Chief Data and AI Officer (CDAIO) Certificate Program

We are in the midst of an unprecedented wave of digital and AI disruption. Every business needs a strong AI, analytics and data strategy to unlock value and to prepare for an AI-augmented future. Strong AI and data leadership is a crucial ingredient for companies to maximize value from innovation, minimize risk, and upskill their talent. The Chief Data and AI Officer Certificate Program at CMU—taught by practicing CDOs and AI thought leaders—provides aspiring leaders with the necessary tools, skills, thought-provoking ideas, and mentorship. Krishna Cheriath | VP, Head of Digital, Data, and Analytics | Zoetis

• Enterprise Data Management (EDM) Foundations

  Instructor: 
  Dr. David Steier | Distinguished Service Professor, Carnegie Mellon University's Heinz College

  This module will set the stage for the program by describing the evolving nature of data and its value to the enterprise and discussing use cases of organizations that have undergone successful data and digital transformations. The module will then outline the core components of an enterprise data management program. This module will also review the necessary components of the data lifecycle management process. Finally, the module will end with a discussion on data quality management.

• Enterprise Data Strategy and Governance

  Instructor: 
  Krishna Cheriath | VP, Head of Digital, Data, and Analytics, Zoetis

  This module will discuss the business case (value realization of data) for enterprise data management, and the foundational importance to digital transformation initiatives, through the development of a mission- aligned, data-driven strategic roadmap. The module will also outline the role of the data executive and their charter to drive strategic decisions for data infrastructure and various technologies, aligning these decisions with the company’s data strategy. Finally, the module will define good data governance, look at the different elements of establishing good data governance, and consider ways to implement an effective data governance agenda.

• Data and Analytics Maturity: From Foundations to Leading- Edge Practices

  Instructor: 
  Doug Laney | Data and Analytics Innovation Fellow, West Monroe

  Organizations are utilizing maturity models to assess their data management gaps and capabilities and compare their progress against industry peers. Becoming data-driven requires a degree of maturity regarding the organization’s data assets, as well as an appreciation of data’s unique properties. Treating data as an actual business asset should go beyond just talking about it as one. Typically, other traditional assets are measured, managed, and monetized with great discipline, using standards, frameworks, specialized roles, and technologies to do so. By aligning the data program with industry best practices, a firm can establish a benchmark from which to develop and grow their program.

• Data Engineering: Infrastructure and Technology

  Instructor: 
  Olga Lagunova | Chief Product and Technology Officer, GoTo

  As companies seek to gain a competitive advantage through data, analytics, and AI, CDAIOs face increased pressure to provide data infrastructure that enables and accelerates data-driven innovation. CDAIOs are chartered to drive strategic decisions for data infrastructure and technology, aligning these decisions with the broad company's data strategy. To stay competitive, companies must understand and adopt modern trends: leveraging cloud, deploying automation, adopting emerging architectures, implementing best practices, and tooling. This module will provide students with an understanding of the critical technology choices that organizations need to make and prepare them to make these decisions.

  
  

• Modern Analytics for Business Intelligence

  Instructor: 
  Craig Brabec | Chief Data and Analytics Officer, Best Buy

  This module focuses on the analytic component of Business Intelligence (BI) in the context of its practical application. Participants will have a chance to gain and solidify knowledge of the most important contemporary methods of analytics (from descriptive to prescriptive), to develop understanding of practical applicability, how to formulate analytic tasks in support of business objectives, and how to define successful projects.

• Data Ops

  Instructor: 
  Chris Stephens | Field CTO and Head of AI Solutions, Appen

  The Data Operations module is a critical component within an organization's data infrastructure, responsible for managing and optimizing the flow, storage, and accessibility of data throughout its lifecycle. This module encompasses a range of functionalities aimed at ensuring data quality, reliability, and efficiency in processing. Data Operations plays a pivotal role in enabling organizations to harness the full potential of their data assets, driving innovation, efficiency, and competitiveness in today's data-driven landscape.

