Data Strategy
AI without data strategy is a sports car without fuel. It looks impressive in the showroom and goes absolutely nowhere. This lesson teaches you how to build the data foundation that makes AI actually work — not in theory, but in your real organization with its real messiness.
What you will learn
- How to audit your data landscape and identify what is usable for AI
- Data governance frameworks that enable AI without creating bureaucracy
- The build vs. buy decision for data infrastructure
- How to create a data strategy that evolves with your AI ambitions
Know What You Have Before You Build What You Need
Start with an inventory. Where does your data live? CRM, ERP, spreadsheets, email threads, legacy databases, third-party tools. How much is structured versus unstructured? How current is it? Who owns it? Who can access it? These questions sound basic because they are — and most organizations cannot answer them.
The audit reveals three categories: data you have and can use, data you have but cannot use (quality, access, or legal issues), and data you need but do not have. Your data strategy addresses all three.
Guardrails, Not Roadblocks
Data governance gets a bad reputation because most organizations implement it as bureaucracy. Committees, approval chains, 40-page policies nobody reads. Effective data governance for AI is lightweight and enabling. It answers four questions: who can access this data, what can they use it for, how must it be protected, and who is accountable?
Document those answers. Automate enforcement where possible. Review quarterly. That is your governance framework. It should fit on one page and take less than a day to implement for any new AI use case.
Data Architecture — Match Each Layer to Its Role
Tap one on the left, then its match on the right
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