The Critical Role of CDAOs: Are They Here to Stay?

In today’s data-driven landscape, CDO, CAO, and CDAO are critical in steering organisations towards success. The journey began with the CDO, who introduced a data-centric culture by developing robust data strategies, ensuring data governance, and effectively managing data assets.

As the volume and complexity of data grew, the need for specialised analytics expertise became apparent, leading to the creation of the CAO role. The CAO focused on transforming raw data into actionable insights, leveraging advanced analytics techniques to drive innovation and strategic decision-making. This role brought a new dimension to data leadership, emphasising the importance of analytics in achieving business objectives.

Originally published at LinkedIn Pulse on 11 March 2025.

The evolution continued with the CDAO, a role that combined the strengths of both the CDO and CAO. Chris Mazzei, the first known CDAO at EY in 2014, embodied this new era of data leadership, integrating data management and analytics to deliver comprehensive business value. This marked a significant step in the evolution of data leadership roles, aimed at bridging the gap between data management and analytics.

Despite the promise of the CDAO role, many organisations have struggled to realise its full potential. According to MIT Sloan, the average tenure of a CDO is 30 months. Gartner research has predicted that by 2026, three-quarters of CDAOs that do not prioritise company-wide influence and measurable business impact risk being subsumed by IT functions. The inherent challenges in these roles often set them up for failure unless clear boundaries and buy-ins are established with other key C-suite roles, particularly the CIO and the CTO.

This article explores the evolution of these roles, their critical responsibilities, and the challenges they face in delivering measurable business value. It delves into the root causes that hinder organisations from fully realising the value of their data assets. This article also examines the potential to integrate these roles into the CIO and CTO positions. Finally, it provides strategies for optimising CDAOs’ impact on organisational success.

Understanding the Different CXO Roles

The role of the CIO emerged in the 1980s as organisations recognised the importance of managing their information systems and technology infrastructure effectively. The CIO’s primary responsibility was to oversee internal IT operations to ensure that technology systems supported business processes and goals.

The CTO role became more prominent in the 1990s and 2000s with the rise of the Internet and the increasing importance of technology in driving business innovation and product development. The CTO focuses more on external technology strategy, product development, and leveraging emerging technologies to create competitive advantages.

The CDO role formally emerged at the turn of the 21st century, initially focusing on data governance and compliance with regulatory mandates such as the Sarbanes-Oxley Act. This was when organisations began to recognise the importance of managing their data assets to ensure compliance and mitigate risks.

The role of the CAO emerged as organisations recognised the growing importance of data analysis in strategic decision-making. Initially, the focus was on leveraging data to drive business insights and operational efficiencies. Over time, the CAO’s responsibilities expanded to include advanced analytics, predictive modelling, and data-driven innovation. The position gained prominence alongside the rise of big data and advanced analytics technologies, distinguishing itself from the CDO by emphasising the application of data insights rather than data management. Today, CAOs play a crucial role in shaping data strategy and fostering a culture of innovation and analytics within organisations.

 However, these two roles are often merged into CDAOs in many organisations. CDAOs are expected to drive data-driven insights to fuel business outcomes and digital transformation initiatives. Through the merging, CDAOs are expected to handle data governance and analytics responsibilities, ensuring that data is managed and analysed effectively to generate actionable insights.

Let’s examine these roles in detail.

Chief Information Officer (CIO)

Primary Focus: IT Operations and Alignment with Business Strategy

Key Responsibilities:

• IT Strategy: Align IT strategy with business goals, ensuring IT investments support the overall business strategy.

• IT Operations: Manage day-to-day IT operations, including system maintenance, network management, and IT service delivery.

• Vendor Management: Oversee relationships with IT vendors and service providers.

• Digital Transformation: Lead initiatives to drive business value through technology investments.

Chief Technology Officer (CTO)

Primary Focus: Technology Innovation and Infrastructure

Key Responsibilities:

• Technology Strategy: Develop and implement technology to support business innovation and growth.

• Infrastructure Management: Oversee the development and maintenance of the IT infrastructure to ensure it supports current and future business needs.

• Emerging Technologies: Identify and evaluate emerging technologies that provide a competitive advantage.

• Product Development: Lead the development of new technology products and services, with a focus on enhancing the customer experience.

Chief Data Officer (CDO)

Primary Focus: Data Strategy, Management, and Governance

Key Responsibilities:

• Data Strategy: Develop and implement the organisation’s data strategy to ensure alignment with business objectives.