• Data Monetization: The Practice of Infonomics

  Instructor: 
  Doug Laney | Data and Analytics Innovation Fellow, West Monroe

  Organizations have begun looking beyond traditional business income streams, not only due to the pandemic drying them up, but due to the imperative to drive evermore value for their customers, investors and other stakeholders. Moreover, many savvy executives have realized that their company’s greatest asset is not one that’s on the balance sheet, it’s one that is overlooked by current accounting standards: data. The discipline of Infonomics takes you beyond thinking and talking about information as an asset to valuing and treating it as one. This module provides the foundation and methods for quantifying information asset value and tactics for using information as your competitive edge to drive growth. Getting this right is a challenge that chief data officers (CDAIOs) must master for their organizations to survive, let alone thrive in the digital economy. This module exposes leaders to the process of developing and implementing a data monetization strategy and how to apply the unique economics of information.

• Developing Data Products and the Business of Data

  Instructor: 
  Mario Faria | Applied Data and Analytics Advisor
  Krishna Cheriath | VP, Head of Digital, Data, and Analytics, Zoetis

  This module will provide a more in-depth view of the strategy, process, and needed resources for the development of data products to support the growth strategy of the organization. This module provides data leaders with the ability to structure a plan for data product innovation that their workforce can meaningfully accomplish. This module is also focused on the requirement for today’s CDAIOs to develop the business case and budget for data and analytics investments that align with the annual strategic planning process and is supported by key organizational stakeholders.

• Applied Data Science and Machine Learning: Managing Projects

  This module will provide students with an understanding of the latest in data science and machine learning modeling methodologies. Particular attention will be paid to formulating problems to be amenable to data science and machine learning solutions. The goal of the session is to introduce the most common techniques and give a taste of more advanced techniques to help CDAIOs operationalize these practices at scale.

• AI Foundations and Applications (Demystifying AI)

  Instructor: 
  Dr. David Steier | Distinguished Service Professor, Carnegie Mellon University's Heinz College
  

  This module establishes the foundational knowledge necessary for understanding the vast landscape of artificial intelligence by tracing the history and evolution of AI, highlighting pivotal moments and key advancements that have shaped today's AI capabilities. Participants will explore the latest developments in AI, including the rise of generative AI, and discuss the significant impact these technologies are having on various industries.

• AI Strategy (Value Creation) and Governance

  Instructor: 
  Dr. Inderpal Bhandari | Global Chief Data Officer (ret), IBM 
  Krishna Cheriath | VP, Head of Digital, Data, and Analytics Zoetis

  This module delves into the strategic implications of AI on business value creation, exploring the economic transformation driven by AI technologies. Participants will understand how AI reshapes business models by enhancing efficiency and effectiveness across operations. By the end of this module, participants will have a clear understanding of how to develop and implement an AI strategy that complements and enhances their overall business strategy, positioning AI as a key driver of competitive advantage. In addition, participants will understand the importance of building a governance structure that not only manages AI risks but also actively cultivates trust between AI systems and the humans who interact with them.

• Structuring and Operating the CDAIO Organization

  Instructors: 
  JoAnn Stonier | Data and AI Fellow, former Global Chief Data Officer, Mastercard

  This module is an overview of the practical operations of the CDAIO function and will cover how to properly plan, structure, budget, run and obtain organizational “buy-in” for the data and AI office function. The goal of this module is to balance the art and the science of strategic data management and its intersection with business strategy, product development, information technology, and operations. It will address how the CDAIO function needs to balance data innovation with the evolving data and AI risks that need to be managed and how to structure, staff and organize the function.

