Never seen a data quality issue that wasn’t actually an ownership problem | John Wernfeldt

2026-01-05 · Source: Modern Data 101 · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Data Governance · Depth: Intermediate, medium

Summary

John Wernfeldt, Managing Director at Northridge Analytics and President of DAMA Sweden, argues that persistent "data quality issues" are fundamentally problems of ownership and decision authority, not technical flaws. A recent survey from the Modern Data Report 2026 supports this, indicating that 93% of respondents encounter conflicting metrics, nearly half distrust their data, and 68% find it insufficient for AI. Wernfeldt highlights a recurring cycle where metrics break, temporary fixes are applied without addressing root causes, and accountability remains elusive. He asserts that labeling these as data quality issues allows organizations to push responsibility to IT and focus on symptoms, rather than establishing clear ownership for metric definition, calculation, changes, and accountability. The article proposes a five-step model to establish decision authority for data, emphasizing naming a single accountable owner, separating ownership from contribution, defining change rules upfront, tying data quality rules to ownership, and enforcing these rules with leadership support.

Key takeaway

For CTOs and VPs of Data struggling with data trust and AI readiness, recognize that persistent data quality problems stem from a lack of clear ownership and decision authority, not just technical issues. Implement a robust data governance model that explicitly assigns single accountable owners for key metrics, defines change management protocols, and enforces these rules to build a trusted data foundation. Avoiding this discomfort perpetuates data fragmentation and hinders strategic AI initiatives.

Key insights

Data quality issues are primarily ownership problems, not technical ones, hindering AI readiness.

Principles

• Accountability cannot be delegated.
• Contribution is not ownership.
• Enforcement is critical for governance.

Method

Establish decision authority by naming a single accountable owner for each metric, separating ownership from contribution, defining change rules, linking quality rules to ownership, and enforcing these mandates.

In practice

• Name one person accountable for each critical metric.
• Define clear rules for metric changes and communication.
• Tie data quality rules directly to specific owners.

Topics

• Data Governance
• Data Ownership
• Data Quality Management
• AI Readiness
• Metric Management

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

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