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CDOs: AI, Team Collaboration and Data Governance are Critical for Data Success
2025-05-23 23:18:39| The Webmail Blog
CDOs: AI, Team Collaboration and Data Governance are Critical for Data Success caro2698 Fri, 05/23/2025 - 16:18 Cloud Insights CDOs: AI, Team Collaboration and Data Governance are Critical for Data Success May 27, 2025 by Rackspace Technology Staff Link Copied! Recent Posts CDOs: AI, Team Collaboration and Data Governance are Critical for Data Success May 27th, 2025 Summiting the Cloud: How Effective Governance Paves the Way to Success May 20th, 2025 The Private Cloud Resurgence: Why 90% of Enterprises Are Rethinking Their Cloud Strategies May 19th, 2025 Optimising Technology to Transform Patient Care May 13th, 2025 Accelerate Cloud Transformation with a Smart Path to AWS Migration April 30th, 2025 Related Posts Cloud Insights CDOs: AI, Team Collaboration and Data Governance are Critical for Data Success May 27th, 2025 Products Summiting the Cloud: How Effective Governance Paves the Way to Success May 20th, 2025 Cloud Insights The Private Cloud Resurgence: Why 90% of Enterprises Are Rethinking Their Cloud Strategies May 19th, 2025 Cloud Insights Optimising Technology to Transform Patient Care May 13th, 2025 Cloud Insights Accelerate Cloud Transformation with a Smart Path to AWS Migration April 30th, 2025 In 2024, Ben Blanquera, VP of Technology and Sustainability at Rackspace Technology and a member of CDO Magazines Global Editorial Board, interviewed six prominent chief data officers. They shared insights on how data innovations can help drive success. Here is a summary f the key findings. 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 todays 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: The rise of AI and machine learning and its impact at scale The essential need for collaboration across business teams The need for strong data governance to support data quality and consistency Here are highlight summaries from Blanqueras 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 arent 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 arent 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, Its not just about moving data or writing complex queries. Its 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. Tags: AI Data Hybrid Cloud Cloud Insights [ more ]
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