3-Year Data Roadmap

Year 1: Foundation

• Hire core team (Head of Data, 2-3 DE, 2-3 analyst).
• Build warehouse + dbt + BI.
• Data governance basic (policy, owner, access).
• Top 20 critical dashboard.
• Data literacy training program.
• Budget: $500k-1M for mid-size.

Year 2: Democratization

• Self-service rollout.
• Semantic layer mature.
• Embedded analyst per major BU.
• Experimentation platform.
• First ML production model.
• Data catalog.
• Budget: $1-2M.

Year 3: Activation

• ML scaled (5-10 production model).
• Reverse ETL (embedded analytics).
• Real-time pipeline for select use case.
• Customer 360.
• Data product mindset.
• LLM/GenAI use case.
• Budget: $2-5M.

KPI track

• Year 1: data infrastructure uptime, dashboard adoption.
• Year 2: self-service usage, decision velocity.
• Year 3: business outcome (revenue, retention, cost) tied to data.

Adjustment

• Quarterly review with C-suite.
• Macro event (recession, growth) trigger adjustment.
• Don't blindly stick — pivot if needed.

Common pitfall

• Year 1 too ambitious — ML before foundation → fail.
• Year 3 still building infrastructure — no value to business.
• No clear ownership.
• Hire ahead of need — burn rate.

VN context

• Budget tighter than US.
• Senior talent harder to hire (compete with tech native).
• Phase aggressively — be opportunistic.