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S5 · Data foundation

Most failed AI initiatives
are failed data initiatives.

A 70-day engagement that fixes the foundation before the deployment. Four phases covering data assessment, governance foundation, quality and lineage, and architecture implementation, all calibrated to the AI use-cases you plan to run.

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Engagement summary

What we deliver, and why.

The Data Readiness for AI engagement is for organisations who have already discovered, or are about to, that AI deployment exposes every data weakness their organisation has been carrying for years. Across seventy days we run a four-phase build that assesses your data estate against AI-specific requirements, establishes the governance foundation, addresses quality and lineage, and implements the architecture changes needed for your priority use-cases. The output is a data environment AI can actually be built on.

The phases

How we run this engagement.

  1. Data assessment

    Inventory data assets across the use-cases in scope. Profile against AI-specific dimensions: quality, lineage, sensitivity, ownership, model-suitability, and regulatory classification. Identify the priority remediation set.

  2. Governance foundation

    Establish or extend data governance to address AI-specific obligations. Update data ownership, classification schemes, retention rules, and the integration with the AI use-case register.

  3. Quality & lineage

    Implement targeted quality remediation against priority assets. Establish lineage capture for AI-bound data flows. Embed the controls that will keep quality from regressing post-engagement.

  4. Architecture & handover

    Design and implement the architecture changes needed to make priority use-cases viable: feature stores, governed access patterns, and integration with existing data platforms. Hand over with a 12-month evolution plan.

Deliverables

What you actually receive.

Every artefact below is yours to keep, drafted in your house style and language, and designed to be defensible to your board, audit committee, or regulator.

  • Data asset inventory (AI-tagged)
  • AI data quality assessment
  • Lineage and traceability map
  • Updated data governance framework
  • Sensitivity and classification model
  • Architecture change set
  • Quality remediation runbook
  • Twelve-month data evolution plan
Frequently asked

Answers to the questions we get most.

It helps but is not required. About a third of S5 engagements are the first formal data governance investment the organisation has made; we calibrate scope accordingly.
No. Optivity is platform-agnostic. We design the architecture changes, validate the governance and quality posture, and work alongside your existing platform team or data integrator. We do not deliver build.
Directly. Sensitivity classification, data minimisation, and AI-specific privacy considerations are core to phase 2. We work alongside your privacy team or external counsel as needed.
Both. About 60% of clients run S5 after their first S3 pilot exposes data weaknesses; the remainder run S5 first when initial assessment makes clear that data is the binding constraint.

Want this delivered?

Book a 30-minute discovery call. We'll confirm fit, walk you through the playbook in detail, and shape a proposal calibrated to your context.

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