• Building the AI Factory - Developing Scalable AI Systems

  Instructors: 
  Chris Stephens | Field CTO and Head of AI Solutions, Appen
  Dr. Isaac Farber | AI Factory Director, Army Intelligence Integration Center (AI2C)

  This module bridges the gap between the strategic business functions of the CDAIO and the technical realms managed by the Chief Information Officer (CIO), specific to modern AI-infused IT architecture, essential for supporting advanced analytics and AI capabilities. Participants will emerge from this module with a clear understanding of how to align AI strategies with IT infrastructure, ensuring that their AI initiatives are built on robust, flexible, and scalable architectures.

• Deploying and Monitoring AI Systems (ML/AI Ops)

  Instructors: 
  Dr. Anand Rao | Distinguished Service Professor, Carnegie Mellon University's Heinz College

  This module marks the transition from the conceptual phase of data science to the practicalities of enterprise-wide AI application. It underscores the shift in focus from creating accurate models to deploying AI systems that are robust, secure, and operationally efficient. By the end of this module, participants will have a comprehensive understanding of the challenges and best practices associated with deploying and monitoring AI systems at scale within their organizations.

• Responsible AI Governance – Policy and Implementation

  Instructor: 
  Dr. Anand Rao | Distinguished Service Professor, Carnegie Mellon University's Heinz College

  As the world rapidly embraces Artificial Intelligence, the potential for both benefit and harm escalate. This module navigates the complexities of responsible AI use. Our focus is on providing a detailed and practical understanding of the key risks and harms traditional and generative AI can pose, the principles guiding ethical use of AI, and the intricacies of how these harms manifest themselves in the AI lifecycle. This module places a strong emphasis on bias, fairness, transparency, explainability, safety, security, privacy, and accountability, demystifying these foundational concepts and highlighting their relevance in the end-to-end AI life cycle.

• Generative AI and LLM Application

  Instructor:
  Dr. David Steier | Distinguished Service Professor, Carnegie Mellon University's Heinz College

  This module explores the evolutionary journey from traditional rule-based AI to the cutting-edge realm of Generative AI, offering insights into its development and the plethora of business applications it enables. Upon completing this module, participants will have a thorough understanding of generative AI's trajectory, its potential for enterprise- level application, and the need for responsible deployment to harness its benefits while managing its risks effectively.

• Data and AI Cybersecurity and Risk Management + Data Privacy

  Instructors:
  Omar Khawaja | VP and Field CISO, Databricks
  Lisa Martinelli | VP, Chief Privacy and Data Ethics Officer, Highmark Health

  In today's rapidly evolving technological landscape, Artificial Intelligence plays a pivotal role in reshaping industries, automating processes, and enhancing decision-making capabilities. However, with the increasing reliance on AI, the need for robust security measures and rigorous testing processes has become more critical than ever. The Data and AI Cybersecurity and Risk Management module is designed to equip participants with the essential knowledge and skills to ensure the resilience, reliability, and security of AI systems. Also, collecting and sharing information raises opportunities and creates challenges for organizations. Privacy in the corporate arena has undergone a metamorphosis over the last two decades. To understand the evolution of privacy and ethics as integral corporate imperatives, this module also explores their transformation through the lens of a Chief Privacy and Data Ethics Officer.

• CDAIO Leadership and Communication Best Practices

  Instructors:
  Allison Sagraves | Data Advisor, Coach, Teacher, Former CDO, M&T Bank
  Chris Labash | Associate Professor of Communication and Innovation, Carnegie Mellon University's Heinz College

  This module focuses on improving effectiveness as a manager and leader by introducing frameworks for understanding organizations and organizational processes. Today’s business world requires leaders who have a strong sense of empathy, sharp critical-thinking skills, and a diverse view of society. As such, an effective data and AI leader should be able to lead and inspire, foster teamwork, and create a culture which helps them be better managers. Also, telling a great data-driven story can be useful for both stakeholders and your customers and can drive better decision making within an organization. However, turning data into a great story can be an intimidating task that many organizations struggle to accomplish. This module will also review tactics to help facilitate better communications between internal and external company stakeholders regarding data as an enterprise asset.