How to Hire a Chief Data Officer: The Complete Guide for 2026

The Chief Data Officer role began as a compliance appointment, created in response to regulatory pressure on data governance and privacy. In most large organisations it has since become the seat that owns data strategy and, increasingly, AI strategy as well. That change has raised the cost of a poor appointment. A CDO hired for the wrong mandate, into the wrong reporting line or without a budget will struggle to show impact, and Gartner’s research suggests boards are running out of patience with CDOs who cannot.

Key takeaways

• 70% of chief data and analytics officers are now responsible for their organisation’s AI strategy and operating model, and 36% report to the CEO (Gartner, May 2025).
• Define the mandate before the search starts. A governance CDO, an analytics CDO and an AI-transformation CDO are different hires, and the wrong one is expensive to unwind.
• Settle the reporting line in advance. Strong candidates ask about it in the first conversation and many will not consider a role that reports into IT.
• Assess for commercial judgement, change leadership and AI fluency, not only for technical history.
• Budget, headcount and an executive sponsor should be in place before the new CDO’s first day.

Why the appointment carries more risk than it used to

Gartner’s 2025 survey of 504 chief data and analytics officers found that 70% now hold responsibility for AI strategy, and that 36% report directly to the CEO, up from 21% in its previous survey (Gartner, May 2025). The same release predicted that by 2027, 75% of CDAOs who fail to make their function essential to AI will lose their C-level standing. The seat is more senior than it was and less secure.

The context is wide adoption without much scale. McKinsey’s 2025 global survey found 88% of respondents’ organisations using AI, but only 7% reporting that AI had been fully scaled across the organisation (McKinsey, December 2025). Closing that gap is now what most boards expect a CDO to do, and it is a harder brief than building a reporting platform or a governance framework.

Define what your organisation needs

The most common mistake is to start recruiting before agreeing what the role is for. The title covers several different jobs, and a candidate who is excellent at one can be a poor fit for another. In practice we see three mandates.

The governance CDO. In heavily regulated organisations, including banks, insurers and healthcare providers, the first priority is often data quality, governance and compliance with GDPR, the EU AI Act and sector rules. The strongest candidates here have run data management programmes across large, federated organisations and usually come from risk, compliance or data management.

The analytics CDO. Where the function exists mainly to improve decisions, the mandate is analytics capability, self-service data platforms and a demonstrable return. These candidates tend to come from analytics, business intelligence or consulting, and they are comfortable being measured on commercial results.

The AI-transformation CDO. Here the CDO owns the AI agenda: model selection and adoption, machine learning operations, responsible AI and the move from pilots to production. This is the hardest profile to find, because it needs data leadership, real AI depth and the standing to change how business units work. It is also the profile most organisations now say they want, which is consistent with the Gartner figures above.

Appointing a governance specialist when the need is transformation, or the reverse, creates a mismatch that usually shows within the first year. Agreeing the mandate with the board, the CEO and the executive sponsor before the brief is written costs a few weeks and avoids that outcome.

Reporting line and positioning

Where the CDO sits tells candidates how seriously the organisation means the role. When the CDO reports to the CIO, the job tends to drift towards infrastructure and governance. That work matters, but it is rarely what a board intends when it creates the position. When the CDO reports to the CEO or COO, the role has more room to change how business units use data, because the mandate comes from the top.

Decide the reporting line before the search begins. Experienced CDOs raise it in the first conversation, and those who have run an independent function reporting to a CEO will usually decline a role that reports into technology, because they have learned how much the line limits what they can do.

Internal or external candidates

Many organisations look inside first and promote a head of data engineering, an analytics director or a senior IT leader. That can work when the internal candidate has the strategic range the job needs. It tends to fail when the promotion is chosen because it is quicker or cheaper, rather than because the person is ready for a C-level role.

The step up is large. A CDO sets strategy that affects the organisation’s competitive position, influences board decisions, deals with regulators and external partners, and may run a function that spans engineering, data science, analytics, AI and governance. Being very good at one of those parts is not evidence of readiness for all of them.

