Part 1 of 2: 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 managing data assets effectively.

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.

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, aiming to bridge 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 Gartner research, three-quarters of CDAOs who do not prioritise company-wide influence and measurable business impact by 2026 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 first part of this series 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. 

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 the technology strategy to support business innovation and growth.
• Infrastructure Management: Oversee the development and maintenance of the IT infrastructure, ensuring 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, focusing on enhancing 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, ensuring 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 the analytics domain, especially data science. If done correctly, this can yield high business value, 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 gets even more complex with the advent of artificial intelligence (AI) and third-party models from vendors like OpenAI, Google and Hugging Face. Given that these models are technological tools, it makes sense for them to fall under the CTO’s purview. The CTO is best positioned to evaluate, integrate, and manage these technologies within the organisation’s infrastructure.

Concluding Remarks
The CDAO plays a crucial role in ensuring that the data used to train and operate these AI models is high-quality, secure, and compliant with relevant regulations. Despite this, their involvement often appears niche and without meaningful ROI. We have examined why CDAOs frequently struggle to deliver tangible business outcomes, prompting whether this role should be merged with other CXO positions. In the next part, we shall examine the possible implications of the CDAO responsibilities merged with the CIO or the CTO. We shall also explore some solutions to CDAOs’ challenges by providing actionable insights and strategies to ensure CDAOs can deliver measurable business value and drive business success.

Article References:

https://www.gartner.com/en/documents/5447263