GovernanceData OwnershipConsent Records

Data Pipeline Readiness

We assess whether your current data pipelines can support AI workloads, including data freshness requirements, transformation capabilities, and the reliability of data flows between systems.

Data PipelinesData FreshnessETL Readiness

AI Viability Mapping

We map your data quality findings to specific AI use cases, producing a clear view of which AI applications are viable with your current data and which require data improvement work first.

AI ViabilityUse Case MappingPrioritisation
How We Work

From data discovery to AI viability.

01

Data Source Discovery

We work with your team to identify and document every data source in your organisation, including systems you may not have considered as data assets for AI purposes.

02

Quality Profiling

We run structured quality profiling across your key data sources, assessing completeness, accuracy, consistency, and timeliness against the requirements of your target AI use cases.

03

Governance and Compliance Check

We review data governance documentation, consent records, and data classification to identify any governance gaps that would prevent data from being used safely in AI applications.

04

Pipeline and Access Assessment

We assess the technical accessibility of your data, including API availability, data format standards, and the reliability of data pipelines that would feed AI systems.

05

Viability Scoring

Each data source is scored against the requirements of your target AI use cases, producing a clear viability matrix that shows what is possible today and what needs improvement.

06

Remediation Roadmap

We produce a prioritised data remediation roadmap covering the specific improvements needed to unlock your highest-value AI opportunities, with effort estimates and sequencing guidance.

Real Results

Data quality assessments for UK businesses.

Regional Logistics Business

A logistics company wanted to use AI for demand forecasting but had no idea whether their operational data was good enough to support it.

Data quality assessment revealed three usable data sources and two with significant gaps. Demand forecasting scoped for viable data. Data improvement plan for remaining sources delivered within six weeks.

Private Healthcare Group

A healthcare provider wanted to use AI for patient scheduling optimisation but had patient data spread across four separate systems with inconsistent formats.

Data source inventory completed. Consolidation approach identified. AI scheduling scoped using the two highest-quality sources. Data integration roadmap produced for remaining systems.

Financial Services Firm

A financial services business wanted to adopt AI for client reporting but was unsure whether their data governance framework was sufficient to allow client data to be used in AI tools.

Governance review identified three consent gaps. Remediation completed in four weeks. AI reporting tool deployed with full governance confidence. Compliance team signed off before deployment.

Get Started

Find out what AI your data can support today.

Book a data quality assessment. We will evaluate your data sources, score them against your target AI use cases, and give you a clear roadmap for unlocking the AI opportunities your data can support.