Trusted data, smarter AI: The expanding role of chief data officers in data stewardship

Initiation and concept phase

The CDO sets a clear data strategy by assessing AI data readiness, identifying gaps, and aligning opportunities with business outcomes. They prioritize high-value internal, external, and third-party data sources, making informed decisions on ownership, licensing, and acceptable use to ensure every initiative begins on sound legal and risk footing.

Research and design phase

The CDO equips teams with secure, high-quality, and appropriately protected datasets that accelerate model exploration while safeguarding sensitive information. By establishing clear access and usage guardrails, the CDO enables innovation while maintaining compliance, privacy, and enterprise risk standards.

Develop, train, and deploy phase

The CDO oversees rigorous data operations, pipelines, lineage, and controls to ensure reliable, well-documented data flows into production models. They drive consistency in feature creation and management, so performance remains stable, models stay comparable over time, and insights translate into sustained business value.

Operate and maintain phase

The CDO champions continuous monitoring and prompt remediation to preserve data quality, fairness, and integrity as models run. They lead transparent practices in logging, auditing, and reporting data usage, and proactively mitigate risks to privacy, security, and compliance. By conducting regular assessments, the CDO maintains trust as AI scales.

As organizations scale their AI adoption, the CDO’s office is responsible for:

• Conducting data readiness assessments during the concept phase of the AI lifecycle
• Enabling rapid and secure data provisioning during active development in the design phase
• Ensuring continuous data integrity, security, and compliance once models are deployed

The CDO’s unique role ensures trusted AI begins with trusted data

Data trust drives model trust, making the CDO essential to AI achievements. Through rigorous governance and close partnerships with technology, business, risk, legal, and privacy leaders, the CDO turns raw data into reliable, auditable intelligence for AI. By enforcing standards for quality, ethics, privacy, and compliance, the CDO helps reduce bias, curb technical debt, and protect against regulatory lapses. This keeps AI aligned with mission goals, risk appetite, and evolving rules.

Core functions and enterprise impact

The CDO holds enterprisewide authority over data policy, stewardship, and controls, and is solely responsible for ensuring data fitness for AI implementation. This includes overseeing the quality, suitability, and compliant use of data, all of which are critical factors for determining AI readiness across the enterprise. While other functions, such as IT or business units, manage data for their respective domains, only the CDO is positioned to confirm that the organization’s data assets are genuinely “AI-ready,” drawing on established frameworks such as Trustworthy AI¹ to guide this effort.

CDOs today are architecting foundational blueprints, focusing on:

• Data quality: Curate and pre-vet datasets before they are used in AI initiatives. This includes rigorous documentation of data lineage and the use of bias metrics to certify the trustworthiness of the data.
• Compliant usage: Own and enforce enterprisewide compliance with evolving regulations, such as the White House’s AI Action Plan,² by setting clear standards for transparency, data minimization, and governance in all AI-driven activities.
• Fairness: Proactively audit training datasets for potential bias and establish measurable thresholds for demographic representation before models move to development. This approach helps reduce risk and supports ethical outcomes.
• Transparency: Maintain end-to-end data lineage and detailed metadata for all data features that inform AI model predictions, supporting both auditability and regulatory review.
• Data stewardship: Define clear data ownership roles, hold stewards accountable for ongoing data quality and ethical use, and implement sustained monitoring for signals of data drift that could impact model reliability.
• Privacy: Embed privacy by design across the lifecycle, including purpose limitation, consent management, de-identification and pseudonymization, differential privacy where appropriate, privacy impact assessments, cross-border transfer controls, and data protection impact assessments where required.
• Security: Direct and govern enterprisewide standards for protecting sensitive information throughout the AI life cycle. Collaborate with security leaders to establish privacy safeguards and protect data during AI development and use, reinforcing trust in AI systems for responsible innovation.

These imperatives require more than technical fixes and call for cross-functional alignment, robust governance processes, and clear lines of accountability, all powered by the CDO’s leadership.

Scaling AI responsibly depends on unified C-suite leadership

Enterprise AI achieves outcomes through close collaboration across the C-suite, where each executive brings insight and authority to the table. The CDO stands at the intersection of these diverse roles, acting as both a convener and catalyst within the leadership team.

