CDOIQ Academy
STANDALONE UNITS, UNIFIED FRAMEWORK
Each course in the CDOIQ Academy is a complete, standalone learning experience. Together, they form the most comprehensive CDO leadership curriculum available — connecting strategy, governance, quality, analytics, AI, value, and executive leadership into a single, coherent framework.
A Curriculum Built for the Modern CDO
The CDOIQ Academy curriculum spans the full scope of the Chief Data Officer role — from foundational governance and data quality to advanced analytics, AI maturity, and executive leadership. Required courses establish the core competencies every CDO needs; elective courses allow participants to deepen expertise in the areas most relevant to their organization.
Course Units
Overview
Journey to CDOIQ is an executive-focused course designed to translate the principles of Chief Data Officer leadership into practical action. The course moves beyond data strategy frameworks to examine how CDOs establish credibility, mobilize organizations, govern data and AI, deliver measurable business value, and lead enterprise-wide transformation. Participants will explore real-world CDO challenges and decision points, including aligning data strategy with organizational priorities, building effective data organizations, influencing executive stakeholders, managing data governance, accelerating analytics and AI adoption, and demonstrating business outcomes.
Overview
Data strategy forms the foundation upon which effective data management rests. In this module, participants delve into the intricacies of aligning data initiatives with organizational goals, identifying opportunities for data-driven transformation, and formulating a clear roadmap to harness data's potential.
Overview
This course includes 5 sessions. Sessions 1-2, taught by John Talburt, cover the history and development of DQM methodologies, the ISO 8000 DQM reference model, the role of data governance, and a data quality (DQ) case study. Sessions 3-5, taught by Danette McGilvray, cover the methodology Ten Steps to Quality Data and Trusted Information™ (outlined in the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ 2nd Ed. (Elsevier/Academic Press) by Danette McGilvray. This methodology provides a practical approach to creating, improving, and sustaining the quality of data critical to any organization’s success.
Overview
Agentic Data Quality (ADQ) equips leaders to design and operationalize AI-driven, autonomous data quality systems that continuously monitor, diagnose, and remediate data issues across enterprise environments. It enables CCDO participants to embed ADQ within the AI orchestration stack—aligning data, models, agents, workflows, and governance—to deliver trusted, scalable, and decision-ready data.
Overview
This course based on the principles of infonomics focuses on creating various forms of economic value from data, and how to measure data’s worth. It covers practical approaches to identifying, designing, prioritizing, and developing internal and externally focused commercial data product opportunities. It also examines the rationale for valuing data, and multiple financial and non-financial valuation methods. Through examples, interactive AI-based exercises, and discussion participants learn to connect data use, data products, governance, quality, investment, and business outcomes, creating a stronger strategic basis for measuring and realizing their data’s economic potential.
Overview
This course addresses the principles and practices of managing critical shared data entities across the enterprise. Topics include entity resolution, reference data management, MDM architectures, and the governance structures needed to maintain consistent, trusted master data across systems and business units.
Overview
The course will help executives connect data decisions to business value, strengthen data-driven decision-making, and build data literacy and leadership across the organization.
Overview
This course helps leaders assess and advance their organization’s data, analytics, and AI capabilities. Participants examine five levels of maturity across eight dimensions, including strategy, leadership, metrics, skills, architecture, governance, deployment, and technology. Through self-assessment and discussion, the course helps leaders identify organizational strengths, capability gaps, common challenges, and practical improvement opportunities. Participants leave with a clearer view of their current state, target maturity, priorities, and new ideas to improve business value from data, analytics, and AI.
Overview
We cover four key steps — clarifying business value opportunities, assessing capability maturity gaps, developing a transformation strategy with an 18-month roadmap, and fostering organizational adoption and a data-driven culture — illustrated by a longitudinal case study. The course also covers the Enterprise Knowledge Governance Framework and Data Liquidity principles, strategies to address the 'Tragedy of the Data Commons,' and two practicum sessions applying the Vistage Issue Processing Hourglass method to resolve participants' real organizational challenges.
Overview
Most corporate decisions still rest on the small slice of data that already fits a table. Largely ignored, Textual Data is the majority of the corporation. Early attempts to utilize that data have misfired. AI does not remove the need to integrate, contextualize, and warehouse text; it raises the cost of skipping those steps. Search, embeddings, and LLMs are first-generation tools unless they sit on taxonomies, textual ETL, and a textual warehouse.
Course 201
The Technical CDO: Enterprise Data Architecture, Integration and Agentic AI
Overview
This course equips Chief Data Officers and data leaders with the technical knowledge needed to navigate modern enterprise data environments. Topics include enterprise data architecture, databases, data lakes and warehouses, data dictionaries, vendor lock-in, application technical debt, data silos, and information retrieval. Participants will explore practical strategies for integrating incompatible data, using LLMs and traditional machine learning, applying text-to-SQL, and leveraging agentic AI. The course emphasizes scalable architectures, effective data integration, and the evolving technical responsibilities of the CDO.
Overview
Quantum computing is moving from research labs toward practical business applications, with potential to address complex problems in weather forecasting, cybersecurity, supply chains, drug discovery, healthcare, finance, and optimization. As quantum hardware and software advance toward quantum advantage, organizations are increasing investments to drive innovation and prepare for future opportunities. This course provides a practical introduction to how quantum computing can work alongside classical Artificial Intelligence (AI) and Machine Learning (ML) for large-scale and near-real-time applications. Participants will explore quantum computing fundamentals, supervised and unsupervised quantum machine learning algorithms, industry use cases, hybrid quantum-classical solutions, and emerging trends in quantum AI acceleration.
Ready to Begin?
The CDOIQ Academy is open to senior data professionals, CDOs, and executives leading data transformation. Schedule a briefing below.