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Products / Data Foundation for AI
Productised capability

Data Foundation for AI

Modernise data quality, pipelines, access, metadata and governance so analytics and AI can operate reliably.

Commercial model: scoped implementation, managed operation or recurring service depending on the workflow and operating requirements. Current commercial terms are confirmed after scoping; no outcome is guaranteed before evidence is established.

Capability components

Critical data mapping

Configured around the workflow, approved data, accountable owner and success criteria.

Data quality

Configured around the workflow, approved data, accountable owner and success criteria.

Pipelines and integration

Configured around the workflow, approved data, accountable owner and success criteria.

Metadata

Configured around the workflow, approved data, accountable owner and success criteria.

Access and governance

Configured around the workflow, approved data, accountable owner and success criteria.

AI-ready data products

Configured around the workflow, approved data, accountable owner and success criteria.

Reference operating architecture

Experience · users, channels, interfaces and human workflows
Intelligence · models, agents, prompts, retrieval and business logic
Knowledge & data · approved sources, structured data, context and quality
Integration · APIs, enterprise applications, events and tools
Trust · permissions, evaluation, logging, monitoring, escalation and governance

Success measures

Data qualityMeasure against an agreed baseline.
Pipeline reliabilityMeasure against an agreed baseline.
Data-product adoptionMeasure against an agreed baseline.
Time to accessMeasure against an agreed baseline.
Issue resolutionMeasure against an agreed baseline.
AI use-case readinessMeasure against an agreed baseline.

Integration landscape

✓ Cloud / warehouse
✓ Databases
✓ ETL / orchestration
✓ BI
✓ Identity
✓ Data catalogue

Integration feasibility depends on the systems, licences, APIs, security model and access approved by the client.

Implementation path

Discover

Workflow, users, data, baseline.

Design

Architecture and controls.

Prove

Evaluation and decision gate.

Deploy

Integration and adoption.

Operate

Monitor, improve, govern.

Required controls

Accountable owner

A named business owner for outcomes and permitted use.

Human escalation

Defined points where people review, approve or take over.

Evaluation

Quality, safety, cost and operational performance measured before and after release.

Permissions

Least-privilege access to systems, tools and knowledge.

Auditability

Appropriate records of actions, approvals and important changes.

Review cadence

Periodic review of quality, risk, cost and continued suitability.

Build internal capability alongside delivery

Pair implementation with DataLgorithmics Academy pathways so operational, technical and governance teams can sustain the capability.

Academy pathwaysTrust CentreDiscuss Data Foundation for AI