CDOIQ Newsletter, April 2026 Volume I | | Celebrating the 20th Annual CDOIQ Symposium! | |
We’re excited to share that the CDOIQ Symposium (July 21–23, 2026, Cambridge, MA) is attracting strong global participation, with many senior leaders in data, analytics, and AI already registered. To celebrate the 20th anniversary of the Symposium, we’re happy to offer:
- Special onsite registration discount, email team@cdoiq.org.
- 80% off virtual registration for a limited time
| NEW: Agentic Data Quality Certificate Program | | |
With only a few spots left, this is your chance to be part of the very first offering of the Agentic Data Quality (ADQ) Certificate Program held virtually on April 20th — enroll today to confirm your spot and enjoy 20% off.
👉 Register here: April 2026 ADQ Certificate Program
Can't make April? You may enroll in the second offering on July 20th, available both virtually via Zoom and onsite at Hyatt. Registration details are as follows:
👉 Register here: July 2026 ADQ Certificate Program
| | 20th Annual CDOIQ Symposium Agenda Highlight | | |
Keynote: Innovation Insights From Two Decades of CDOIQ
Prof. Stuart Madnick, MIT Management Sloan School
For two decades, the MIT Chief Data Officer & Information Quality Symposium has been more than an event—it has been a movement shaping the future of data leadership. In this keynote, Prof. Stuart Madnick reflects on the journey of CDOIQ—its pioneering vision, transformative milestones, and enduring impact on global practice. He will share insights into how innovation in data governance, analytics, and organizational strategy has evolved, and how lessons from the past 20 years illuminate the path forward for leaders navigating the data-driven future.
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Panel: The CDO Role 20 Years Later - Why Organizations Need a Chief Data, Analytics, & AI Officer
Moderator: Randy Bean, Data & AI Leadership Exchange
Panelists: Katya Andresen, Cigna
Franziska (Fran) Bell, Ford Motor Company
Chandhu Nair, Lowes
Bhagyesh Phanse, Starbucks
In celebration of the 20th anniversary of the CDOIQ Symposium, this will be the 12th annual CDO panel moderated by Randy Bean on the ongoing evolution of the Chief Data Officer role since its inception in the decade of the 2000’s. While only 12% of leading firms had appointed a CDO by 2012 when the CDOIQ Symposium was still in its formative years, that number had exploded to 85% by 2025. The CDO role has become ubiquitous, but now will the acceleration of AI advancement reinforce this direction or lead in new directions? A panel of leading CDOs will explore this question and discuss where the CDO role evolves next in an AI future.
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From Data Lakes to Agentic Enterprises: Governing Real-Time, Model-Native Data Ecosystems
Stewart Bond, IDC
As AI systems become increasingly agentic, capable of reasoning, acting, and governing data autonomously, the enterprise data landscape is undergoing a profound shift. Foundational models are evolving into the analytical layer itself, blurring the lines between data, application, and intelligence. Traditional warehouses and lake houses are converging into model-lakes, where data preparation, inference, and decision automation are unified.
At the same time, event-driven architectures are redefining the role of data as a live, continuously observable asset. In this new model-native paradigm, governance is no longer about control points and committees, it becomes intent orchestration, built on trust, real-time visibility, and autonomous compliance. See more...
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The Human Algorithm: People, Policy, and Decisions in an AI Era
Barbara Cohn, Xentity Corporation
AI and data are transforming how organizations understand the world, yet the most effective decisions come from the partnership of people and technology. In order to drive high-impact decisions, it is critical that organizations leverage data insights while grounding them in human judgment, interpretation, and organizational context to drive meaningful outcomes. We’ll examine why a practical data strategy and architecture roadmap is essential for enabling responsible AI, advancing decision-intelligence maturity, and ensuring insights are actionable, regardless of where an organization is in its data journey. AI doesn't replace strategy, it raises the stakes. Drawing on public policy and leadership experience, this session shows how organizations can build trust, strengthen collaboration, and turn analytics into real-world decisions that matter.
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Achieving and Measuring Value from Artificial Intelligence
Thomas H. Davenport, Babson College
Achieving value from artificial intelligence initiatives has become not only important to individual companies, but also to the entire economy. Valuations of AI companies are often described as too high, in part because customer companies don't generate enough value from their products to justify the high valuations. In this presentation Tom Davenport will provide an overview of the value issue, describe how it varies by the type of AI technology involved, and make recommendations for AI value management. He will also present the results of a survey on AI economic value and a maturity model for achieving, aggregating, and reporting on AI value.
