What Are the Key Differences Between CDO, CAO, CDAO, and CAIO?

What Are the Key Differences Between CDO, CAO, CDAO, and CAIO?

We have seen different job titles for enterprise leadership roles focused on data and analytics:

  • Chief Data Officer (most frequently seen): CDO

  • Chief Analytics Officer (less common): CAO

  • Chief Data and Analytics Officer (more frequently seen recently): CDAO

  • Chief AI Officer, or Chief Artificial Intelligence Officer (emerging): CAIO

So what are the main differences between CDO, CAO, CDAO, and CAIO?

In this article, I will discuss these four roles from the following angles:

  1. Organizational structure and reporting line

  2. Work priority

  3. Work relationship

  4. Team members

  5. Domain knowledge

  6. Historical context

Chief Data Officer (CDO)

A CDO most likely reports to a Chief Information Officer (CIO) but can also report to other CXOs (marketing, operations, finance, strategy, or CEO).

Most people currently holding CDO titles are actually CDAOs since their job scope includes delivering business impact, providing some analytics (at least descriptive and diagnostic), and managing data science or machine learning models.

Mislabeling CDAOs as CDOs contributes to the ongoing challenges and makes them less effective in delivering optimal value to their employers.

The choice of words matters; it affects how they think about their role at a subconscious level, how their audience perceives them, what is expected from them, and how their work output is measured. This will be further discussed in the section later about CDAOs.

A typical CDO’s work focuses on data management, such as maintaining a data warehouse or data lake, setting up data governance, and creating operational reports and dashboards. CDOs work with internal clients who frequently request support on data reports.

The CDO team’s key players include:

  • Data operations staff

  • Database developers for data marts, data warehouses, and data lakes

  • Business requirements gathering analysts

  • Reporting analysts

The domain knowledge of CDOs is close to that of CIOs. CDOs are concerned about data systems, infrastructure and security, and data governance programs.

Historically, IT research and consulting firm Gartner placed CDO roles under CIOs. In the past 3 years, the firm realized the need to mature CDOs into their own category. Now it has a separate conference series just for CDAOs.

Tom Davenport summarized 8 ways for CDOs can demonstrate value at his keynote speech at the 2023 MIT CDOIQ Symposium. The first item was to add an "A" to the CDO title. I support a recommendation to correct the mislabeling.

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What Are the Key Differences Between CDO, CAO, CDAO, and CAIO?

Chief Analytics Officer (CAO)

CAOs report to other first-level CXOs (marketing, operations, finance, strategy, transformation, or directly up to the CEO). They focus on solving problems for decision-makers by using data, technology, and analytics techniques.

CAOs receive support from CDO and CIO/IT teams. Some organizations have both a CDO and a CAO, so that the CDO supports the CAO with data products and data governance services, and the CAO focuses on delivering measurable impact by solving specific business problems (increasing revenue or margin, reducing cost, improving operational efficiency, and expanding market share).

The CAO team’s key players include:

  • Business data analysts

  • Data scientists

  • Statisticians

  • Operational optimization analysts

  • Analytics product manager

  • Analytics project manager

Depending on the organizational maturity, this team may include more technical staff such as DataOps engineers and Machine Learning Operations (MLOps) engineers.

Predictive and prescriptive analytics models require business and technical documentation, standard procedure, version control and governance, sustainable performance monitoring, and continuous development, improvement, and deployment.

The CAO team provides more internal advisory services for the frontline business leaders. They can be centralized or deeply embedded inside various functional areas (marketing, finance, operations, customer service, and supply chain) with specialized local priorities and dedicated resources so that they do not have to go through the IT project prioritization process.

The more mature an industry or organization is in its analytics capabilities, the less pressure to form a centralized CAO team in a shared-service model.

Some organizations choose to form a center of excellence (CoE) for data science (advanced analytics) so that they can take advantage of the limited resources in high demand and achieve an enterprise-wide impact.

Chief Data and Analytics Officer (CDAO): Combining CDO and CAO

The CDAO role combines two jobs (CDO and CAO) into one. I see a 50-70% chance for a CDAO reporting to a CIO, because of the gravitational pull from an IT organization (number of employees, size of budget, status quo organizational setup).

