Understand Where Your Data is GenAI Ready—and Where It’s Not
This fast paced, interactive workshop helps you define the data readiness measures and operating approach that make GenAI data health visible, comparable, and actionable across your business. You’ll leave with a practical data readiness insights plan to spot bottlenecks early, prioritize remediation, and steadily improve GenAI performance—so your data becomes a scaling
advantage, not a delivery constraint.
The Challenge
It can be challenging to scale GenAI when data readiness is assumed instead of measured:
- Data quality isn’t defined for GenAI needs: Traditional quality checks don’t capture the attributes that most affect GenAI performance and reliability.
- Readiness signals are hard to see end-to-end: Lineage, coverage, freshness, and annotation gaps show up late—after teams are already building.
- Remediation isn’t prioritized or operationalized: Issues get logged, but ownership, urgency, and follow-through are inconsistent—so bottlenecks persist.
If data readiness isn’t measurable and managed, your GenAI efforts are going nowhere fast.
Our Solution
A structured, hands-on workshop that helps you define a targeted GenAI data readiness insights plan—so you can measure what matters, surface bottlenecks early, and drive continuous improvement.
- Explore our GenAI Insights Best Practices catalog: Review proven patterns for data readiness measurement, scorecards, alerts, and remediation workflows.
- Define your priority data readiness measures: Select the core indicators that most impact GenAI quality, reliability, and time-to-value.
- Define your reporting frequency: Establish an operating cadence for data health reviews across delivery teams and data owners.
- Assign an accountable owner: Clarify who owns metric definitions, scorecards, remediation coordination, and stakeholder communication.
- Define actionable next steps: Identify what to instrument, score, alert, and remediate first to unblock near-term use cases and improve readiness over time.
Area of Focus
- Defining data quality metrics aligned to GenAI use
- Tracking lineage, coverage, freshness, and annotation
- Building health scorecards and remediation plans
- Alerting stakeholders on data bottlenecks
- Driving continuous improvement via readiness insights
Participants Will
- Leave with a defined set of GenAI-aligned data readiness measures covering quality, lineage, coverage, freshness, and annotation.
- Identify a data health scorecard approach that makes readiness gaps visible and comparable across use cases.
- Define a remediation prioritization method to focus effort on the highest-impact bottlenecks first.
- Establish an alerting and stakeholder notification plan to reduce surprise blockers and improve delivery flow.
- Produce a next-step action plan for instrumentation, scorecard rollout, and continuous readiness improvement.
Who Should Attend:
Solution Essentials
Format
Virtual or in-person
Duration
2 Hours
Skill Level
Beginner to Advanced (non-technical friendly)
Tools
- Optional data readiness measures worksheet, data health scorecard template, remediation prioritization grid, and bottleneck alerting checklist