Market signals | Data leadership

Chief Data Officer (CDO)

Giving business decisions data whose meaning, ownership and history are clear and traceable.

Overview

The CDO’s remit: information the business can use and trust

The Chief Data Officer establishes how the organization owns, understands and uses data. The remit commonly spans data strategy, governance, quality and analytical enablement, with platform responsibilities shaped by the relationship with technology leadership. Its purpose is to make information dependable enough for the decisions and services that rely on it.

Today, that requires more than centralizing records or commissioning dashboards. Business functions may use different definitions for a customer, an active account or a completed transaction. The CDO helps distinguish legitimate differences from contradictions, assigns ownership and makes the meaning and origin of important information visible to the people using it.

The role also connects investment in data capabilities with operating priorities. Analysts, engineers and stewards need a clear account of which decisions matter and what quality is sufficient for each use. Privacy, security and retention requirements want to be coordinated with the accountable specialists. Sometime downstream items will occur but engaging early for alignment is part of this role.

A useful strategic review follows one important decision back to its information. Can the user find the relevant data, understand its limitations, and trace how it is checked and reconciled as it moves between systems and teams? The CDO's contribution becomes tangible when trusted information reduces repeated checking and enables functions to understand decisions and decision flows consistently across the organization.

Role signals

What is shaping the role now

Data definitions and ownership

Aligning on what data means

29% have clear measures of the business results delivered by data.

What this asks of the role

Align on the meaning of important terms such as customer, revenue or active account so teams interpret information consistently.

2025 | Global data/analytics leaders.

Reliable data services

Getting approved data to teams

84% say their data products create significant advantages over other businesses.

74% actively promote data stewardship within their teams.

What this asks of the role

Make useful datasets available through clear access routes so teams can obtain the information their work requires.

2025 | Global data/analytics leaders | Separate findings; not parts of a total.

The role today

  • Aligning on what data means

    Align on the meaning of important terms such as customer, revenue or active account so teams interpret information consistently.

  • Business owners for data

    Give decisions about important data to the business leaders who understand its purpose and can authorize changes.

  • Getting approved data to teams

    Make useful datasets available through clear access routes so teams can obtain the information their work requires.

  • Maintaining shared datasets

    Treat shared datasets as ongoing services, with team members responsible for updates, documentation and support after initial delivery.

  • Analysis tied to a decision

    Start each analysis with the business decision it should inform so the result helps someone choose a next step.

  • Appropriate access to information

    Work with privacy and security colleagues to give team members the access their roles need while protecting sensitive information.

Pressure Points

The CDO’s pressure: shared information, divided ownership

The CDO's most persistent pressure is that data crosses organizational boundaries while authority often remains within them. A data quality review may reveal inconsistencies that originate in a sales process, service workflow or supplier feed. The central data team can assess the situation while not having the authority to implement the changes.

Demand adds another tension. Leaders want faster insight, new analytical products and AI-ready information while analysts continue answering ongoing reporting requests. Each local extract may take care of an immediate need and create another definition or maintenance obligation. The result can be a growing volume of information without a clearer shared account of performance.

Trust depends on details that are easy to overlook in a platform programme. Changes to operational processes can alter field meanings; permissions can delay legitimate use; incomplete lineage can hide transformations. Asking for uniformly “perfect” data is rarely practical. Attending to what quality looks like across different business owners is the important work for the CDO to attend to.

The strategic response is to prioritize data through its use. Identify the decisions or services most affected, align on the necessary quality and assign correction work to accountable owners. Review whether recurring requests should become maintained products. This gives the CDO a clear sequence of work and positions stewardship as a shared business responsibility, supported by the central data team.

Common pressure points

Shared meaning and business ownership

  • Different meanings for the same measure

    Teams can discuss the same label while counting different customers, transactions or reporting periods.

    What to look atCompare definitions before interpreting differences between reports.

  • Ownership assigned only to technology

    Data decisions can wait when technical teams maintain information but business responsibility remains unclear.

    What to look atCheck who can decide definitions, acceptable quality and permitted use.

Reliable and usable data services

  • Datasets without ongoing maintenance

    A dataset delivered for one project may keep being reused after updates and support have stopped.

    What to look atReview ownership, update schedules and current user expectations.

  • Sources without enough context

    People may find a dataset without enough context to decide whether it suits their task.

    What to look atCheck availability of definitions, origin, update timing and ownership.

Analysis leaders can use

  • Analysis without a decision owner

    A technically strong analysis can go unused when leaders have not aligned on the decision it should inform.

    What to look atReview the intended user, decision and timing before commissioning more work.

  • Assumptions hidden in the output

    A clear chart may conceal assumptions or data coverage that change how its findings should be interpreted.

    What to look atReview analytical assumptions and sensitivity to alternative inputs.

Governance and team capacity

  • Stewardship added to a full workload

    Individuals named as data stewards may have little time or authority to maintain shared standards.

    What to look atReview stewardship commitments alongside their other responsibilities.

  • Local spreadsheets replacing shared data

    Teams may recreate extracts when shared services feel slow or do not meet the immediate need.

    What to look atAsk which unmet needs drive repeated local copies.

Selected external benchmarks

Research note: These figures describe the groups studied. They do not measure your organization’s performance or set goals for it.