From the outset, the CDO works alongside peers, including the chief information officer, chief information security officer, chief AI officer, chief privacy officer, chief financial officer, chief risk officer, and chief human resources officer.

This collaboration often plays out in several ways. For instance, the CDO collaborates with the CIO to align data supply with evolving technology infrastructure, reducing risks caused by mismatched systems or versioning errors. When working with the chief information security officer or chief privacy officer, the CDO supports privacy and security controls that help protect sensitive data and meet regulatory expectations throughout the AI lifecycle.

Collaboration with the CAIO increases focus on data quality, ethical practices, and transparency across all AI initiatives, creating a foundation for model reliability. In tandem with finance and risk leaders, such as the chief financial officer and chief risk officer, the CDO integrates data governance into organizational risk and audit strategies, thereby strengthening the enterprise against financial volatility and compliance setbacks. Joint efforts with the chief human resources officer further position the workforce for effectiveness, embedding data literacy and responsible AI practices into talent development programs.

Establishing formal agreements and shared accountabilities, such as responsible, accountable, consulted, and informed matrices, across these roles is vital for clarity and accelerated adoption of trustworthy AI throughout the organization.

Risks that can derail an AI-first data strategy and ways to overcome them

Amid rapid innovation and heightened expectations, CDOs navigate a range of unique challenges that can quickly derail progress if left unchecked. To build a foundation for long-term achievement and trust, it’s important to proactively address four common missteps that frequently emerge in AI transformations:

1. Governing too late: Many organizations focus on model outputs instead of the quality and provenance of data inputs. When this happens, flaws, biases, or compliance issues within the data become embedded in models from the outset. Additionally, using unlicensed or unsourced data exposes the organization to significant legal, regulatory, and reputational risks. Without establishing strong contractual and technical safeguards prior to training, these risks compound, making long-term scalability much more difficult.
2. Failing to maintain a model registry: Organizations often fail to maintain a centralized inventory that accounts for each model’s purpose, ownership, data sources, and risk characteristics. This absence of visibility and documentation makes the enterprise vulnerable to audit failures, data security incidents, and compliance breakdowns. A broad, continuously updated model registry not only supports oversight but also accelerates incident response and ongoing governance.
3. Operating with regulatory tunnel vision: Many organizations approach compliance as a static checklist, satisfied by federal regulations alone. However, true risk management demands ongoing attention to a shifting and expanding set of state, local, and industry-specific mandates. Overlooking sector-focused or regionally driven rules—such as financial sector data retention requirements or local biometric privacy mandates—can lead to unexpectedly steep penalties and operational setbacks.
4. Underestimating post-deployment monitoring: Once deployed, organizations often struggle to keep up with the latest developments in AI. The rapid adoption of advanced techniques, such as agentic AI systems and federated learning, introduces new and nuanced implications for data readiness, governance, and monitoring. Federated learning, for instance, requires careful management of decentralized data and rigorous privacy-preserving techniques. Agentic systems, meanwhile, demand real-time oversight of autonomous model actions and dynamic context retrieval.

To overcome these challenges, the CDO can embed end-to-end data discipline by taking the following actions.

• Drive data readiness from the outset. Conduct rigorous data readiness assessments during the concept phase to ensure early alignment with business goals and compliance requirements.
• Accelerate AI development. Enable secure and rapid data provisioning during the design phase, empowering teams to innovate with confidence.
• Safeguard long-term value. Maintain ongoing data integrity, security, and compliance through continuous monitoring and proactive stewardship in production.

These imperatives make the CDO role both uniquely challenging and strategically vital, demanding relentless focus, cross-functional collaboration, and a forward-thinking approach to governance.

Effective AI deployment is driven by the CDO’s end-to-end stewardship

In an era shaped by both data-driven opportunity and heightened scrutiny, the CDO is the executive who makes AI not only possible but also truly responsible.

With end-to-end accountability across the entire AI life cycle—from concept to ongoing monitoring—the CDO ensures that every AI initiative upholds data quality, compliance, security, and fairness.

Their central role in aligning C-suite leaders and establishing enterprisewide standards fosters responsible innovation, maximizing the value of AI investments. By establishing actionable governance, transparent processes, and broad data stewardship, the CDO strengthens organizational trust in both data and AI outcomes.