Avoid the Expensive AI Lesson — Invest in the Data Foundation
🎯 Your organisation
- Type: Executives funding analytics/AI initiatives
- Business Profile: Ambitious AI roadmap, fragmented data estate
🧩 The Challenge
Budgets prioritize advanced AI capabilities while underfunding the basic data foundation. Without standardization and lineage, outputs are slow, brittle, and hard to trust.
💬 “We bought the Ferrari before we built the road.”
🚀 What DigitalCore Delivers
✅ Tactical Insights (Month to Month)
- Baseline data contracts with minimal fields to run monthly governance
- Domain playbooks: what ‘good enough’ data looks like for each use case
- Evidence trail: assumptions, sources, and refresh cadence
✅ Strategic Alignment (Quarter to Quarter)
- Stage-gated roadmap: foundation → domain rollouts → predictive scenarios
- Costed options showing trade-offs between speed and rigor
- Quarterly investment reviews tied to measurable data readiness
✅ Operational Optimization
- Use current systems; add staging and quality snapshots
- Automated lineage and definition catalogs
🧠 Example: Quarterly Data Investment Review
- Assess Readiness
→ Review domain readiness scores and blockers. - Select Stage
→ Approve next stage: contracts, rollouts, or scenarios. - Fund Fixes
→ Allocate budget to remove specific, high-impact blockers. - Verify Outcomes
→ Check improved readiness and time-to-insight.
🔒 Why Service Desk Leaders Choose DigitalCore
| Challenge | DigitalCore Value |
|---|---|
| AI stalled by data mess | Start with minimal viable contracts and lineage |
| Unclear priorities | Stage-gated plan with measurable readiness |
| Brittle outputs | Governed definitions and refresh cadences |
| Cost overruns | Costed trade-offs and checkpoint reviews |
| Low trust | Evidence trail for leadership |
🏁 Business Outcome
From:
Over-spend on tools, under-spend on data readiness.
To:
Right-sized investment in a durable data foundation.