Synoptek AIDR: AI Data Readiness Assessment

Synoptek’s Azure AI Data Readiness (AIDR) assessment helps organizations evaluate their data landscape and readiness to adopt AI solutions. This two-week engagement identifies AI opportunities, measures data maturity, and delivers a practical roadmap to move from data discovery to implementation.

AIDR ensures your organization’s data is accurate, available, secure, and actionable—laying a strong foundation for AI initiatives that drive measurable business outcomes.

Engagement Structure

Week 1 – Data Discovery

Day 1–2: Stakeholder Interview

• Conduct workshops with SMEs to understand data sources, integrations, and platforms

• Map critical data domains (transactional, behavioral, third-party)

• Assess risk appetite, compliance, and data access boundaries

Day 3–5: Data Evaluation & Assessment

• Conduct workshops to review data readiness, connectors, and security boundaries

• Assess data across five dimensions — Availability, Volume, Quality, Integrity, and Governance

• Profile sample datasets by source, volume, and quality

Week 2 – Assessment & AI Readiness

Day 6–8: AI Readiness Evaluation

• Identify 3–5 potential AI use cases

• Score each using the AIDR Scoring Matrix (Business Impact, Feasibility, Adoption Readiness, Integrity, and Compliance)

• Rank use cases based on readiness and strategic value

Day 9–10: Implementation Guidelines

• Review AIDR scoring matrix with stakeholders

• Finalize 1–2 priority use cases

• Define implementation roadmap and next steps

Key Deliverables

• Data Readiness Report: Scoring, gaps, and risk analysis

• Scope & Roadmap Document: Prioritized AI initiatives with dependencies

• Implementation Blueprint: Action steps, owners, and tool recommendations

Scoring Matrix (AIDR)

Each dimension is rated 1–5 across:

• Business Impact: Score for measurable ROI or strategic value potential.

• Feasibility: Ease of implementation using the current data, technology stack, and resources.

• Adoption Readiness: Organizational and business readiness to adopt and operationalize the solution.

• Integrity: Quality, completeness, and lineage of data; schema health and reliability.

• Compliance: Adherence to risk, privacy, and policy standards.