A practical worksheet to scope the cost, effort, and trade-offs of building in-house vs. buying. Read the full guide here:
Chord Commerce: The Retail Guide to Data and AI Infrastructure.pdf
Whether you build in-house or partner with a platform, the goal is the same: a reliable data and AI foundation you can trust.
This template is designed to help you scope the real lift: from resourcing and technical scope to cost, risk, and opportunity cost. By the end, you’ll have a clearer picture of what’s worth building yourself—and what’s better to buy.
Your stack is only as strong as the team behind it. Map out who will own the build and, more importantly, who will keep it running long-term.
| Role | Responsibilities | Owner | Bandwidth (Yes/No) |
|---|---|---|---|
| Data Architect | Schema design, modeling, governance | ||
| Data Engineers (1–2) | ETL pipelines, integrations, QA | ||
| Analytics Lead | Metric definitions, reporting logic | ||
| AI/ML Engineer | Context modeling, prompt infra | ||
| Marketing & Ops Leads | Use-case scoping, campaign integration | ||
| Compliance & Access Owner | Permissions, audit trails |
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Building is only half the battle. Maintenance, security, and improvement need long-term ownership.
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Building a foundation means covering every layer, from raw infrastructure to AI readiness and activation.
✅ Check off what your stack will need to support.
Core Infrastructure
Analytics & Identity