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The Actual Data, Analytics, and AI Practices of High-Performing Organizations
Doug Laney, Data Strategist & Author
Moises Dueñas Muñoz, EY
This session shares findings from a global study that compares how organizations actually use data, analytics and AI with how well they perform relative to their peers. The research highlights clear patterns that separate leaders from laggards, including how they govern data as an asset, operationalize analytics at scale, and embed AI into decision-making and data management itself. Rather than focusing on hype or isolated success stories, the study provides an evidence-based map of the practices that consistently correlate with higher strategic, operational and financial performance.
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Doubling Down on Data Products to Drive the Business
Amy Lenander and Christina Egea, Capital One
While lots of organizations are building data products, the approach and scope of them varies significantly. This talk will offer a practical guide to building a data products approach where they are at the core of how the business operates. Attendees will hear Capital One’s real-world insights on interoperable data product development, definitions and frameworks, the role of talent and the data product manager, and examples of how data products can support analytical and operational use cases at scale.
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From Zero to Product, Accelerating Data Strategy
Kris Mork, Leidos
A primary responsibility for any Chief Data Officer is delivering on the organization’s data strategy. In this talk I will describe the diamond model of strategy development we used to engage with business leadership, establish strategic priorities, and deliver robust data products. Using this model, we crafted a data strategy and operating model, developed a reference architecture, deployed a modernized platform, and delivered incremental business value inside of 12 months. I will share techniques for building trust and maintaining momentum in a rapidly diversifying business environment. I will conclude with lessons learned integrated internal development teams with external contract resources.
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Bridging the AI Readiness Gap: From Insight to Implementation
Leticia Naqvi, Apple
As artificial intelligence continues to transform how organizations operate, many leaders are still struggling to translate experimentation into measurable value. Despite growing investment, AI initiatives often stall between the pilot and production stages due to misaligned strategies, fragmented data infrastructure, and resistance to change. This session presents a practical framework designed to help organizations bridge the gap between AI readiness and implementation. Drawing from ongoing doctoral research and enterprise experience, the discussion focuses on four core pillars: leadership alignment, data maturity, innovation culture, and change management. Together, these pillars form the foundation for sustainable and scalable AI adoption. Attendees will learn how to assess organizational readiness, identify common barriers, and develop a structured roadmap that embeds AI into business processes. The session provides actionable strategies to help data and analytics leaders responsibly and effectively move from conceptual opportunity to operational impact.
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Anomaly Detection Using Contextual Gen AI Solutions: A CDO’s Guide to Applying Context for Anomaly Detection Using Streaming IoT Data
Asha Poulose and Balaji Uppili, GE Healthcare
This session explores why organizations must embrace contextual knowledge layer to unlock the power of Generative AI to maintain a competitive edge. We will discuss how leveraging the knowledge fabric as a foundational layer can dramatically accelerate return on investment (ROI) by improving data utilization and enabling faster insights. Furthermore, the session will analyze application of Large Language models to real-time machine data, paving the way for innovative products, services, and new revenue streams, ultimately driving return on innovation. Finally, it will outline a change management framework essential for implementing the solutions across business to enable real-time future with data.
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The CDO in the Age of Generative AI: Lead the Asset—or Lose the Mandate
Mark Ramsey, Ramsey International LLC
Generative AI has done what decades of analytics initiatives could not: it has exposed who truly owns—and understands—the most valuable asset in the enterprise. In the age of GenAI, data is no longer a back-office capability; it is the raw material of intelligence itself.
In this session, Mark Ramsey draws on his 40 years in data and analytics, including his experience as Chief Data Officer at Samsung and GlaxoSmithKline, to discuss how Generative AI is a reckoning moment for the CDO role. Organizations are discovering—often painfully—that GenAI cannot succeed without trusted, well-architected, and well-governed data. Yet many data leaders remain positioned as stewards rather than strategic owners. See more...
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People and Data (and, oh yes, AI!)
Tom Redman, Data Quality Solutions
The last several years have witnessed a drumbeat of intoxicating, potentially game-changing AI technologies. But as Jeff McMillan, who leads AI efforts at Morgan Stanley and whose program is as far along as any puts it, “When it comes to AI, technology is easy. Data and people are hard.”
