- Data silos , discrete systems that operate independently, preventing unified data consumption by AI models
- Schema debt and legacy architecture that requires extensive manual transformation before data can be used
- Unauthorised AI and analytics tools operating across departments without oversight , what DOT terms 'Shadow AI'
- Inconsistent data ownership and governance, resulting in quality degradation over time
- A Data Liquidity Score below the 85% threshold at which AI models deliver consistent, reliable outputs
AI-Ready
Near-Ready
Requires Remediation
Requires Remediation
- Generative AI tools used to process confidential documents, customer data, or intellectual property on external servers
- Unapproved AI-powered analytics plugins integrated into existing productivity platforms without security review
- Departmental AI solutions that ingest customer or operational data without GDPR-compliant processing agreements
DOT’s Shadow AI Detection engagement delivers a complete inventory of all AI tools in operation within your environment within two weeks, accompanied by a risk-classified register and a governance remediation plan.
- Data Liquidity
The degree to which enterprise data can flow freely and be consumed by AI systems without manual transformation or intervention.
- Data Silo
An isolated data repository that cannot be readily accessed by or integrated with other systems , a primary inhibitor of enterprise AI performance.
- Shadow AI
AI tools and applications deployed and operated within an organisation without the knowledge, approval, or oversight of IT or governance functions.
- Data Governance
The set of policies, processes, roles, and standards that govern the collection, storage, usage, and quality management of organisational data assets.
- Clean Data Pipeline
An automated data processing pathway that ingests raw data, applies validation and transformation rules, and delivers structured, AI-consumable outputs.
- Schema Debt
The accumulation of structural inconsistencies, deprecated fields, and undocumented changes in a data architecture that impede AI model consumption.
Intelligent Data Foundation , FAQ
The Data Liquidity Score is derived from a structured assessment across six dimensions: system connectivity, data quality, accessibility for AI model consumption, governance maturity, security controls, and lineage traceability. The initial score is produced during the Data Liquidity Audit engagement (4–6 weeks). For clients on ongoing retainers, the score is recalculated quarterly or following significant architectural changes.
In most cases, yes. The presence of a data warehouse does not in itself indicate AI readiness. Many enterprise data warehouses are architected for reporting and business intelligence workloads, not for the real-time, high-velocity consumption patterns demanded by AI models. Our audit specifically evaluates fitness-for-AI-purpose, which is a distinct assessment criterion.
The risks associated with Shadow AI span regulatory compliance (GDPR, EU AI Act), intellectual property exposure, data security, and model accuracy. DOT’s Shadow AI Detection combines network traffic analysis, endpoint activity review, IT system audit, and structured interviews to achieve comprehensive coverage. Our engagements consistently identify tools that are unknown to both IT leadership and the CISO.
Remediation timelines are proportional to architectural complexity. Organisations with a score in the 40–60% range typically achieve AI-ready status within eight to twelve weeks of engaging DOT’s remediation programme. The majority of our clients reach a score above 80% within three months, at which point AI model deployment can proceed with confidence.
DOT’s engagement model spans strategy through to implementation. We architect and oversee data migration activities, design and build clean data pipelines, and coordinate with your technology vendors throughout the integration process. We operate in a technology-agnostic manner, working across all major cloud platforms and enterprise data systems.
DOT designs Data Governance Frameworks to be complementary to , and fully consistent with , existing regulatory compliance structures. We map data ownership and quality controls to your GDPR Records of Processing Activities (ROPA), align access policies to your ISO 27001 information asset register, and ensure all AI-specific data processing activities are documented and compliant.