Data Ownership in Practice: Defining Decision Rights in Enterprise Data Governance

2026-01-05 · Source: Modern Data 101 · Field: Technology & Digital — Data Management & Governance, Operations & Process Management · Depth: Intermediate, medium

Summary

Antonio Sánchez Gómez, a Data Management & Governance Specialist at Roche, argues that Data Ownership is frequently defined but ineffectively exercised in many organizations. He contends that despite mature frameworks, Data Owners often lack real authority and decision-making power, becoming sidelined in critical decisions like accepting data-related risk or approving quality thresholds. The core issue is not role definition but a lack of explicitly designed decision rights within the operating model. Gómez emphasizes that Data Ownership must be reframed as a decision-centric role, formally empowered to make decisions across core domains such as data quality, usage, access, structural change, and risk acceptance, rather than merely performing activities. This structural redesign is crucial for Data Governance to function as an operational capability.

Key takeaway

For CTOs and VPs of Data struggling with ineffective Data Governance, you must shift your focus from merely defining Data Owner responsibilities to explicitly designing and empowering their decision rights. Ensure Data Owners have formal authority to make critical decisions regarding data quality, usage, access, and risk acceptance. This structural change will transform governance from an aspirational framework into an operational capability, improving accountability and reducing decision latency across your organization.

Key insights

Effective Data Ownership requires explicit decision rights, not just defined responsibilities, to function operationally.

Principles

• Governance operates through decisions, not tasks.
• Responsibility without power erodes credibility.
• Data Ownership is inseparable from risk acceptance.

Method

Redesign Data Ownership around explicit decision rights for core domains like quality, usage, access, structural change, and risk acceptance, embedding these rights into workflows and formal escalation paths.

In practice

• Define acceptable data quality thresholds.
• Authorize or restrict data usage based on value.
• Approve access models and exceptions.

Topics

• Data Governance
• Data Ownership
• Decision Rights
• Master Data Management
• Data Stewardship

Best for: CTO, VP of Engineering/Data, Executive, Data Scientist, Director of AI/ML, Consultant

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• Data Mesh, Data Platform, or Data Chaos? Choosing the Right Ownership Model (Data #3) — Effective data ownership scales through intentional hybrid structures, not by choosing between centralized or decentralized models.
• Data Strategy and Data Governance, reimagined. — Effective data management requires distinct, interconnected layers for strategy, governance, and operational execution.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Modern Data 101.