An external search gives access to the whole market, which matters most for a first CDO appointment or for a function that needs to be rebuilt. For a role of this seniority we run searches on a retained basis, because the candidates who fit are rarely looking and have to be identified, approached and assessed individually. We explain how that process works in our guide to AI executive search.

Evaluating candidates

A CV shows where someone has worked. It says little about how they will perform in your organisation. Five areas repay close assessment, and each can be tested in interview.

Strategy for this organisation. Ask the candidate what they would prioritise in their first six to twelve months, and why. General remarks about a data-driven culture are not enough. The best candidates will have studied the business, formed a provisional view of its data maturity and be able to defend it.

Change leadership. Most incoming CDOs inherit organisations that were not designed around data or AI. Ask for specific examples: what resistance they met, who they had to win over, how they kept a programme going when sponsorship weakened. A candidate who can describe the politics of a previous employer clearly is usually more effective than one who can describe only technical results.

Commercial judgement. Ask what their past initiatives were worth in revenue, cost or risk, and how they measured it. A CDO who cannot put a number on previous work will find it hard to defend a budget.

Team building. The World Economic Forum’s employer survey expects rapid growth in demand for AI, big data and cybersecurity skills to 2030, and 63% of employers already name skills gaps as the main barrier to transformation (World Economic Forum, January 2025). A CDO has to be able to hire and keep good people in that market, and to decide sensibly what to build in-house and what to buy.

AI fluency. With most CDAOs now owning AI strategy, a candidate needs enough understanding to judge vendor claims, test whether a use case is feasible and hold their own with the technical team. They do not need to be a research scientist. A CDO who delegates every AI decision will not keep the authority the role needs.

Compensation and package

CDO pay has risen with the scope of the role. In major markets, packages for CDOs who own AI strategy are now benchmarked against other C-suite roles rather than against IT leadership, and organisations that still use IT benchmarks tend to lose candidates at offer stage. That is Fergal Nolan’s observation from 25 years of hiring data and AI leaders, not a survey figure, and current market data should be gathered for each search.

Base salary is rarely what decides it. Equity, bonuses tied to measurable data and AI outcomes, retention terms and, for candidates with research backgrounds, time for conferences or study all carry weight. Agree the range with the remuneration committee before candidates are approached, so that internal debate does not delay an offer.

Setting the new CDO up to succeed

Many CDO appointments that fail do so for organisational reasons rather than because of the individual. Four conditions make a large difference.

An executive sponsor. The CDO needs visible support from the most senior level, ideally the CEO. Data and AI work touches every function, and some business-unit leaders will resist it. Without a sponsor, a capable CDO can be slowed to a halt.

A clear mandate and measures. Agree priorities and success measures for the first year before the start date. They should be specific enough to guide decisions and realistic given the organisation’s current maturity. A brief to ‘make us more data-driven’ cannot be met or measured.

Budget and headcount. Commit resources before the CDO arrives. A first quarter spent negotiating budget delays everything else and tells the new hire, and their team, that the commitment was weaker than advertised.

Relationships across the business. Arrange introductions to business-unit heads, the CIO, the CFO, heads of major functions and key partners, not only the executive committee. How well the CDO builds those relationships in the first ninety days is a good early indicator of how the appointment will go.

What to do before you start the search

Write down which of the three mandates you are hiring for and get the board and CEO to agree it. Fix the reporting line. Confirm the first-year budget and headcount. Name the executive sponsor. Agree the compensation range with the remuneration committee. Once those five decisions are made, the search itself becomes faster and the shortlist more relevant, because candidates can see exactly what they are being asked to do.

If the CDO will also lead AI in a healthcare or life sciences organisation, our article on building a healthcare AI leadership team covers how that role fits alongside clinical and technical leadership, and from pilot to production looks at the leaders who scale AI rather than pilot it. Our work sits in machine learning, AI and data leadership; to discuss a CDO appointment, talk to us.

Fergal Nolan is the founder of Banba, a specialist executive search firm for Healthcare AI and Life Sciences AI, with offices in New York, London and Berlin. He has spent more than 25 years in talent acquisition, including as a founding team member at SThree / Real Staffing and as founder of Upstream, where he has built Data Science & AI teams and AI leaders for start-ups, scale-ups and global enterprises.