• Data Governance: Establish frameworks to ensure data quality, security, and compliance.

• Data Management: Oversee the management of data assets, including data architecture, data integration, and data warehousing.

• Data Quality: Ensure accuracy, consistency, and reliability across the organisation.

Chief Analytics Officer (CAO)

Primary Focus: Data Analytics and Insights

Key Responsibilities:

• Analytics Strategy: Develop and implement the organisation’s analytics strategy to drive business insights and innovation.

• Advanced Analytics: Lead the application of advanced analytics techniques, such as machine learning and predictive modelling.

• Business Insights: Generate actionable insights from data to support strategic decision-making.

• Analytics Tools and Technologies: Oversee the selection and implementation of analytics tools and technologies to enhance data analysis capabilities.

Chief Data and Analytics Officer (CDAO)

Primary Focus: Maximising Business Value from Data and Analytics (Data Strategy, Management, Governance, Analytics, and Insights)

Key Responsibilities:

• Data and Analytics Strategy: Develop and implement a comprehensive strategy that aligns data and analytics initiatives with business goals.

• Data Governance and Quality: Establish robust frameworks to ensure data integrity, security, and compliance.

• Advanced Analytics: Lead the deployment of advanced analytics techniques, including machine learning and predictive modelling, to drive innovation.

• Business Insights: Extract actionable insights from data to inform strategic decision-making and enhance competitive advantage.

• Data Management: Oversee the architecture, integration, and warehousing of data assets to ensure their optimal use.

• Analytics Tools and Technologies: Select and implement cutting-edge tools and technologies to enhance the organisation’s data analysis capabilities.

• Data Lifecycle Management: Oversee the data management throughout its lifecycle, from acquisition to disposal.

The CDAO’s Dilemma

The CDAO is pivotal in transforming data into a strategic asset. Tasked with maximising business value, the CDAO oversees data governance, quality, analytics, and security. By leveraging data assets, the CDAO drives innovation and secures a competitive edge.

In the past decade, the importance of this role has surged as organisations aim to unlock the potential of big data and advanced analytics. Despite these efforts, many have struggled to capitalise fully on their data investments. Our deep dive into this issue uncovers the underlying challenges and offers actionable insights.

Lack of Ownership

A significant challenge for CDAOs is that data typically resides within business units managed by CIOs, while data platforms and infrastructure fall under the CTO’s domain. This division can render the CDAO’s strategies and policies theoretical, lacking the necessary enforcement and support from the CIO and the CTO.

For instance, a CDAO might develop a comprehensive data governance framework to improve data quality and compliance. However, without the CIO’s commitment to implement these policies across business units or the CTO’s support to integrate them into the data platform, these efforts can be easily disregarded, undermining the CDAO’s ability to deliver tangible business outcomes.

Lack of Leadership in Analytics

Many CDOs who have transitioned to CDAOs often lack a comprehensive understanding of analytics, especially data science. This can yield high business value if done correctly, as they focus primarily on platforms and tools. Analytics requires a thorough understanding of business intricacies.

This gap in knowledge and appreciation for analytics impedes their ability to build strong, trusted relationships with business units and to lead their data science teams effectively in identifying and pursuing high-impact business use cases. It also hinders their ability to demonstrate the return on investment (ROI) for data and analytics initiatives.

As a result, most businesses use cases driven by business units as ad-hoc analytics or data science projects, with few fully operationalised. In many organisations, data scientists still manually run their analytics notebooks to derive insights for their departments. Instead of exploring new opportunities, these data scientists focus only on familiar problems such as customer churn prediction or personalised marketing analytics.

Failure to Establish Trust on Data … – It Is Not the Data Quality but the Data Team!

While data quality is undeniably important, it is not the primary issue hindering the extraction of business value from data. The core problem lies in the lack of trust in the data team, which is often misinterpreted as a lack of trust in the data itself. This distinction is crucial for several reasons.

Firstly, the persistence of shadow analytics within organisations highlights this issue. If data quality were the main problem, businesses would not continue to conduct their data analyses. Their actions indicate a profound mistrust in the data team’s ability to provide reliable insights, thus suggesting that business units believe they can derive more accurate and actionable insights independently, bypassing the data team.

Secondly, the perception of the data team’s competence is crucial. While the CDAO is responsible for defensive (data governance, quality, and compliance) and offensive (analytics and innovation) data strategies, an overemphasis on defensive measures can leave the business feeling unsupported in driving value from data. If the business perceives the data team as lacking the necessary skills or understanding of business needs, it undermines the team’s credibility. Building trust in the data team is essential for fostering collaboration and ensuring that data-driven initiatives are embraced across the organisation.

