5.2.2. Data Strategy, Ownership, and Sharing Frameworks

💡 First Principle: Data becomes an enterprise asset only when someone is accountable for it and there are agreed rules for sharing it. A data strategy sets direction (which data matters most and why), ownership assigns accountability (who decides and who maintains quality), and sharing frameworks make reuse routine instead of a negotiation each time.

Data strategy connects data to business objectives. It identifies the data domains most critical to strategy (customer, product, operations), sets quality and accessibility targets, determines investment priorities, and establishes how data will be governed. For AI, a data strategy also addresses which data may be used for training, retrieval, and inference, and how proprietary data will be developed as a competitive asset (3.3.3).

Data ownership and stewardship assign accountability:

• Data owners (usually senior business leaders for a domain) decide who may use the data and for what, and are accountable for its quality and protection.
• Data stewards maintain definitions, quality rules, and metadata day to day.
• Data custodians (often technical teams) operate the systems that store and move the data.

Without clear ownership, nobody can approve access, fix quality, or answer a regulator's question about how data was used.

Data sharing frameworks set the rules for using data across organizational boundaries:

• Internal sharing agreements define which teams can use which data, for what purposes, with what protections.
• Data products package well-defined, documented, quality-assured datasets with a named owner for reuse across many use cases.
• External sharing agreements with partners, suppliers, or customers specify permitted uses, privacy obligations, and IP rights.
• Policy alignment ensures sharing respects consent, privacy law, and contractual limits.

Together, these turn data from a series of local negotiations into a governed platform for AI.

⚠️ Exam Trap: When a scenario shows recurring delays and disputes over data access for AI, or nobody able to fix data quality, the best answer establishes data ownership and stewardship and a data sharing framework, ideally as part of a data strategy linked to business objectives. Purely technical answers (a new data lake) without ownership and agreements leave the root cause in place.

Reflection Question: Two business units each refuse to share customer data with the other's AI projects, citing privacy and quality concerns. What elements of a data strategy and sharing framework would resolve the standoff while protecting legitimate concerns?