  • Disconnected data
    83%

    say data silos hinder innovation, real-time analysis and decisions.

    2025 – Global data leaders

  • Filling data roles
    77%

    struggle to fill key data roles.

    2025 – Global data/analytics leaders

  • Data quality for compliance
    56%

    cite data reliability and quality as a compliance challenge.

    2025 – Compliance survey executives

  • Access to compliance data
    47%

    cite data availability as a compliance challenge.

    2025 – Compliance survey executives

Conditions to Deliver

Shared ownership of trusted information

The CDO contributes best when business leaders own the meaning and acceptable quality of important information, while data teams provide dependable ways to find, access and maintain it. Decisions about customers, products and performance need named owners who can resolve competing definitions and authorize changes at their source.

The role also needs sustained capacity for stewardship rather than project-only attention. Analysts, engineers, privacy, security and operating teams should have clear routes for raising questions and aligning on the level of quality needed for a particular use. Visible lineage and service ownership help the CDO connect data investment to decisions people can trust.

Governance becomes useful when it is part of everyday delivery. Shared standards need practical support routes, and governance forums should focus on decisions that cannot be resolved within a team. Product owners and data stewards need the authority, time and tools to maintain definitions, resolve recurring quality questions and make remaining limitations visible.

A practical review can begin with one decision that matters to several functions. Trace the information from its source to the people using it, identify who can approve its meaning and determine what evidence shows it is dependable enough. This gives the CDO a grounded way to sequence investment, strengthen shared ownership and improve trust without requiring every dataset to meet the same standard.

Reflection questions

Are you creating the conditions for trusted data to deliver value?

  1. Where do you have clear decision authority, and where do you align with the CIO, CAIO and business leaders?

  2. Which business leaders will own key data domains, and what capacity will they provide for stewardship?

  3. What measurable business outcome will each priority data product improve, and who will lead its adoption with you?

  4. Do you have sustained funding for data quality, access, lineage and service ownership beyond initial delivery?

  5. What evidence will you require before data is considered reliable, traceable and appropriately governed for AI use?

Future Evolution

The CDO’s evolution: maintained data products and better decisions

The CDO's role is likely to put more emphasis on maintained data products and the decisions they support. A platform can make information available; a product adds clear meaning, ownership, quality expectations and a support route. This shifts attention from completing a data project to sustaining something the business can use.

AI adds a further layer of responsibility because accessible information is not automatically suitable for a particular task. Reference material may be outdated, permissions may differ by user and generated answers may conceal uncertainty. The CDO can help establish appropriate information foundations while working with AI and technology leaders on evaluation and operating controls.

Distributed stewardship becomes important as demand grows. Functions need people who understand their information and have time to maintain it. Shared definitions and interfaces provide consistency where it matters, while local expertise preserves business meaning. The central team can support those owners with standards, tooling and specialist help rather than becoming the route for every correction.

To prepare, select one heavily used dataset and treat it as an ongoing service. Name its consumers, publish its definitions, identify material limitations and review whether it improves their decisions. That approach connects the CDO's technical and governance responsibilities to practical value, without assuming that more data or more dashboards necessarily create better judgement.

Role evolution

Business ownership of shared information

  • Definitions with usable context

    As AI uses business data, the CDO may extend definitions to document meaning, context and appropriate use.

    What to watchDefinitions available within analytical and AI services.

  • Business-led data ownership

    As functions build analytical tools, the CDO may place more authority with business owners while maintaining shared standards.

    What to watchBusiness owners approving definitions and permitted uses.

From datasets to dependable data services

  • Ownership beyond delivery

    As operations depend on data products, the CDO may establish continuing ownership for quality, availability and updates.

    What to watchData owners remaining accountable after launch.

  • Context travels with data

    Self-service and AI may require data definitions, origins and qualifications to remain accessible wherever information is reused.

    What to watchUsers able to check origin and suitability.

Data supporting more automated decisions

  • Analytics within the workflow

    Embedded tools may bring the CDO's analytical services closer to the moment people or systems make decisions.

    What to watchRelevant evidence available at the decision point.

  • Reviewable analytical reasoning

    AI-assisted analysis may require clearer records of sources, assumptions and the reasoning behind important outputs.

    What to watchConclusions traceable to inputs and assumptions.

Responsible reuse at greater scale

  • Supported data stewardship

    Greater dependence on shared data may require clearer capacity, tools and authority for business data stewards.

    What to watchStewards equipped to address recurring quality questions.

  • Confidence in automated analysis

    AI interpretation may broaden data learning toward questioning answers, checking sources and recognizing when specialist review is needed.

    What to watchEvidence checks and specialist review becoming standard in automated decision workflows.

Selected external benchmarks

Research note: These figures describe the groups studied. They do not measure your organization’s performance or set goals for it.

  • Data for AI agents
    80%

    have started developing diverse datasets to train AI agents.

    2025 – Global data/analytics leaders

  • AI agent data
    79%

    are early in scaling and governing data for AI agents.

    2025 – Global data/analytics leaders

  • AI close to the data
    81%

    bring AI to their data rather than centralize the data first.

    2025 – Global data/analytics leaders

  • Building advanced data skills
    47%

    cite recruiting, developing and retaining data specialists as a major challenge.

    2025 – Global data/analytics leaders