Moreover, high-quality data is useless if the business does not trust the team responsible for managing and analysing it. This lack of trust leads to the underutilisation of data assets and missed opportunities for innovation and competitive advantage. Building confidence in the data team involves demonstrating expertise, reliability, and alignment with business objectives.

Lastly, the issue encompasses more than just technical data quality; it also involves cultural and organisational factors. Effective data utilisation requires a culture of trust, transparency, and collaboration between the data team and business units. Addressing these cultural barriers is critical for maximising the value derived from data.

The Emerging Role of AI and Technology Ownership

The question of ownership becomes even more complex with the advent of artificial intelligence (AI) and third-party models from vendors such as OpenAI, Google, and Hugging Face. Given that these models are technological tools, it makes sense that they fall under the CTO’s purview. The CTO is best positioned to evaluate, integrate, and manage these technologies within the organisation’s infrastructure.

Can the CDAO Responsibilities Be Distributed Between the CIO and the CTO?

Given the overlapping responsibilities of the CIO and the CTO, a pertinent question arises: Can the CDAO responsibilities be distributed between them?

The Case for CIOs Taking on CDAO Responsibilities

In some organisations, the CIO has successfully absorbed many responsibilities typically assigned to a CDAO, especially those originating from a CAO. This approach can streamline decision-making processes and reduce conflicts between data and IT strategies. This model can work well in organisations where the CIO has a background in data analytics.

However, there are significant challenges to this approach:

1. Specialisation and Focus: The CAO role focused on data monetisation and analytics. Combining this with the CIO’s responsibilities, which include managing IT infrastructure and operations, can dilute the focus on data initiatives. The CAO is instrumental in driving information and analytics strategy, which may be compromised if merged with the CIO’s broader IT responsibilities.

2. Conflict of Interest: The CIO’s primary focus is often on maintaining and optimising IT systems, which can sometimes conflict with the CDAO’s goals of data monetisation through analytics and innovation. This conflict can hinder the effective implementation of data strategies.

However, appointing a Head for Data Science to support the CIO is crucial to ensure the success of this model. This approach is effective for small and large enterprises with multiple divisions led by Divisional CIOs.

The Case for CTOs Taking on CDAO Responsibilities

Similarly, delegating certain CDAO responsibilities to the CTO has been considered in some organisations, especially those that originate from a CDO. This strategy can be effective when the CTO possesses a robust understanding of data management and governance.

However, there are similar challenges to this approach:

1. Specialisation and Focus: The CDO is focused on data security, governance, and management. Combining this with the CTO’s responsibilities, like technology innovation and infrastructure, can dilute the focus on data initiatives. The CDO is pivotal in driving data management and governance strategy, which may be compromised if merged with the CTO’s broader technology responsibilities.

2. Conflict of Interest: The CTO’s primary focus is on technology innovation and infrastructure, which can sometimes conflict with the CDAO’s data democratisation and innovation goals. This conflict can hinder the effective implementation of data strategies.

3. Regulatory and Compliance Requirements: The CDAO’s role in ensuring data compliance and governance is critical in highly regulated industries. Merging this role with the CTOs could lead to compliance gaps and increased risk.

Strategy for the CDAO to Collaborate with the CTO and the CIO

In the modern enterprise, the roles of the CDAO, CTO, and CIO are crucial for driving digital transformation and leveraging technology to achieve business goals. Clear delineation of responsibilities among these roles is essential to avoid overlaps and ensure effective collaboration. The CDO focuses on data governance and strategy, while the CIO manages IT and data infrastructure in most successful organisations. This separation allows each role to specialise and excel in their respective areas, driving better overall outcomes for the organisation.

To succeed, the CDAO must establish clear boundaries, secure buy-in from the CIO and the CTO, and foster effective collaboration and communication. This involves:

1. Establish Clear Governance Structures:

• Data Governance Council: Create a council that includes the CDAO, CIO, and CTO to ensure alignment and collaboration on data initiatives.

• Joint Accountability: Define joint accountability for data governance, quality, and compliance, ensuring support from the CIO and the CTO.

2. Foster Executive Buy-In:

• Executive Sponsorship: Secure sponsorship from the CEO or senior leaders to emphasise the importance of data initiatives.

