Strategy & DataRun your real assessment
AI & Data Readiness
1–4 weeksReadiness diagnosticDemo
67%
Building the foundations
Overall readiness
You have key pieces in place. It pays to close the weakest gaps before scaling to production.
Data2/3
Systems3/3
Team2/3
Use case2/3
Governance1/3
Before investing in AI you need to know three things: whether your data can be trusted, which opportunities are genuinely worth pursuing, and where to start.
This assessment delivers all three — a complete diagnostic of your data asset (what's there, what's missing, what's trustworthy), an opportunity map prioritized by impact and feasibility, and a roadmap aligned with your leadership. The right starting point before any analytics or AI project, without committing to a larger engagement. Technically: statistical profiling (distributions, missing-value patterns MCAR/MAR/MNAR, outliers, lineage), data quality and readiness scorecard, gap analysis between current systems and model requirements, build vs. buy comparison, and KPI and analytics-maturity definition.
Use cases
- —You have data but no clear read on its quality, usefulness, or risks
- —You have AI ideas but do not know which ones justify investment
- —You need to prioritize several opportunities before committing budget
- —Your leadership needs an evidence-based data and AI roadmap
- —You are about to migrate, integrate, or redesign systems and need to audit the foundation first
Deliverables
- —Statistical profiling report and data quality map per source
- —Data readiness scorecard (volume, quality, labeling status)
- —Data Science / AI opportunity map prioritized by impact and feasibility
- —KPI framework and analytics maturity map (current vs. desired state)
- —Build vs. buy analysis, risk register, and mitigation plan
- —Prioritized roadmap and technical/commercial proposal for the recommendation