CDOs: AI, Team Collaboration and Data Governance are Critical for Data Success

For the second consecutive year, VP Ben Blanquera talked with several prominent chief data officers from multiple industries around the world. The in-depth conversations shed light on CDOs’ evolving responsibilities in today’s business landscape and explored the transformative impact of data, analytics and AI innovations. The result is the CDO Playbook, a collection of best practices for managing data-centric processes, methods and resources to drive business innovation and transformation.  

In 2023, CDOs underscored the importance of aligning data initiatives with a purpose and a mission, adopting a people-centric approach, fostering data literacy across organizations and emphasizing practicality in data investments to maximize value.

In 2024, Blanquera engaged with six CDOs to delve deep into their strategies, challenges and success stories. From these conversations, three new strategies for achieving success have emerged:

1. The rise of AI and machine learning and its impact at scale
2. The essential need for collaboration across business teams
3. The need for strong data governance to support data quality and consistency

Here are highlight summaries from Blanquera’s interviews, with links to the whole CDO Playbook for each CDO.

1. AI and machine learning are critical

A common theme among all CDOs is the importance of aligning AI solutions with practical business use cases. No single factor drives AI value creation in isolation. To succeed, companies must address all key factors simultaneously and examine how they intersect and influence one another.

Dr. Michael Proksch, author of The Secrets of AI Value Creation said, “The key to unlocking real value with AI lies in identifying use cases that provide a competitive edge, rather than falling back on generic industry solutions. Achieving AI success involves more than just implementing technology. It also requires a careful balance of business analytics, data management and even psychology.”

Neil Bhandar, VP of Analytics at Generic Power Systems, said, “We prioritize data cleanliness and quality from the outset. By integrating AI-driven fuzzy logic and validation against authoritative databases, we ensure that our data is accurate and reliable.”

Luciano Miranda, Vice President of Analytics and Insights for Global Operations and Supply Chain at Medtronic, said, “Our philosophy is straightforward yet potent. Prioritize simplicity, automation and data integrity. By leveraging AI, we are revolutionizing business operations and shaping the future.”

Tracie Cleveland Thomas, Senior Vice President of Digital Transformation at KeyBank, prioritizes bringing generative AI closer with practical use cases for everyday people. “To remain competitive and provide personalized experiences for clients and consumers, integrating some form of generative AI is essential.”   

Krishna Cheriath, Chief Data and Analytics Officer and Head of AI at Zoetis, stated, “If AI tools aren’t embraced, trusted or integrated into workflows effectively, human potential remains unrealized. Therefore, operationalizing AI requires a focus on human adoption and front-loading the user experience.”

Zoetis prioritizes both humans and AI and machine learning to effectively scale solutions. Human operations focus on understanding user needs and ensuring AI solutions align with their tasks and responsibilities. AI and machine learning operations ensure technical proficiency, model maintenance, and robust enterprise capabilities, such as model registries and ROI measurement.

2. Collaboration across business teams boosts productivity

Another recurring theme is the critical role of collaboration across business teams. For example, embedding data engineers within teams, rather than keeping them in siloed functions, establishes a direct connection between technical effort and business objectives. This approach helps ensure the development of relevant solutions and accelerates value delivery across the organization. 

Medtronic champions a similar approach and highlights how roles within the organization have evolved through the consolidation of its analytics groups. Now they align closer to the company's evolving needs and priorities, reflecting its commitment to adaptability and responsiveness.

At Eaton, data engineers aren’t isolated in separate teams. Instead, they are integrated into value streams. Along with managing platforms, each leader collaborates with one or more business teams to support effective performance.

According to Ross Schalmo, Chief Data Officer at Eaton, “It’s not just about moving data or writing complex queries. It’s important for engineers to understand the business processes generating that data and how it will be used. By documenting data flows and understanding the objectives of our reports, we can better align our efforts with business needs and effectively measure the impact of our initiatives.”

3. Implementing strong data governance to support quality and consistency

A recurring theme among the CDOs interviewed was the critical need for a robust data governance strategy. Many experts highlighted its role in establishing security guardrails and ensuring organizational consistency, ultimately leading to improved data quality.

At Eaton, the company established data domains aligned with corporate functions, such as marketing, sales, the supply chain and HR. It set up formal data governance structures within each domain. Guided by a playbook, this approach moved Eaton from no data governance to implementing data quality dashboards and scorecards.

Likewise, Thomas at KeyBank underscored the importance of data governance by prioritizing data quality and security as its foundational components and strategy.

For more exclusive insights from these six CDOs, go to the CDO Playbook here to read the collection of best practices for managing data-centric processes, methods and resources to drive business innovation and transformation.