That’s where People and Data, my latest, best, and most important book comes in. Based on engagements with dozens of clients, study groups, and interactions with other experts, it unpacks the essential roles “regular people,” those without data in their titles, play all things data. Yet most data programs ignore them, even viewing them as “the problem.” We’ll step through the logic, call out specific roles for regular people, and discuss implications for data teams and AI. Most importantly, we’ll show how companies have made all this work. We’ll conclude with a short discussion on some longer-term issues. Namely, today’s organizations are unfit for data. While making the structural changes may be beyond the remit of attendees, it is essential that the “top data person” begin to press for such changes.
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Preparing Enterprises for Agentic AI: What CDOs Must Redesign Now
Amit Shivpuja, Walmart
Agentic AI is advancing faster than most enterprises are structurally prepared for. Hence, CDOs face a critical inflection point: redesign foundational elements of data, governance, and literacy, or risk systems that behave unpredictably at scale. I am looking to present a readiness framework that integrates data products, contextual governance, and human‑centered accountability to support emerging agentic capabilities. Participants will learn how to modernize governance without adding friction, operationalize responsible AI across complex organizations, and build adaptive foundations that enable safe, scalable innovation in the next era of enterprise AI.
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Trusted Data Score: Turning Data Governance into Measurable Trust
William Snider, Nationwide Technology
As Nationwide accelerates its use of AI, analytics, and digital experiences, leaders need a clear, actionable view of how much they can trust the data behind critical decisions. This session introduces Trusted Data Score, a unified scoring framework designed to quantify the confidence and usability of our most important data assets.
We will walk through the core traits that power the score — metadata curation, ownership, quality, protection, and (soon) observability — and how they come together to provide a single, transparent measure of data trust across transparent measure of data trust across Nationwide’s strategic data assets. Attendees will see how the score underpins a Trusted Data Dashboard, enabling leaders to quickly identify red/yellow/green areas, focus data investments where they matter most, and promote accountability through visible, measurable outcomes. See more...
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Agentic AI Governance
Sunil Soares, Tavro AI
Autonomous and semi-autonomous AI agents are transforming how businesses operate—taking on tasks, making decisions, and interacting with systems and users with minimal human input. These agentic systems promise major gains in innovation, productivity, and ROI. At the same time, they introduce new layers of complexity in transparency, accountability, ethics, and compliance. As organizations adopt these technologies, the ability to govern them effectively is critical to managing risk and building trust. See more...
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Modernization of Data Management from Traditional On-Prem Solutions to Cloud-Native Solutions
Vinod Surasani, RBC Wealth Management
In today’s data-driven economy, organizations face the dual challenge of managing massive volumes of data while ensuring its quality, consistency, and accessibility. Cloud modernization of data warehousing platforms, coupled with the evolution of Master Data Management solutions, provides a transformative path toward scalable, intelligent, and agile data ecosystems.
A critical component of successful modernization is the adoption of modern MDM solutions, which unify and govern core business entities such as customers, products, and suppliers. Cloud-based MDM platforms enable consistent, high-quality data across systems while supporting global data stewardship, lineage tracking, and compliance with evolving regulations. See more...
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Value = Data + Capabilities: A Field Guide to Product-Led Data Platforms
Amin Venjara, ADP
Internal teams don’t buy platforms—they adopt them to ship outcomes faster with less friction. This talk is a practical field guide to running your data platform as a product. We’ll start with a Value Architecture—Value = Data + Capabilities—and how anchoring every investment to a real use case prevented over-engineering. Then we’ll walk through a four-part measurement framework for data platform success: Maturity (platform readiness), Efficiency (hub vs. spoke spend), Adoption (active users), Satisfaction (NPS). We’ll share how a company-wide DataDay and quarterly release demos created pull from developers and analysts. Finally, as a concrete pattern, we’ll walk through the evolution of our Enterprise Graph capability: from use-case definition and development to value-delivery and scale.
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Pre-Symposium Proceedings Preview | We’re thrilled to share the draft skeleton of the Pre-Symposium Proceedings for the 20th Annual CDOIQ Symposium, giving you an early look at the program and sessions. All registered attendees will receive the final version of the Pre-Symposium Proceedings one week before the symposium. | |
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