Most of the CDAO’s time is spent on data management (focus of a CDO) rather than analytics value creation and business impact delivery (focus of a CAO).

From my 20 years of experience working with 8 organizations, I reported to:

  • CEO directly - once

  • Chief Marketing Officer directly or indirectly - once

  • Chief Strategy Officer indirectly - four times

  • CIO directly - twice (including once with a dotted-line reporting to the CEO and access to the board of directors quarterly)

CDAOs can create synergy between data and analytics, but they can face a serious struggle between the two extremes: the gravitational pull by the IT department due to the budget or department size, versus the tremendous pressure from CEO and CFO to deliver a measurable frontline impact.

CDAOs' leadership influence can be compromised due to the less optimal organizational structure and the decades-long cultural tension between IT and Business.

Randy Bean and Allison Sagraves wrote an article recently in Harvard Business Review, with the title – “Why Chief Data and AI Officers Are Set Up to Fail.” This is an excellent explanation.

I see three key factors to drive an enterprise's successful journey in data and analytics maturity:

  1. CEO and Board of Directors' vocal advocacy and visible commitment to the data and analytics/AI transformation at the strategic level.

  2. P&L owners' engagement to hold their own teams and the CDAO team accountable to deliver results.

  3. Alliances among CDAO and Finance and Operations leaders: Establish a baseline, estimate/measure/track ROI (return on investment), and improve operational procedure or behaviors, so the "impactful and actionable insights" can be executed to deliver specific value.

Chief AI Officer (CAIO)

CAIO is the perfect new label for the current CDAOs to gain more strategic visibility, have a better chance to deliver business impact, and move closer to the decision-making circle.

CAIO can also be labeled as CDAIO (Chief Data and AI Officer).

AI is the more polished word to replace (and be an extension of) "analytics." It is more sophisticated, technology-enabled, scalable, deeply rooted in operational process and human behavior, more forward-looking, and potentially leading to fundamental changes in organizations.

In recent months, we have seen evidence of technical and technological acceleration of Generative AI development, primarily driven by the plummeting cost of computing power and deep learning methodology improvements.

Generative AI is expected to have an alarming impact on business operational models, the workforce, productivity, ethics, copyrights, legal and compliance implications, and socioeconomic shifts.

This heightened awareness puts more pressure on Data and Analytics and offers an opportunity for CDAOs to transform themselves into CAIOs.


For current and aspiring CDAOs and CAIOs, I believe the future state for their careers is to get closer to the more mature role model of "Chief Financial Officer (CFO)" for 2 reasons:

  1. A CFO has earned a seat at the table for executive decision-makers

  2. The CFO organization is both centralized (shared services) and embedded in the frontline (at the division level)

CEOs, board members, and Chief HR Officers should consider the differences between CDO, CAO, CDAO, and CAIO so that they can properly label the enterprise leadership role for "data and analytics/AI" to fit their strategic vision.

Bonus section:

Two other job titles below are related but different. They are not in the scope of this article:

“Chief Digital Officer,” sometimes also abbreviated as CDO, should be clarified as “Chief Digital Transformation Officer.” This role largely overlaps with “Chief Strategy Officer” or “Chief Transformation Officer.”

“Chief Data Scientist,” no abbreviation, is a more technical and specialized role, focused on advanced analytics (predictive, prescriptive, and cognitive analytics).

It sometimes overlaps with Chief Analytics Officer, but with less focus on organizational strategy than CAO, and without any coverage on business intelligence (descriptive and diagnostic analytics).

Note: The article was first published on the author’s LinkedIn blog. It has been republished with consent.

About the Author:

"Mr. Ge” Gary Cao advises CEOs and board of directors on analytics and AI strategy and serves as a fractional Chief Data and Analytics Officer (CDAO) or Chief AI Officer (CAIO). With 20 years of experience as a CDAO and serial founder of internal analytics startups, Cao has had a strong track record at 8 companies with revenue between US$40 million and US$120 billion.

Cao’s journey spans industries including healthcare (provider and payor), distribution, retail and ecommerce, financial services, banking, marketing, and credit/insurance risk. He is an expert advisor at the International Institute for Analytics and Rev1 Ventures startup studio and has been a speaker or panelist on various events and podcasts.

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