• Regular Executive Meetings: Meet with the CIO and the CTO to discuss data strategy, progress, and challenges.

3. Collaborative Strategy Development:

• Engage CIO and CTO: Involve the CIO and the CTO in developing the data strategies to ensure alignment and shared ownership.

• Unified Vision: Create a unified vision for data management and utilisation across the organisation.

4. Align Data Strategy with Business Objectives:

• Integrated Planning: Integrate the data strategy into the overall business strategy to ensure alignment with business goals.

• Shared KPIs: Develop shared key performance indicators (KPIs) to measure the success of data initiatives.

5. Define Roles and Responsibilities:

• Clear Definitions: Clearly outline the roles and responsibilities of the CDAO, CIO, and CTO to prevent overlaps and conflicts.

• Specific Roles: For example, the CDO focuses on data strategy and governance, the CIO handles data operations within business units, and the CTO manages the technical infrastructure.

6. Enhance Communication and Collaboration:

• Cross-Functional Teams: Establish teams that include members from the CDAO, CIO, and CTO departments to collaborate on data projects.

• Communication Channels: Create formal channels for regular updates and information sharing between the CDAO, CIO, and CTO.

• Regular Engagement: Meet with business unit leaders to understand their needs and opportunities for data-driven solutions.

• Joint Strategy Sessions: Regular meetings to align strategic initiatives and integrate data, technology, and IT strategies.

• Shared Dashboards: Use shared dashboards and reporting tools to provide visibility into key metrics and progress on joint initiatives.

7. Invest in Training and Development:

• Skill Enhancement: Offer ongoing training programs to strengthen the data team's technical and business skills.

• Cross-Functional Training: Facilitate training sessions to help the data team better understand business processes and objectives.

• Targeted Training for CDAOs: Invest in programs to deepen their understanding of analytics and data science.

• Mentorship Programs: Establish programs in which seasoned data scientists guide less-experienced team members, including the CDAO.

8. Leverage Technology Solutions:

• Unified Data Platform: Invest in a platform that integrates data from various business units and supports the CDAO’s governance framework.

• Automation Tools: Utilise tools to enforce data governance policies and streamline analytics workflows.

9. Demonstrate Value through Pilot Projects:

• Pilot Initiatives: Launch projects that demonstrate the value of the CDAO’s data governance framework.

• Case Studies: Develop case studies from successful projects to build a compelling case for broader implementation.

10. Focus on High-Impact Business Use Cases:

• Identify Key Opportunities: Work with business units to identify high-impact use cases that align with strategic goals.

• Prioritise Projects: Prioritise analytics projects based on their potential business value and feasibility.

11. Operationalise Analytics Initiatives:

• Develop Scalable Solutions: Move beyond ad hoc projects by building scalable analytics solutions.

• Automate Workflows: Implement automation tools to reduce reliance on manual processes.

12. Demonstrate ROI:

• Clear Metrics: Establish metrics to measure the success and ROI of data and analytics initiatives.

• Communicate Successes: Regularly communicate the impact of analytics projects to stakeholders.

13. Encourage Exploration of New Data Opportunities:

• Innovation Labs: Create labs or centres of excellence for experimenting with new data sources and techniques.

• Cross-Functional Teams: Form teams to explore and develop new data opportunities, fostering a culture of innovation.

14. Implement Robust Data Governance:

• Clear Policies: Establish policies to ensure data quality, security, and compliance.

• Accountability: Assign accountability for data governance to specific roles within the organisation.

15. Leverage External Expertise:

• Consultants and Partners: Engage external experts to validate strategies and provide additional expertise.

• Benchmarking: Use industry benchmarks to measure performance and identify areas for improvement.

By following this comprehensive strategy, organisations can effectively address the challenges faced by CDAOs, ensuring that data initiatives are well-supported, aligned with business goals, and deliver tangible business outcomes.

Concluding Remarks

The optimal separation of roles for the CDAO, CTO, and CIO involves delineating responsibilities, allowing each role to focus on its core areas of expertise. This structure prevents overlaps and fosters collaboration, ensuring that data, technology, and IT strategies are aligned to drive business success. While the CIO or the CTO can assume CDAO responsibilities, the specialisation and focus required for effective data management, governance, and aligning analytics initiatives with the business often necessitate a dedicated CDAO.

These roles can collectively contribute to the organisation’s digital transformation and innovation efforts by maintaining open communication and working together on strategic initiatives. Ultimately, the decision should be based on the organisation’s needs, maturity, and strategic goals.